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Local equity market participation and stock liquidity
Accepted Manuscript Title: Local Equity Market Participation and Stock Liquidity Author: Lei Zhang PII: DOI: Reference:
S1062-9769(16)00019-3 http://dx.doi.org/doi:10.1016/j.qref.2016.02.005 QUAECO 908
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Received date: Revised date: Accepted date:
2-9-2014 23-11-2015 1-2-2016
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Please cite this article as: Zhang, L.,Local Equity Market Participation and Stock Liquidity, Quarterly Review of Economics and Finance (2016), http://dx.doi.org/10.1016/j.qref.2016.02.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Highlights
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We study the relationship between local retail participation and stock liquidity. We use county‐level racial composition to proxy for local retail participation. Stocks in counties with a higher white percentage are more liquid. This effect is stronger among stocks with a high retail concentration. Noise trading increases liquidity under endogenous informed trading.
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Local Equity Market Participation and Stock Liquidity
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Nanyang Business School
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Lei Zhang *
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Abstract: This paper investigates the relationship between local equity market participation and stock liquidity. We use county-level racial composition as a proxy for local retail participation. We find that stocks headquartered in counties with a higher white percentage are more liquid. This effect is stronger among stocks with a high retail concentration (i.e., small size, low institutional ownership, low price). Our findings support Admati and Pfleiderer (1988) that noise trading increases liquidity under endogenous informed trading, i.e., the impact on liquidity is stronger when there are more institutional investors located nearby, especially the ones with “small” investment styles and “transient” trading styles.
Keywords: stock liquidity, retail investors, local bias, noise trading
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JEL Classification: G10, G11, G14, G34
Please address all correspondence to Lei Zhang, Division of Banking and Finance, Nanyang Technological University, 639798, Singapore. Email: [email protected]. I am grateful to Joel Hasbrouck for providing data on stock liquidity measures and Brian Bushee for the classifications of institutional investors. I would like to thank Stephen Dimmock, David Hirshleifer and Rajesh Aggrawal for their helpful comments and suggestions. All errors and omissions are my own.
Introduction Liquidity plays an increasingly important role in empirical asset pricing, market microstructure and
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corporate finance. This paper examines the relationship between local equity market participation and stock liquidity to understand how the trading of local retail investors affects stock liquidity. Two strands of literature have made this study highly worthwhile. First, recent studies show that the tendency to invest in
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local stocks (“local bias”) is even more pronounced for individual investors than for institutional investors. Grinblatt and Keloharju (2000) use a Finnish dataset to find that individual investors tend to hold the
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stocks of companies with nearby headquarters. Using data from a large U.S. discount brokerage, Ivković
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and Weisbenner (2005) find that U.S. retail investors exhibit a strong preference for local investments. They document that the average share of local investments (defined as investments in companies
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headquartered within 250 miles from the investor) is around 30%, which is nearly 20% higher than the average percentage for all firms headquartered within 250 miles from the household. Similar to ownership,
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Loughran and Schultz (2004) provide evidence that retail trading is concentrated in local stocks as well. They find that the trading volume of West Coast companies is lower than that of East Coast firms in the
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bed or on their way to work at that time.
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morning. A direct explanation is that investors in the West Coast, who trade West Coast stocks, are still in
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Though the local bias of individual investors is ubiquitously documented, the origin of this bias is in debate. One argument is that local retail investors have superior information on local firms. Ivković and Weisbenner (2005) show that local investments of individual investors outperform their non-local investments, suggesting that local individual investors possess and exploit their information advantage. However, with the same data, by accounting for the contemporaneous correlation in the cross-section of stock returns, Seasholes and Zhu (2009) find that portfolios of local stocks do not significantly outperform portfolios of remote stocks. In fact, after adjusting for the local benchmark, local investments of individuals’ portfolio even underperform the non-local investments. Their results bring into question the information superiority hypothesis and support the familiarity hypothesis, which argues that investors over-weight local stocks not because they are better informed but because they are familiar with them 1
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(Huberman (2001)). Grinblatt and Keloharju (2001a) also provide support to the familiarity hypothesis by showing that Finnish investors are more likely to trade stocks of firms that share the investors’ same language and cultural background.
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Second, suppose that local retail investors trade because of familiarity instead of information-based reasons; in the market-microstructure literature, such trading is usually referred as “noise trading” or
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“liquidity trading”. In fact, noise trading was proposed as a solution to the “no-trade” results of Grossman
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and Stiglitz (1980) and the information acquisition paradox of Grossman (1976). Noise trading keeps the price from fully revealing and makes the rational expectations equilibrium compatible with costly
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endogenous information acquisition.
Although noise trading constitutes a fundamental component of many microstructure models, the
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theories offer different predictions on how noise trading may affect stock liquidity. In Glosten and Milgrom’s (1985) model, an increased proportion of noise trading decreases the market maker’s adverse
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selection costs and thus increases stock liquidity. However, the amount of informed trading could be
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endogenously determined. Kyle’s (1985) model suggests that the effects of increased informed trading
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exactly offset those of increased noise trading and that liquidity is invariant to changes in the level of noise trading. In Admati and Pfleiderer (1988)’s model, even with endogenous informed trading, an equilibrium exists in which noise trading increases stock liquidity. Under their framework, increased noise trading increases the profits of informed traders while at the same time attracting more informed traders with the same information. Therefore, each informed trader must trade more aggressively to exploit the short-lived information and, as a result, compete away their information advantage. In equilibrium, the market maker faces a smaller adverse selection problem when noise trading increases. Surprisingly, little empirical evidence exists to clearly identify the impact of noise trading on liquidity. One approach is to look for unexpected large shifts in noise trading. Greene and Smart (1999) use the “Investment Dartboard” column in The Wall Street Journal as a natural experiment to evaluate the effect of 2
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the change of noise trading on the change of liquidity. They find that the bid-ask spread decreases and the trading volume increases after analysts recommend stocks in this column. Their evidence is consistent with the predictions of Glosten and Milgrom (1985) and Admati and Pfleiderer (1988).
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In this paper, we aim to analyze how the (noise) trading of local retail investors affects stock liquidity. Our approach is to identify an exogenous measure of the supply of local retail trading, which is less likely
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to be driven by firm-specific characteristics that may affect firms’ demand for liquidity. We start from the
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existing literature that documents a striking racial difference in risky asset holdings (Blau and Graham (1990), Choudhury (2002), Hong, Kubik and Stein (2004)). The difference holds after controlling for racial
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differences in income and other demographic factors (Blau and Graham (1990)). Choudhury (2002) uses the Health and Retirement Study (HRS) 1992 survey data to show that at every income and education level,
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the percentage of minority households who own risky assets is much smaller than that of white households. Hong, Kubik and Stein (2004) show that, on average, the stock-market participation rate for white
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households is 34.3%, compared to 9.2% for minority households. Even at the highest quintile of wealth
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distribution, the stock-market participation rate of white households remains much higher than that of
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minority households (57.5% vs. 35.6%)1.
We then provide evidence that there are large cross-sectional variations in the white percentage among U.S. counties. On average, the proportion of white population is 81%, and it ranges from 25% to 99% with a standard deviation of 12%. It is higher in northern states and lower in southern states. However, this is not merely a state phenomenon. For example, for counties in the state of California, the white percentage ranges from 56% to 98% with a mean of 76%. For counties in the state of New York, the white percentage ranges from 52% to 98% with a mean of 78%. Given that white households are more likely to 1
Many factors might contribute to the low market participation rate of minority households. Brimmer (1998) shows that African American households are more risk-averse than white households. African American households who do have a margin of funds to invest typically prefer safer assets, such as checking accounts or real estate, when compared with white households. Another possibility is that of cultural or intergenerational reasons. African Americans are more willing to invest in real estate and certificates of deposit because those industries have marketed their services to African Americans and have agents who are themselves African American. Mabry (1999) points out that African Americans shy away from stocks partly because of their mistrust of Wall Street.
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participate in the stock market than minority households and that local investors tend to invest and trade in local stocks for non information-based reasons, it is reasonable to use the county-level white percentage as a proxy for the level of noise trading by local retail investors. The cross-sectional variations in the local
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white percentage allow us to study the relationship between the local retail participation and stock liquidity and to understand the impact of noise trading on liquidity.
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If the local white percentage is indeed a valid proxy for local retail trading, given the proximity
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ownership or trading behavior of retail investors, it should be positively related to the fraction of trading volume associated with retail investors. Because the number of trades by institutions and individuals is not
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publicly available, we follow Shu (2006) and Hvidkjaer (2008) to infer the fraction of the institutional trading volume and subtract it from 1 to obtain the fraction of retail trading volume. We document a strong
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positive relationship between the local white percentage and the fraction of retail trading volume. The effect is especially strong for stocks with a high retail concentration (small size, low institutional
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streams.
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ownership, and low price) and for stocks headquartered in counties with more volatile household income
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Next, we run our main regressions of stock liquidity on the local white percentage. We find that stocks headquartered in counties with a higher white percentage are more liquid. This result is robust to the inclusion of state, industry and credit rating fixed effects, controls for firm-specific characteristics, controls for other local demographic characteristics and alternative measures of liquidity. The result is not only statistically significant but also economically relevant. A one standard deviation (11.9%) increase in the local white percentage increases the liquidity of a stock by 10.4%. This result survives our robustness checks and holds for sub-samples separated by county population, sample period and firm age. It also holds for the sample of firms that never move headquarters during the sample period. The result remains strongly significant in between-effects regressions and firm fixed-effects regressions. More importantly, we perform a series of sub-sample comparisons. The impact of local white 4
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percentage on stock liquidity is stronger for stocks with a potentially high retail concentration (i.e., small size, low institutional ownership, low price, value, high volatility and scarce analyst coverage). It is especially stronger for firms located in the “only-game-in-town” counties and in counties with more
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volatile household income streams. The differences in coefficients between sub-samples are always significant. We also analyze the change of stock liquidity and the change in the local white percentage for a
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small group of firms that moved their headquarters. Consistently we document an increase in stock
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liquidity when they move from a county with a low white percentage to another county with a high white percentage.
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The result for sub-sample comparisons strengthens our belief that the impact of the local concentration of white people on stock liquidity indeed occurs through the channel of retail trading by
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local investors. The findings are consistent with market microstructure models that predict a positive relationship between noise trading and stock liquidity (Glosten and Milgrom (1985) and Admati and
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Pfleiderer (1988)). The key difference between the two models is that in Admati and Pfleiderer’s (1988)
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model, the informed trading is endogenously determined. Given that endogenous informed trading seems
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to be a rather reasonable assumption, we expect the effect of local retail participation on liquidity to be dependent on the level of competition among informed traders. It is generally accepted that institutional investors possess an information advantage over locally headquartered stocks (Coval and Moskowitz (1999, 2001)). Institutional investors that tilt their portfolios toward small stocks and have short investment horizons are more active to collect information and pursue information-based trading (Coval and Moskowitz (1999), Baik, Kang and Kim (2009), Yan and Zhang (2009)). We therefore perform sample separation by the number of local institutional investors and distinguish institutional investors by their investment style (small/large) and trading style (transient/non-transient). The results strongly support Admati and Pfleiderer (1988) in that the impact of local retail participation on stock liquidity is stronger when there are more institutional investors located 5
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close-by, especially more local institutional investors with “small” investment styles and “transient” trading styles. Our paper makes several contributions to the literature. To the best of our knowledge, this study is the
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first to document the interesting effect of local retail participation on stock liquidity. The measure of the local white percentage is unlikely to be driven by unobserved firm-specific characteristics. We show that it
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serves as a good proxy for the level of local noise trading by retail investors. From this perspective, our
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work offers the first large-sample cross-sectional analysis of the effect of noise trading on stock liquidity. Second, our work complements the existing literature on how geographical locations affect stock
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liquidity. Loughran and Schultz (2005) show that stocks located in urban areas are more liquid. Even after controlling for the urban effect, the coefficient on the local white percentage remains strongly significant.
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We also show that the local education level is not a driver of our result. On the contrary, the variable of the local white percentage completely absorbs the explanatory power of local education level.
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Third, our paper complements the emerging literature that explores the impact of local demographics
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on corporate financial policies. Becker, Ivkovic and Weisbenner (2011) find that the fraction of local
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seniors is an important determinant of corporate dividend policy. The focus of this paper is on local retail participation and stock liquidity.
Fourth, we add more evidence to the literature that evaluates the relationship between stock liquidity and firm performance. Using the local white percentage as an instrument for stock liquidity, we find results consistent with those of Fang, Noe and Tice (2009), i.e., that stock liquidity positively affects firm value. We show that the beneficial effect of liquidity is directly related to the level of local noise trading and the competition among local institutional investors. Collectively, our paper adds to the understanding of the way that retail investor trading and local bias affect stock liquidity and firm value. The rest of the paper proceeds as follows. In the next section, we describe the data and variable definitions. Section 3 reports the summary statistics and the results on the fraction of retail trading volume. 6
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Section 4 presents our main results on stock liquidity and robustness checks. We also provide sub-sample comparisons and present the results for firms that move headquarters. In section 5, we offer market microstructure explanations of our results. In section 6, we focus on local retail participation, stock
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liquidity and firm value. Section 7 concludes.
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II. Data and Variable Definitions
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II.A Sample Selection
We compile our data from several sources. We first describe the county-level data. The county-level data
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come from the US Census Bureau Population Estimates Program from 1990 to 2005. The Population Estimates Program publishes total resident population estimates and demographic components of change
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(births, deaths, and migration) each year. It also publishes the estimates according to demographic characteristics (age, sex, race, and Hispanic origin) for the nation, states, and counties.2
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We only include the county populations aged from 35 to 85 years. The purpose of this requirement is
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to focus on the age groups that are most likely to own and trade stocks.3 We require the size of the county
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population to be larger than 50,000.4 In addition, the data on household income come from the US Census Bureau SAIPE (Small Area Income and Poverty Estimates) datasets from 1990 to 2005. The data on historical local unemployment rates come from the Bureau of Labor Statistics. Daily and monthly stock returns come from the Center for Research in Security Prices (CRSP), and firm-level accounting information is from the CRSP/Compustat Merged database. We only include stocks
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These estimates are provided with the assistance of the Federal State Cooperative Program for Population Estimates (FSCPE). The Census Bureau begins the process of preparing population estimates by updating population information from the most recent census with information found in the annual administrative records of Federal and state agencies. The Federal agencies provide tax records, Medicare records and some vital statistical information. The FSCPE agencies supply vital statistics and information about group quarters such as college dorms or prisons. The Census Bureau and FSCPE members combine census and administrative records information to produce current population estimates that are consistent with the last decennial census counts. 3 We also try to change this requirement into age groups of 20-85, 25-85 or 30-85 years. All of the results are consistent and available upon request. In all later references, “population” refers to the selected age group of 35 to 85 years. 4 All of the results are consistent if we change this requirement into above 10,000 or above 100,000.
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with book values larger than 5 million dollars to ensure that our results are not driven by tiny stocks. Our information on historical firm locations (county, state, zip code) comes from Compact Disclosures from 1990 to 2004. The corresponding values of latitude and longitude are obtained from the Gazetteer Files of
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Census 2000. Quarterly equity holdings of institutional investors come from Thomason CDA/Spectrum (13F). The locations (zip codes) of institutional investors come from their SEC filings. The various
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measures of stock illiquidity are obtained from Joel Hasbrouck’s website.5
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II.B Variable Definitions
We first describe the measures of our dependent variable: stock illiquidity. We use the Amihud (2002)
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illiquidity measure as our main variable. Hasbrouck (2009) and Goyenko, Holden and Trzcinka (2008) find
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that the Amihud illiquidity measure is highly correlated with the high-frequency TAQ-based measures and effectively captures the price impact of trading.6 The Amihud illiquidity measure averages the square root
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of the ratio of the absolute price change divided by the daily dollar volume over each day in year t. It is
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calculated as
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Illiquidity i , t
1 Dt
(1000 *
D ays t
|daily return| |daily dollar volum e|
),
where Dt is the number of days in year t. We use three other popular illiquidity measures as robustness checks. The second illiquidity measure is the estimate of the coefficient ci in the following market-adjusted model:
pi ,t ci qi ,t i rm ,t ui ,t
,
where pi ,t is the log of the price and qi ,t is a trade direction indicator that takes the values +1 or -1 for buys and sells, respectively. rm ,t is the market return and ui ,t is the residual independent of qi ,t and 5
http://pages.stern.nyu.edu/~jhasbrou/Research/GibbsEstimates2006/Liquidity%20estimates%202006.htm. Goyenko, Holden and Trzcinka (2008) conclude that the literature has generally not been mistaken in the assumption that (low-frequency) liquidity proxies measure liquidity. 6
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rm ,t . The trade direction indicators qi ,t are unobserved because the model is estimated from daily CRSP
data, and the coefficient estimates are obtained using the Gibbs sampler. The third measure is Roll’s (1984) measure of effective trading costs, estimated as the square root of
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the negative of the auto-covariance of daily log returns, which is set to zero when the sample
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auto-covariance is positive. The fourth measure is the intercept 0i and slope coefficient 1i from the
ci ,t 0i 1i zt ,
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latent common factor model given by the following equations:
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pi ,t ( 0i 1i zt )qi ,t i rm ,t ui ,t ,
where zt is the latent common factor. All four measures are winsorized such that our results are not
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driven by outliers. We do not include Pastor and Stambaugh’s (2003) gamma measure because the authors caution against its use as a liquidity measure for individual stocks.7
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The major independent variable is what we call the “local white percentage,” which represents the
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proportion of the white population in each county. Other county-level variables are defined as follows.
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“Log(local population)” is the natural log of the size of a county’s population. “Local male fraction” is the fraction of the male population. “Local senior fraction” is the fraction of the population that is more than 65 years old as in Becker, Ivković and Weisbenner (2011). “Log(local household income)” is the natural log of the median household income in each county. “Local unemployment rate” is the historical county-level unemployment rate.
Next, we define firm-specific control variables. We first construct an urban dummy. A stock is defined as an urban stock if the company is headquartered less than 100 miles from the center of one of the thirty largest US cities according to the 2000 census. The market value of assets is defined as stock price 7
Goyenko, Holden and Trzcinka (2008) find that the Pastor and Stambaugh Gamma and the Amivest illiquidity measure have little to do with price impact benchmarks used in the literature.
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(data199) * shares outstanding (data25) + short term debt(data34) + long term debt(data9) + preferred stock liquidation value (data10) – deferred taxes and investment tax credits (data35). The market-to-book ratio is defined as the market value of assets divided by the book assets (data6). Total debt is defined as
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long-term debt (data9) + short-term debt (data34). Firm leverage is total debt divided by book assets (data6). The market cap is the natural log of a firm’s market value of equity (data199*data25). Profitability
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is the operating income before depreciation (data13) divided by the book assets (data6). Firm age is
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defined as the natural log of the number of years since a firm first appears in the CRSP. Cash holding is cash and short-term investments (data1) divided by book assets (data6). Tangibility is net PPE (data8)
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divided by book assets (data6). The dividend yield is calculated as cash dividends per share (data26)/stock price (data199). Institutional ownership is the fraction of institutional holdings, calculated from the
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Thomason CDA/Spectrum Institutional Ownership Database (13F).
We follow Gaspar, Massa and Matos (2005) to define institutional turnover. We first calculate the
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portfolio churn rate of institutional investors, which captures how frequently an investor rotates his
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positions on all of the stocks of his portfolio. If we denote the set of companies held by investor i as Q, the
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churn rate of investor i at quarter s is
CR i , s
jQ
N i , j , s P j , s N i , j , s 1 P j , s 1 N i , j , s 1 P j , s
N i , j , s 1 P j , s 1 N i , j , s P j , s
,
2
jQ
where Pj , s and N i , j , s represent the price and number of shares of stock j held by investor i at quarter s. Let wi , j ,t be the weight of investor j’s holding stock i held by institutional investors at quarter t. The investor turnover of stock i is the weighted average of the total portfolio churn rates of its investors over the previous four quarters: T u rn o veri , t
j S i
1 w i , j ,t 4
4
CR r 1
j ,t r 1
.
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The yearly return is the cumulative stock return over the year. The return volatility is the standard deviation of monthly stock returns in the year. Analyst coverage is the total number of analysts covering the stock in a year.8
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III. Preliminary Analysis
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III.A Summary Statistics
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We provide summary statistics of our sample in Table I. Panel A presents summary statistics of the main variables. We report the number of observations, the mean, the 5-percent quantile, the 95-percent quantile
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and the standard deviation. For the full sample, the mean of local white percentage is 81.4% with a standard deviation of 11.9%. The 5-percent quantile is 57.3%, and the 95-percent quantile is 96.5%. The
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data show a large cross-sectional variation in the local white percentage across U.S. counties. Panel B presents the correlation matrix of local population characteristics. We report the correlations among the
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local white percentage, the size of the local population, the local male fraction, the local senior fraction, the
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local household income and the local unemployment rate. We find that the local white percentage is
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negatively correlated with the size of the local population (-0.29), positively correlated with the local male fraction (0.26), positively correlated with the local household income (0.21) and negatively correlated with the local unemployment rate (-0.30).
As expected, the local white percentage is relatively higher for northern states than that of southern states due to historical reasons. To determine whether this is simply a state effect, we summarize the variable of local white percentage by states in Panel C. We report the state name, the state code, the mean, the median, the minimum, the maximum, the standard deviation and the number of observations for each state. We conclude that the variations of the local white percentage are not merely a state effect. We find
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The data on analyst coverage come from the Thomson I/B/E/S database.
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that even for large states, such as California, there still exist considerable county variations in the percentage of local white population. The percentage ranges from 56% to 98% with a mean of 76%. For counties in the state of New York, the local white percentage ranges from 52% to 98% with a mean of 78%.
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In southern states, such as the state of South Carolina, it ranges from 56% to 91% with a mean of 79%.
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III.B Fraction of Retail Trading Volume
We argue from the existing literature that the local white percentage serves as a measure of the rate of local
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equity market participation of retail investors. If it is indeed a valid proxy, given the proximity ownership/trading behavior of retail investors, it should be positively related to the fraction of trading
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volume associated with retail investors.
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In Table II, we explore the impact of local retail investor characteristics on the cross-section of retail trading volume. Because the number of trades by institutions and individuals is not publicly available, we
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follow Shu (2006) and Hvidkjaer (2008) to infer the fraction of institutional trading volume and subtract it
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from 1 to obtain the fraction of the retail trading volume. We first calculate the fraction of institutional
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trading volume using the following formula:
N
InstitutionalFractioni ,t
| IO j 1
i , j ,t
IOi , j ,t 1 |
TOi ,t
,
where IOi , j ,t is institution j’s ownership of stock i for quarter t, calculated as institution j’s holdings of stock i divided by i’s shares outstanding at the end of quarter t. TOi ,t is the total turnover of stock i in quarter t. We then calculate the fraction of retail trading volume as RetailFractioni ,t 1 InstitutionalFractioni ,t .
Because the measure of institutional trading fraction double-counts the trades between institutions, the
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institutional fraction ranges from 0 to 2; therefore, the retail fraction ranges from -1 to 1.9 In Table II, the dependent variable is the yearly average of the retail fraction. Columns (1) and (2) are based on the full sample. Column (1) is the baseline specification. We control for state fixed effects;
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county-level characteristics, such as population, gender, age, household income, unemployment rate and an urban dummy, as well as firm-specific variables, including leverage, market-to-book, market capitalization,
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profitability, firm age, cash holding, tangibility and the dividend yield. We further control for institutional
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ownership, institutional turnover, past return and return volatility. All variables on the right-hand side are lagged values. From column (2), we include industry fixed effects at the two-digit SIC level and cluster the
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standard errors at the firm level. Columns (3)-(6) are based on sub-samples where retail trading is more likely to be evident. Column (3) is for the sub-sample where the firm’s book asset is below the sample
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median. Column (4) is for the sub-sample where the firm’s institutional ownership is below the sample median. Column (5) is for the sub-sample where the maximum price in the previous year is below the
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sample median. Column (6) is for the sub-sample where the volatility of local household income during
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the sample period is above the sample median.
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The results justify our claim that the local white percentage measures the level of the local equity market participation of retail investors. We document a strong positive relationship between the local white percentage and the fraction of retail trading volume. The effect is stronger for stocks with potentially high retail concentration (small size, low institutional ownership and low price) and especially stronger for stocks headquartered in counties with more volatile household income streams. It makes intuitive sense that in areas where investors have a higher need to trade because of volatile income, the impact of the local white percentage on the retail trading volume is indeed higher. Unreported results show that the effect is also stronger in counties where the “only-game-in-town” effect of Hong, Kubik and Stein (2008) is higher. For other demographic characteristics, the local unemployment rate is negatively related to retail trading 9
The average institutional trading fraction in our sample is 57%, consistent with the findings of Shu (2006) and Hvidkjaer (2008).
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volume. The local senior fraction is negatively related to the retail trading volume, consistent with the finding of Becker, Ivković and Weisbenner (2011) that seniors are largely buy-and-hold investors with longer investment horizons. For firm-specific control variables, market cap, institutional ownership and
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dividend yield are negatively related to retail trading volume, whereas return volatility, cash holding and
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market-to-book are positively related to retail trading volume.
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IV. Results on Stock Liquidity IV.A Main Results
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Having established that the local white percentage serves as a nice proxy for the level of local retail participation, and there are enough cross-sectional variations for us to exploit, we now present our main
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results of stock liquidity on the local white percentage.
We report the findings in Table III. The dependent variable is the Amihud illiquidity measure, which
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is defined as the square root of the average daily price impact of trading in each year. Column (1) is the
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baseline specification. In column (2), we add more controls on local characteristics including the local
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male fraction, the local senior fraction, the local household income, the local unemployment rate and an urban dummy. From column (3), we cluster the standard errors at the firm level. We add more firm-level controls in column (4). In column (5), we add state fixed effects. In column (6), we further include industry fixed effects (two-digit sic) and a credit rating fixed effect (investment grade/junk/non-rated). ***, ** and * represent significance levels of 1%, 5% and 10%, respectively, using robust standard errors with t-statistics given in parentheses.
The results show that the local white percentage is strongly and negatively related to stock illiquidity. We always include exchange dummies (NYSE/AMEX/NASDAQ) to control for the microstructural differences among exchanges that might affect stock liquidity (Pagano and Roell (1996)). We include 14
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industry and state dummies to control for other unobserved industry and state effects (for instance, tax reasons as in Ivković, Poterba and Weisbenner (2005)) that affect the trading behavior of individual investors. We include credit rating dummies to control for the effects of credit ratings on stock liquidity
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(Odders-White and Ready (2006)). In the most complete specification, i.e., in column (6) of Table III, the coefficient on the local white percentage is -0.624 with a t-statistic of -5.95, which is not only statistically
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significant but also economically relevant. A one-standard-deviation (11.9%) increase in the local white
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percentage increases the liquidity of a stock by 10.4%.10
The results for control variables are interesting. We find that the size of the local population and the
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local male fraction are negatively related to stock illiquidity, whereas the local household income11 and local unemployment rate are positively related to stock illiquidity. However, these relationships lose most
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of their statistical significance when we cluster the standard errors at the firm level and include more firm-specific controls. The urban dummy is significantly negatively related to stock illiquidity, which is
d
consistent with Loughran and Schultz (2005). Consistent with Gopalan, Kadan and Pevzner (2009), the
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market cap is negatively related to stock illiquidity, leverage is positively related to stock illiquidity and the
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cash holding is negatively related to stock illiquidity. Consistent with intuition, firm age is positively related to stock illiquidity, whereas institutional ownership, institutional turnover and past stock return are negatively related to stock illiquidity. IV.B Robustness Checks
Next, we provide some robustness checks on the positive relationship between local retail participation and stock liquidity. We report the results in Table IV. Panel A is for sub-sample analysis. We separate the sample by the local population and the sample period. In columns (1) and (2), we separate the sample by 10
To put this number into perspective, we find that a one-standard-deviation increase in cash holdings increases stock liquidity by 7%, a one-standard-deviation increase in the historical return increases stock liquidity by 11%, and a one-standard-deviation increase in firm leverage reduces stock liquidity by 7%. 11 This finding is to some extent consistent with Becker, Cronqvist and Fahlenbrach (2010) in that the presence of local wealthy individual blockholders reduces stock liquidity.
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the size of local population (above/below median)12. In columns (3) and (4), we analyze the subsamples separated by sample period (before 1998/after 1998). We always utilize the “full specification”, as in column (6) of Table III, and cluster the standard errors at the firm level. For brevity, we do not report the
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control variables. We find consistent results in both sub-samples separated by the size of population. The impact of the
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local white percentage is higher in relatively more populous counties. We also try to break the sample up
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by the number of firms in each state-year and find similar results in both subsamples (unreported), which means that the impact of local retail participation holds for both large and small states. Interestingly, the
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effect is stronger in the before-1998 period, which is consistent with the idea that in the pre-Internet age, the “locality” of retail trading should be higher.
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In Panel B of Table IV, we use different measures of stock illiquidity as dependent variables. We employ four measures from column (1) to column (4). C^bma is the coefficient on the change in the trade
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direction indicator in the basic market-adjusted model. C^roll is the Roll (1984) trading cost estimator
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excluding quote mid-points. Gamma0 and Gamma1, respectively, are the intercept and slope coefficient in
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a latent common factor model to estimate the effective costs of trading. Consistent with the results on the Amihud illiquidity measure, in all specifications, the variable of the local white percentage is significantly and negatively related to stock illiquidity.
In Panel C of Table IV, we use alternative measures of the local white percentage. In columns (1) and (2), the local white percentage is defined as log(1+county white population*county average income/county book assets). County book assets are the sum of book assets for all firms headquartered in the county. In columns (3) and (4), the local white percentage is defined as log(1+county white population*county average income/county market assets). County market assets are the sum of market assets for all firms
12 The sample median of the county population is 457,216. Unreported results show that even among counties with a population size above the sample median, the percentage of local white population ranges from 52% to 95% with a mean of 78%.
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headquartered in the county, which is along the line of Hong, Kubik and Stein (2008) and represents the per-asset supply of local white households. We find that in all specifications, the alternative measures of local white percentage are significantly and negatively related to stock illiquidity. However, the drawbacks
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of these variables are that they depend on both the supply and the demand of liquidity by local firms. They are not a purely supply-based measure. We therefore always use them as robustness checks.
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In Panel D of Table IV, we perform between-effects and firm fixed effects regression of stock
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illiquidity on the local white percentage. The dependent variable is Amihud illiquidity. Column (1) fits random effects models using the between regression estimator. Given the unbalanced panel, column (2)
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performs between-effects estimation with weighted least squares rather than the default OLS. Column (3) fits random-effects models using the population-averaged estimator. In column (4), we run OLS
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regressions with firm fixed effects and cluster the standard errors at the firm level. In all specifications, we find consistent results that the local white percentage is significantly and negatively related to stock
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illiquidity. For the regression with firm-fixed effects, the significance of the local white percentage is
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weaker than before (coefficient of -0.452 with a t-statistic of -2.59), which actually supports our claim that
variations.
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the explanatory power of the local white percentage mostly comes from cross-sections instead of time
Panel E of Table IV deals with the concerns that liquid firms self-select themselves into counties with a high local white percentage. We therefore separate the sample by firm age and also focus on the firms that never move headquarters. Later, we will examine the change of liquidity for firms that do move their headquarters. Column (1) is for the sub-sample with a firm age of more than 10 years. Column (2) is for the sub-sample with å firm age of less than 10 years. Columns (3) and (4) are for the firms that never move headquarters during the sample period. The dependent variable is the Amihud illiquidity measure in columns (1)-(3), whereas in column (4), it is the Gibbs estimate of C^bma. We find that the result holds for both sub-samples separated by firm age and is actually slightly higher for relatively older firms. The effect 17
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of the local white percentage on illiquidity remains strongly negative for the sub-sample of firms that never move headquarters during the sample period. IV.C Sub-sample Comparisons
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Because we argue that our result is driven by local retail trading, it should be stronger along the
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dimensions where the effects of individual investors are expected to be more evident. We therefore separate the sample according to those dimensions and compare the impact of the local white percentage
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between sub-samples. We consider firm characteristics including firm size, institutional ownership, price, P/E ratio, analyst coverage and return volatility.13 We also consider county characteristics such as the
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“only-game-in-town” effect and average household income volatility.
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We report the results in Table V. In Panel A, we separate the full sample by the size of book assets (columns (1) and (2), above median/below median), and the level of institutional ownership (columns (3)
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and (4), above median/below median). In Panel B, we separate the full sample by the maximum stock price
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in the previous year (columns (1) and (2), above median/below median), and the price-earnings ratio (columns (3) and (4), above median/below median). In Panel C, we separate the full sample by the level of
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analyst coverage (columns (1) and (2), above 1/below 1), and the return volatility (columns (3) and (4), above median/below median). In Panel D, we separate the full sample by the only-game-in-town effects (columns (1) and (2), above median/below median), and the local household income volatility (columns (3) and (4), above median/below median). The only-game-in-town effect is measured as log(county population*average income/county book assets). Local household income volatility is calculated as the volatility of a county’s average household income during the sample period. We always utilize the “full specification” as in column (6) of Table III and cluster the standard errors at the firm level. For brevity, we
13
Kumar and Lee (2006) suggest that small-cap stocks, value stocks, stocks with lower institutional ownership and lower-priced stocks are more likely to have a higher retail concentration. Kumar (2009) shows that individual investors prefer stocks with lottery features (low price, high volatility).
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do not report the control variables. We perform a Chi-squared test to evaluate the differences in coefficients between sub-samples. ***, ** and * represent significance levels of 1%, 5% and 10%, respectively.
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The results strongly support our claim that the effect of the local white percentage on stock liquidity is due to local retail trading. We find that the impact of the local white percentage on stock illiquidity is
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significantly stronger for small firms (-0.665 vs. -0.189), for firms with low institutional ownership (-0.797
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vs. -0.116), for stocks with a low price (-0.713 vs. -0.059), for stocks with a low P/E ratio (-0.759 vs. -0.387), for stocks with low analyst coverage (-0.780 vs. -0.112), and for stocks with high return volatility
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(-0.740 vs. -0.480). Consistent with the previous results on retail trading volume, the impact of the local white percentage on stock illiquidity is significantly stronger for counties with a higher
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“only-game-in-town” effect (-0.627 vs. -0.186) and for counties with more volatile household income
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IV.D Firms Moving Headquarters
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streams (-0.766 vs. -0.160). The differences in coefficients between sub-samples are always significant.
We now analyze the change of stock illiquidity and the change of local retail participation for the firms that
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re-allocated headquarters during the sample period. We report the results in Table VI. Historical firm locations come from Compact Disclosure. We denote a firm as a mover if the location of its headquarters in year t is in a different county from its location in year t-1. The dependent variable is the difference in stock illiquidity between year t+1 and year t-1. The right-hand-side variables also change from year t-1 to year t+1. The dependent variable in columns (1) and (2) is Amihud illiquidity, and it is C^bma, C^Roll, Gamma0 and Gamma1 from column (3) to column (6), respectively. Year, state and industry fixed effects are always included. Among the movers, on average, the change in Amihud illiquidity is -0.0049, and the change in the local white percentage is 1.8%. The relationship between the change in stock illiquidity and the change in 19
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the local white percentage is negative and significant, which means that when firms move from counties with a low share of white population to counties with a high share of white population, there is an increase in stock liquidity. The results are not only statistically significant but also economically relevant. A
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one-standard-deviation increase in the change of local white percentage (16.1%) increases stock liquidity by 20.5% (column 2 of Table VI). The results for other illiquidity measures are consistent.
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IV.E Further Evidence
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Finally, we provide some further evidence on the effect of local retail participation on stock illiquidity. Hong, Kubik and Stein (2004) document that college graduates are more likely to participate in the equity
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market. It is therefore worthwhile to jointly consider the impact of the local education level, the local white
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percentage and stock liquidity. We measure the local education level as the percentage of adults in each county with some college education, which includes those who completed one to three years of college.
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The data at the county education level come from the Economic Research Service in the US Department of
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Agriculture. Moreover, we perform another robustness check to our mail results, by examining the relationship between local equity market participation and stock illiquidity at the county level.
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We present the results in Table VII. Panel A runs county-level regressions of local education level on local population characteristics. In Panel B, we examine the effects of local education level and local racial composition on stock illiquidity. The dependent variable in columns (1) and (2) is the Amihud illiquidity measure. The dependent variable in columns (3) and (4) is the Gibbs estimate of trading costs C^bma. In columns (2) and (4), we include both the local educational level and the local white percentage in the regression. We always utilize the “full specification” as in column (6) of Table III and cluster the standard errors at the firm level. In Panel C, we perform the analyses at the county level. Specifically, for each county, we calculate the average illiquidity and average firm characteristics across all the firms headquartered in the county. The dependent variable is the average firm illiquidity in the county. There are 20
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5399 county-year observations in the regressions. Indeed, the local education level and the local white percentage are positively related with a correlation of 0.27. The positive relationship still holds in county-level multivariate regressions, but the
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local white percentage alone only explains about 1.5% of the local education level in terms of R-squared. Panel B shows that, if put separately, the local educational level is significantly and negatively related to
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stock illiquidity. However, if we put the local white percentage and the local education level together in the
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regression, the former entirely absorbs the explanatory power of the latter. For the Amihud illiquidity measure, the t-statistic of the local education level drops from -3.72 to -1.07 on the inclusion of local
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whites. We find a similar pattern for C^bma. In both cases, the variable of local white percentage remains highly significant as before. This result further strengthens our belief that the measure for the local white
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percentage indeed captures the level of retail participation in the equity market.14 Also, the county-level results are consistent with the previous ones that local equity market participation is strongly negatively
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related to the county-average stock illiquidity.
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V. Market Microstructure Explanations
In the previous section, we established a positive relationship between the local white percentage and stock liquidity. We argue that the channel is generated through the trading of local retail investors. We now provide additional tests to strengthen this argument. Specifically, we use an instrumental variables regression approach to relate stock illiquidity to the measure of retail trading fraction as previously constructed in Table II, where we instrument the retail trading fraction by the variable of local white percentage. Effectively, this represents a two-stage OLS regression: in the first stage, we use the local white percentage to explain retail trading fraction of the stock, and in the second stage, we use the
14 It is also consistent with the findings of Choudhury (2002) that at every education level, the ownership of risky assets by white households is much higher than that by minority households.
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explainable (predicted) part of retail trading fraction by the local white percentage from the first stage regression to explain stock illiquidity. We report these results in Table VIII. In columns (1)-(3), we report the results for the subsample of
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firms with high retail concentration, such as small firms, firms with low institutional ownership and stocks with low price. In columns (4)-(6), we report the results for the subsample of firms with low retail
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concentration, such as large firms, firms with high institutional ownership and stocks with high price. The
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results strongly support our argument that local equity market participation affects stock liquidity through the trading of local retail investors. The instrumented retail trading fraction is highly negatively related to
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stock illiquidity for stocks with high retail concentration, i.e., small firms, firms with low institutional ownership and stock with low prices. The F-statistics in the first-stage regressions are around 10 showing
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that the instrument is not weak. On the contrary, the results are not significant for stocks with low retail concentration with the F-statistics well below 5, showing that local retail participation may not be large
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enough to drive the overall retail trading of these stocks.
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Next, Seasholes and Zhu (2009) show that local individual investors tend to invest and trade local
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stocks because of familiarity instead of information-based reasons. In the market-microstructure literature, such trading is usually referred as “noise trading”. Therefore, our findings are consistent with market microstructure models that predict a positive relationship between noise trading and stock liquidity (Glosten and Milgrom (1985) and Admati and Pfleiderer (1988)).15 The key difference between the two models is that in Admati and Pfleiderer (1988), the informed trading is endogenously determined. In AP (1988), with endogenous informed trading, an equilibrium exists in which noise trading increases stock liquidity. Under their framework, increased noise trading increases the profits of informed traders while at the same time attracting more informed traders with the 15
Greene and Smart (1999) use the “Investment Dartboard” column on The Wall Street Journal as a natural experiment to evaluate the effect of the change of noise trading on the change of liquidity. Their evidence is consistent with Glosten and Milgrom (1985) and Admati and Pfleiderer (1988), but they do not perform further tests to distinguish between the two models.
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same information. Therefore, the informed trader must trade more aggressively to exploit the short-lived information and as a result compete away the information advantage. In equilibrium, the market maker faces a smaller adverse selection problem when noise trading increases.
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Given that endogenous informed trading seems to be a rather reasonable assumption, we expect the effect of local retail participation to be dependent on the level of competition among informed traders. It is
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generally accepted that institutional investors possess an information advantage over locally headquartered
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stocks (Coval and Moskowitz (1999, 2001)). Institutional investors that tilt their portfolios toward small stocks and have short investment horizons are more active in collecting information and pursuing
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information-based trading (Coval and Moskowitz (1999, 2001), Baik, Kang and Kim (2009), Yan and Zhang (2009)). We therefore perform sample separation by the number of local institutional investors and
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compare the effect of local retail participation in different sub-samples. We also distinguish institutional investors by their investment style (small/large) and trading style (transient/non-transient).
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We report the results in Table IX. Because CDA/Spectrum 13F does not provide the locations of
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institutional investors, we collect the zip codes of the locations of money managers from their SEC filings
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downloaded from Morningstar Document Research.16 The corresponding values of latitude and longitude are obtained from the Gazetteer Files of Census 2000. We obtain the style classifications of institutional investors from Brian Bushee’s website. The investment style is “small” if the Bushee investor style classification is “SGR” (small growth) or “SVA” (small value). The investment style is “large” if the Bushee investor style classification is “LGR” (large growth) or “LVA” (large value). The trading style is “transient” if the Bushee investor type classification is “TRA” (transient). The trading style is “non-transient” if the Bushee investor type classification is “QIX” (quasi-indexed) or “DED” (dedicated)17.
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Morningstar Document Research provides the SEC filings of money managers from 1998 onward. For the sample period before 1998, we backfill the information on institutional locations using the 1998 data. 17 The detailed definitions of style classifications can be found in Bushee (2001), Bushee and Noe (2000) and from his website at: http://acct3.wharton.upenn.edu/faculty/bushee/.
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For each firm-year, we count the number of institutional investors that are located within a 250-mile radius18 around the firm’s headquarter. In Panels A, C and D, the dependent variable in columns (1) and (2) is the Amihud illiquidity. The dependent variable in column (3) and column (4) is the Gibbs estimate of
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trading costs, C^bma. We always utilize the “full specification” as in column (6) of Table III and cluster the standard errors at the firm level. For brevity, we do not report the control variables. We perform a
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Chi-squared test to evaluate the differences in coefficients between sub-samples.
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The results are consistent with AP (1988). In Panel A, the impact of local noise trading on stock liquidity is much stronger for stocks with more institutional investors nearby. Panels C and D show that it
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is especially true for local institutional investors that are expected to be more active in information acquisition, that is, investors with a “small” investment style (Panel C1) or “transient” trading style (Panel
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D1). If we separate the sample by local institutional investors with a “large” investment style (Panel C2) or a “non-transient” trading style (Panel D2), there is no significant difference between the sub-samples.
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We have shown that local noise trading has a stronger impact on stock liquidity for stocks with a high
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retail concentration. To determine whether the results are actually driven by some firm characteristics, in
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Panel B of Table IX, we report the correlations between indicators created by the number of local institutional investors and indicators created by firm characteristics. For example, the indicator (firm size) is 1 if the book size is above the sample median and 0 otherwise. The indicator (investment style: small) is 1 if the number of local small-style institutional investors is above the sample median and 0 otherwise. Other indicator variables are similarly defined. For indicators based on firm characteristics, we consider firm size, institutional ownership, price, P/E ratio, analyst coverage, return volatility, the “only-game-in-town” effect and local household income volatility. We find that indicators of the local number of institutional investors are not correlated with indicators
18 The use of a 250-mile radius follows Ivković and Weisbenner (2005) in that it can be seen as a plausible upper bound on the span of local information.
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of firm characteristics. The indicator (investment style: small) has a correlation of 0.01 with the indicator (size), 0.03 with the indicator (institutional ownership), 0.04 with the indicator (price), -0.01 with the indicator (P/E), -0.01 with the indicator (analyst coverage), -0.01 with the indicator (return volatility),
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-0.11 with the indicator (only-game-in-town), and a correlation of 0.07 with the indicator (local household
local institutional investors are not driven by firm characteristics.
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VI. Results on Firm Value
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income volatility). Therefore, we conclude that the sub-sample differences according to the number of
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In this section, we extend our analysis to determine whether local retail participation directly affects firm value. Fang, Noe and Tice (2009) investigate the relationship between stock liquidity and firm
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performance. They document that liquid firms have better performance measured by the market-to-book ratio. They argue that the beneficial effect of liquidity is generated through a feedback channel where
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liquidity stimulates the entry of informed investors who make prices more informative to stakeholders.
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From AP (1988), if in equilibrium an increase in noise trading stimulates the entry and competition of
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informed traders, resulting in an increase in stock liquidity, we should expect a positive relationship between local retail participation and firm value. We report the results in Table X. Panel A is our main specification. Columns (1)-(3) are OLS regressions using the same set of control variables as in Table III. We control for local characteristics, such as population, household income, unemployment rate, gender, age and an urban dummy, as well as firm-specific variables, including leverage, market size, profitability, firm age, cash holding, tangibility, dividend yield, institutional ownership, stock turnover, past return and return volatility. All right-hand-side variables are lagged values. Columns (4)-(6) are instrumental variable (IV) regressions. In columns (4) and (5), we instrument stock illiquidity with the local white percentage. In column (6), we use both the local white percentage and the local education level as instruments. We report the F-statistic of the 25
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weak-identification test to test the presence of a weak instrument and the Hansen J statistic (p-value) of the over-identification test to justify the validity of instruments. For robustness checks, in Panel B of Table X, we run between-effects and firm fixed effects
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regressions of firm value on local equity market participation. Column (1) fits random effects models by using the between regression estimator. Given the unbalanced panel, column (2) performs a
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between-effects estimation with weighted least squares rather than the default OLS. Column (3) fits
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random-effects models using the population-averaged estimator. In column (4), we run OLS regressions with firm fixed effects and standard errors clustered at firm level. Here the “Full Specification” refers to
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the specification as in Panel A, column (3). For the sake of brevity, we do not report the control variables in the regression.
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The results are interesting. The variable of the local white percentage is significantly and positively related to firm value. A one-standard-deviation increase in the local white percentage increases the firm’s
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market-to-book ratio by 5% (column 3 of Panel A). The IV regressions confirm the findings of Fang, Noe
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and Tice (2009) that stock liquidity increases firm performance. This result is robust to the inclusion of
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industry and state fixed effects as well as credit rating fixed effects. The F-statistic of the weak-identification test is always above 10, and the Hansen J-statistic (p-value) is 0.028 (0.86). Therefore, the quality of instruments is very high. The result for between-effects regressions remains highly significant. Consistent with before, the magnitude of the fixed effect regression is reduced, but it remains significant with a t-statistic of 2.16, which means that the explanatory power of local white percentage mostly comes from the cross-sections instead of time variations. From Panel C to Panel E in Table X, we perform sub-sample comparisons. In Panel C, we separate the sample according to the size of book assets (columns (1) and (2)), and the level of institutional ownership (columns (3) and (4)). Panel D separates the sample according to the number of local institutional investors and their investment styles. Columns (1) and (2) are based on the number of local 26
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small-style institutional investors. Columns (3) and (4) are based on the number of local large-style institutional investors. Panel E separates the sample by the number of local institutional investors and by their trading styles. Columns (1) and (2) are based on the number of local transient institutional investors.
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Columns (3) and (4) are based on the number of local non-transient institutional investors. We perform a Chi-squared test to evaluate the differences in coefficients between the sub-samples. The standard errors
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are clustered at the firm level in all specifications.
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Consistent with the previous findings on stock liquidity, the effect of the local white percentage on firm value is stronger for small firms and firms with low institutional ownership. More importantly, the
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result is stronger for stocks surrounded by more local institutional investors who are supposed to be more active in information acquisition, i.e., investors with “small” investment or “transient” trading styles. Our
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results complement those of Fang, Noe and Tice (2009) in that the beneficial effect of liquidity on firm value is directly linked to the level of local noise trading and competition among local institutional
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VII. Conclusion
te
d
investors.
In this paper, we investigate the relationship between local equity market participation of retail investors and stock liquidity. We use county-level racial composition as a proxy for the level of local retail trading. Our work is built upon existing studies that document striking racial differences in risky asset holdings and proximity investment behavior of individual investors. We provide evidence that there are large cross-sectional variations in racial compositions among U.S. counties, which allows us to exploit the cross-sectional differences such that we can investigate how local retail trading affects stock liquidity. We find that stocks headquartered in counties with a higher percentage of local white population are more liquid. The impact is stronger for stocks with a high retail concentration (i.e., small-size, low institutional ownership, low price, value, high volatility and scarce analyst coverage); it is also stronger for 27
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firms located in the “only-game-in-town” counties and in counties with more volatile household income streams. Our findings are consistent with microstructure models that predict a positive relationship between
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noise trading and stock liquidity. In particular, we provide evidence supporting the predictions of Admati and Pfleiderer (1988) that the impact of local retail participation on stock liquidity is stronger when there
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are more institutional investors located close by, especially local institutional investors with “small”
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investment styles and “transient” trading styles.
Finally, we document a positive effect of local retail participation on firm value, supporting the view
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that liquidity increases firm performance. Collectively, our results add to the understanding of how retail
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investor trading and local bias affect stock liquidity and firm value.
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Baik, B., Kang, J. and Kim, J., 2009, Local Institutional Investors, Information Asymmetries and Equity Returns, Journal of Financial Economics, Forthcoming. Barber, B. M., and T. Odean, 2001, Boys Will Be Boys: Gender, Overconfidence, and Common Stock Investment, Quarterly Journal of Economics, 116, 261–292. Barber, B. M., T. Odean, and N. Zhu, 2006, Do Noise Traders Move Markets?, Working paper, UC Davis.
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Black, F., 1986, Noise, Journal of Finance, 41, 529–543. Becker, B. O., Zoran Ivković, and Scott Weisbenner, 2011, Local dividend clienteles, The Journal of Finance 66, 655-683.
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Blau, Francine D. and John W. Graham, 1990, Black-White Differences in Wealth and Asset Composition, Quarterly Journal of Economics, 105, 321-339.
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Brimmer, Andrew, 1998, Income, Wealth, and Investment Behavior in the Black Community, American Economic Review, 78, 151-155. Bushee, Brian, 1999, A Taxonomy of Institutional Investors: How Investor Behavior Matters, Investor Relations Quarterly 2, 13-18.
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Chordia, T., and A. Subrahmanyam, 2004, Order Imbalance and Individual Stock Returns: Theory and Evidence, Journal of Financial Economics, 72, 485–518.
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Choudhury, S., 2002, Racial and Ethnic Differences in Wealth Holdings and Portfolio Choices, Division of Economic Research, Social Security Administration Working Paper.
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Coval, J., Moskowitz, T., 1999. Home bias at home: Local equity preference in domestic portfolios. Journal of Finance 54, 2045-2073. Coval, J., Moskowitz, T., 2001. The geography of investment: Informed trading and asset prices. Journal of Political Economy 109, 811-841.
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Fang, V., Noe, T., and Tice, S., 2009, Stock Market Liquidity and Firm Value, Journal of Financial Economics 94, 150-169. Gaspar, J. M., Massa, M., and Matos, P., Shareholder Investment Horizons and the Market for Corporate Control, Journal of Financial Economics 76, 135-165. Glosten, L. R., and P. R. Milgrom, 1985, Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders, Journal of Financial Economics, 14, 71–100. Gopalan, R., Kadan, O. and Pevzner, M., Investment Decisions, Asset Liquidity and Stock Liquidity, working paper, 2009. Goyenko, R.Y., Holden, C.W. and Trzcinka, C.A., Do Liquidity Measures Measure Liquidity?, Journal of Financial Economics, forthcoming. Griffin, J. M., J. H. Harris, and S. Topaloglu, 2003, The Dynamics of Institutional and Individual Trading, Journal of Finance, 58, 2285–2320. Grinblatt, M., and M. Keloharju, 2000, The Investment Behavior and Performance of Various Investor Types: A Study of Finland’s Unique Data Set, Journal of Financial Economics, 55, 43–67. Grinblatt, M., and M. Keloharju, 2001a, How Distance, Language, and Culture Influence Stockholdings and Trades, Journal of Finance, 56, 1053–1073. 29
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Grinblatt, M., and M. Keloharju, 2001b, What Makes Investors Trade?, Journal of Finance, 56, 589–616. Grossman, S. J., 1976, On the Efficiency of Competitive Stock Markets Where Traders have Diverse Information, Journal of Finance, 31, 573-585. Grossman, S.J. and Stiglitz, J.E., 1980, “On the Impossibility of Informationally Efficient Markets”, American Economic Review 70, 393-408.
ip t
Guiso, Luigi, Paola Sapienza, and Luigi Zingales, 2004, Does local financial development matter?, Quarterly Journal of Economics 119, 929-969. Hasbrouck, J., 2009, Trading Costs and Returns for US Equities: Estimating Effective Costs from Daily Data, Journal of Finance, forthcoming.
cr
Hong, Harrison, Jeffrey Kubik, and Jeremy Stein, 2008, The only game in town: Stock price consequences of local bias, Journal of Financial Economics 90, 20-37.
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Hong, H., Kubik, J., Stein, J., 2004, Social interaction and stock market participation, Journal of Finance 59, 137-163. Huberman, Gur, 2001, Familiarity breeds investment, Review of Financial Studies 14, 659-680.
an
Hvidkjaer, Soeren, 2008, Small Trades and the Cross-Section of Stock Returns, Review of Financial Studies 21, 1123-1151.
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Ivković, Z., and S. J. Weisbenner, 2005, Local Does as Local Is: Information Content of the Geography of Individual Investors’ Common Stock Investments, Journal of Finance, 60, 267–306. Ivković, Z., James Poterba, and Scott Weisbenner, 2005, Tax-motivated trading by individual investors, American Economic Review 95, 1605-1630.
d
Loughran, Tim, and Paul Schulz, 2005, Liquidity: Urban versus rural firms, Journal of Financial Economics 78, 341-374.
te
Loughran, T., Schultz, P., 2004. Weather, stock returns, and the impact of localized trading behavior. Journal of Financial and Quantitative Analysis 39, 343-364. Kyle, A. S., 1985, Continuous Auctions and Insider Trading, Econometrica, 53, 1315–1335.
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Kumar, Alok and Lee, Charles, 2006, Retail Investor Sentiment and Return Comovements, Journal of Finance 61, 2451-2486. Kumar, Alok, 2009, Who Gambles in the Stock Market?, Journal of Finance 64, 1889-1933. Massa, Massimo, and Andrei Simonov, 2006, Hedging, familiarity and portfolio choice, Review of Financial Studies 19, 633-685. Mabry, Tristan, 1999, Black Investors Shy Away from Stocks, Wall Street Journal, May 14. Nofsinger, J. R., and R. W. Sias, 1999, Herding and Feedback Trading by Institutional and Individual Investors, Journal of Finance, 54, 2263–2295. Odean, T., 1999, Do Investors Trade Too Much?, American Economic Review, 89, 1279–1298. Odders-White, E. and Ready, M., 2006, Credit Ratings and Stock Liquidity, Review of Financial Studies, 19, 119-157. Pagano, M., and Roell, A., 1996, Transparency and liquidity: a comparison of auction and dealer markets with informed trading, Journal of Finance,51, 579–611. Roll, R., 1984, A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market, Journal of Finance, 39, 1127–1139. 30
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Seasholes, M., Zhu, N., 2009, Is There Information in the Local Portfolio Choices of Individuals, Journal of Finance, forthcoming. Shleifer, A., and R. W. Vishny, 1997, The Limits of Arbitrage, Journal of Finance, 52, 35–55. Shu, T., 2006, Trader Composition, Price Efficiency, and the Cross-Section of Stock Returns, working paper, University of Texas at Austin.
ip t
Sophie Shive, 2010, “How do Local Investors Affect Prices? Power Outages as a Natural Experiment”, working paper. Yan X. and Zhang Z., 2009, Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?, Review of Financial Studies, Forthcoming.
Ac ce p
te
d
M
an
us
cr
Zhu, N., 2002, The local bias of individual investors. Unpublished working paper, Yale University.
Appendix: Variable Definitions County Level Variables Local white percentage: the percentage of white population in each county. We only include the population aging from 35 to 85. The data comes from the US Census county population estimates datasets from 1990 to 2005. 31
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cr
ip t
Log(local population): the natural log of the size of county population aging from 35 to 85. The data comes from the US Census county population estimates datasets from 1990 to 2005. Local male fraction: the percentage of male population in each county. We only include the population aging from 35 to 85. The data comes from the US Census county population estimates datasets from 1990 to 2005. Local senior fraction: the percentage of population more than 65 years old in each county. We only include the population aging from 35 to 85. The data comes from the US Census county population estimates datasets from 1990 to 2005. Log(local household income): the natural log of the median household income in each county. The data come from the US Census Bureau SAIPE (Small Area Income and Poverty Estimates) datasets from 1990 to 2005. Local unemployment rate: the historical rate of unemployment in each county. The data comes from the Bureau of Labor Statistics.
us
Firm Specific Variables
an
Retail trading fraction: the fraction of trading volume associated with retail investors. We infer the fraction of institutional trading volume from the CDA/Spectrum quarterly institutional holdings data. We first calculate the fraction of institutional trading volume using the following formula: N
j 1
M
InstitutionalFractioni ,t
| IO
i , j ,t
IOi , j ,t 1 |
TOi ,t
,
where IOi , j ,t is institution j’s ownership of stock i for quarter t, calculated as institution j’s holdings of stock i divided by i’s shares outstanding at the end of quarter t. TOi ,t is the total turnover of stock i at quarter t. We then
d
calculate the fraction of retail trading volume as:
te
RetailFractioni ,t 1 InstitutionalFractioni ,t .
Ac ce p
Amihud illiquidity: the Amihud (2000) illiquidity measure averages over each day in year t the square root of the ratio of the absolute price change divided by daily dollar volume. It is calculated as:
Illiquidity i ,t
1 Dt
(1000 *
D ays t
|daily return| |daily do llar volum e|
),
where Dt is the number of days in year t.
C^bma (Gibbs estimate): the estimate of the coefficient ci in the following market-adjusted model:
pi ,t ci qi ,t i rm ,t ui ,t
where pi ,t is the log of the price and qi ,t is a trade direction indicator taking the values +1 or -1 for buys and sells respectively. rm ,t is the market return and ui ,t is the residual independent of qi ,t and rm ,t . The trade direction indicators qi ,t are unobserved because the model is estimated from daily CRSP data, and the coefficient estimates are obtained using the Gibbs sampler. C^roll (Roll’s model): the Roll (1984) measure of effective trading costs, estimated as the square root of the negative of the autocovariance of daily log returns, and set to zero when the sample autocovariance is positive. Gamma0, Gamma1 (Latent Factor Model): the intercept 0i and slope coefficient 1i from the latent common factor model given by the following equations: 32
Page 34 of 53
ci ,t 0i 1i zt ,
pi ,t ( 0i 1i zt )qi ,t i rm ,t ui ,t ,
where zt is the latent common factor.
jQ
N i , j , s P j , s N i , j , s 1 P j , s 1 N i , j , s 1 P j , s
N i , j , s 1 P j , s 1 N i , j , s P j , s
d
CR i , s
M
an
us
cr
ip t
Urban dummy: a stock is defined as an urban stock if the company is headquartered less than 100miles from the center of one of the top thirty largest US cities according to the 2000 census. Market value of assets: stock price (data199) * shares outstanding (data25) + short term debt(data34) + long term debt(data9) + preferred stock liquidation value (data10) – deferred taxes and investment tax credits (data35). Market-to-book: market value of assets/book assets (data6) Firm leverage: (long term debt (data9) + short term debt (data34))/book assets (data6) Market cap: natural log of a firm’s market value of equity (data199*data25) Profitability: operating income before depreciation (data13)/book assets (data6) Firm age: natural log of the number of years since a firm appear in CRSP. Cash holding: cash and short-term investments (data1)/book assets(data6) Tangibility: net PPE (data8)/book assets (data6) Dividend yield: cash dividends per share (data26)/stock price (data199) Institutional ownership: fraction of institutional ownership, calculated from Thomason CDA/Spectrum institutional ownership Database (13F). Institutional turnover: We first define portfolio churn rate of institutional investors. It captures how frequently an investor rotates his positions on all the stocks of his portfolio. Investor-level portfolio information comes from Thomason CDA/Spectrum, a database of quarterly 13-F filings of money managers to the SEC. If we denote the set of companies held by investor i by Q, the churn rate of investor i at quarter s is:
2
te
jQ
,
where Pj , s and N i , j , s represent the price and number of shares of stock j held by investor i at quarter s. Let wi , j , t
Ac ce p
be the weight of investor j’s holding stock i held by institutional investors at quarter t. The investor turnover of stock i is the weighted average of the total portfolio churn rates of its investors over the previous four quarters: T u rn o veri , t
j S i
1 w i , j ,t 4
4
CR r 1
j ,t r 1
.
Past return: cumulative stock returns in the previous year. Return volatility: standard deviation of stock returns in the previous year. Analyst coverage: the total number of analysts covering the stock in a year. The data come from Thomson I/B/E/S database. It is 0 if there is no analyst coverage. State-fixed effects: dummy variables for states. Industry-fixed effects: two-digit sic dummies. Exchange dummies: dummy variables for NYSE, AMEX and NASDAQ respectively. Credit rating dummies: dummy variables for investment grade, junk and non-rated respectively. Table I Summary Statistics Panel A: Summary Statistics of Variables Panel A presents summary statistics of the main variables used in the subsequent analysis. We report the number of observations, 33
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the mean, the 5%-quantile, the 95%-quantile and the standard deviation. “Local” variables are defined at the county level. The data on county population characteristics come from the US Census Bureau population estimates program from 1990 to 2005. The measures on stock illiquidity are obtained from Joel Hasbrouck’s website. Stock returns and firm level accounting information are from CRSP and Compustat. The detailed definitions of each variable can be found in the Appendix. 5%-Quantile
95%-Quantile
Std. Dev.
58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471
81.4% 13.03 47.4% 21.6% 10.66 5.1% 0.73 0.43 0.72 1.35 1.25 1.24 0.91 0.23 5.22 5.12 1.73 0.07 2.16 0.18 0.28 0.01 0.35 0.65 0.12 0.13
57.3% 11.40 45.4% 15.5% 10.24 2.6% 0 -0.47 0.02 0.18 0 0.14 0.15 0 2.28 1.97 0.44 -0.31 0.69 0.08 0.02 0 0.01 0.52 -0.61 0.05
96.5% 14.72 49.4% 28.1% 11.10 8.3% 1 0.96 3.12 4.66 4.69 4.41 2.52 0.63 8.93 8.74 5.08 0.27 3.43 0.68 0.77 0.04 0.81 0.81 1.21 0.24
11.9% 0.92 1.3% 4.2% 0.26 1.8% 0.44 0.45 1.15 1.56 1.66 1.49 0.77 0.21 2.01 2.07 1.71 0.19 0.81 0.22 0.23 0.02 0.26 0.10 0.57 0.06
M
an
us
cr
ip t
Mean
te
Local white percentage Log(local population) Local male fraction Local senior fraction Log(local household income) Local unemployment rate Urban dummy Retail trading volume Amihud illiquidity C^bma (Gibbs estimate) C^roll (Roll’s model) Gamma0 (latent factor model) Gamma1 (latent factor model) Firm leverage Log(book value) Log(market cap) Market-to-book Profitability Log(firm age) Cash holding Tangibility Dividend yield Institutional ownership Institutional turnover Past return Return volatility
Number of Obs.
d
Variables
Panel B: Correlation Matrix of Local Population Characteristics
Ac ce p
This panel presents the correlation matrix of local population characteristics. We report the correlations among local white percentage, log of local population, local male fraction, local senior fraction, log of local household income and local unemployment rate.
Local white percentage Local population Local male fraction Local senior fraction Local household income Local unemployment rate
Local white percentage
Local population
Local male fraction
Local senior fraction
Local household income
Local unemploym ent rate
1 -0.29 0.26 0.09 0.21 -0.30
1 0.05 -0.08 0.00 0.28
1 -0.61 0.52 -0.21
1 -0.46 0.23
1 -0.48
1
Table I (Cont’d) Panel C: Local White percentage by States This panel presents the summary statistics of local white percentage by states. We report the state name, the state code, the mean, the median, the min, the max and the standard deviation, in the descending order of the number of firm-year observations in each state. 34
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Median
Min
Max
Std. Dev.
Num
CA NY TX MA NJ PA IL FL OH MN CT CO GA MI VA MO WA MD WI NC TN OR IN UT OK AZ NV AL LA NH SC KS KY DE AR NE DC RI IA ID ME HI NM MS VT SD AK ND WV MT
0.765 0.784 0.799 0.911 0.830 0.873 0.786 0.860 0.822 0.915 0.894 0.903 0.638 0.839 0.719 0.714 0.860 0.672 0.891 0.765 0.757 0.911 0.863 0.956 0.840 0.937 0.872 0.703 0.646 0.978 0.792 0.924 0.845 0.815 0.899 0.902 0.325 0.938 0.947 0.973 0.984 0.306 0.932 0.594 0.976 0.972 0.837 0.978 0.941 0.976
0.755 0.700 0.777 0.934 0.838 0.914 0.729 0.872 0.811 0.914 0.892 0.904 0.557 0.884 0.783 0.787 0.853 0.759 0.918 0.761 0.771 0.903 0.898 0.954 0.832 0.936 0.867 0.680 0.715 0.980 0.826 0.937 0.835 0.817 0.948 0.893 0.321 0.930 0.942 0.976 0.985 0.283 0.932 0.562 0.978 0.974 0.836 0.980 0.941 0.976
0.562 0.517 0.660 0.678 0.554 0.518 0.692 0.715 0.724 0.868 0.859 0.834 0.443 0.590 0.425 0.502 0.810 0.305 0.768 0.600 0.539 0.861 0.752 0.933 0.793 0.926 0.815 0.556 0.351 0.958 0.558 0.704 0.815 0.773 0.707 0.876 0.313 0.892 0.923 0.957 0.969 0.234 0.903 0.440 0.963 0.958 0.816 0.970 0.939 0.976
0.982 0.985 0.966 0.983 0.985 0.996 0.990 0.980 0.989 0.982 0.984 0.986 0.972 0.987 0.894 0.975 0.966 0.970 0.993 0.927 0.980 0.985 0.989 0.985 0.921 0.947 0.933 0.926 0.903 0.993 0.906 0.968 0.970 0.846 0.985 0.972 0.351 0.987 0.980 0.984 0.993 0.452 0.974 0.848 0.987 0.986 0.852 0.982 0.942 0.976
0.084 0.106 0.057 0.075 0.084 0.130 0.096 0.058 0.069 0.025 0.020 0.053 0.139 0.125 0.147 0.152 0.041 0.191 0.084 0.077 0.130 0.033 0.083 0.014 0.025 0.005 0.034 0.078 0.181 0.007 0.091 0.047 0.037 0.022 0.094 0.027 0.011 0.031 0.016 0.008 0.006 0.073 0.015 0.117 0.007 0.008 0.011 0.004 0.001 0.000
10676 5542 5213 3395 2881 2714 2643 2303 2131 2011 1716 1503 1393 1197 1051 1023 1003 976 808 790 708 635 604 506 504 490 488 359 344 302 286 256 247 241 202 200 194 194 177 127 101 86 85 70 38 27 16 8 5 2
cr
us
an
M
d
ip t
Mean
Ac ce p
California New York Texas Massachusetts New Jersey Pennsylvania Illinois Florida Ohio Minnesota Connecticut Colorado Georgia Michigan Virginia Missouri Washington Maryland Wisconsin North Carolina Tennessee Oregon Indiana Utah Oklahoma Arizona Nevada Alabama Louisiana New Hampshire South Carolina Kansas Kentucky Delaware Arkansas Nebraska District of Columbia Rhode Island Iowa Idaho Maine Hawaii New Mexico Mississippi Vermont South Dakota Alaska North Dakota West Virginia Montana
State Code
te
State Name
Table II Local Retail Investors and the Cross-section of Retail Trading Volume This table explores the impact of local retail investor characteristics on the cross-section of retail trading volume. We measure retail trading volume as the fraction of trading volume associated with retail investors. Because the number of trades by institutions and individuals is not publicly available, following Shu (2006) and Hvidkjaer (2008) we infer the fraction of 35
Page 37 of 53
institutional trading volume from the CDA/Spectrum quarterly institutional holdings data. We first calculate the fraction of institutional trading volume using the following formula: N
InstitutionalFractioni ,t
j 1
i , j ,t
IOi , j ,t 1 | ,
TOi ,t
IOi , j ,t is institution j’s ownership of stock i for quarter t, calculated as institution j’s holdings of stock i divided by i’s
shares outstanding at the end of quarter t.
ip t
where
| IO
TOi ,t is the total turnover of stock i at quarter t. We then calculate the fraction of
retail trading volume as:
cr
RetailFractioni ,t 1 InstitutionalFractioni ,t .
an
us
The dependent variable is the yearly average of retail fraction. Columns (1) and (2) are based on the full sample. Column (1) is the base-line specification. We control for state fixed effects, county-level characteristics, such as population, gender, age, household income, unemployment rate and an urban dummy, as well as firm specific variables including leverage, market-to-book, market capitalization, profitability, firm age, cash holding, tangibility and dividend yield. We further control for institutional ownership, institutional turnover, past return and return volatility. All variables on the right-hand side are lagged values. From column (2) we include industry fixed effects at two-digit SIC level and cluster the standard errors at firm level.
Ac ce p
te
d
M
Columns (3)-(6) are based on sub-samples where retail trading are more likely to be evident. Column (3) is for the sub-sample where the firm’s book asset is below the sample median. Column (4) is for the sub-sample where the firm’s institutional ownership is below the sample median. Column (5) is for the sub-sample where the maximum price in the previous year is below the sample median. Column (6) is for the sub-sample where the volatility of local household income is above the sample median. The detailed definitions of each variable can be found in the Appendix. ***, ** and * represent significance levels at 1%, 5% and 10% respectively using robust standard errors with t-statistics given in parentheses.
Table II (Cont’d) Full sample
Sub-sample Low institutional
High local household income
36
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(2)
Small size (3)
ownership (4)
Low price (5)
volatility (6)
Local white percentage
0.130*** (5.28)
0.140*** (3.32)
0.153*** (3.06)
0.206*** (3.94)
0.161*** (3.26)
0.267*** (4.57)
Log(local population)
-0.002 (-0.92) -0.370 (-1.21) -0.248*** (-2.99) 0.026* (1.81) -0.006*** (-3.17) 0.002 (0.23) -0.032*** (-3.38) 0.021*** (19.03) -0.013*** (-9.90) 0.006 (0.65) -0.009*** (-3.91) 0.063*** (6.56) -0.022** (-2.50) -1.068*** (-8.46) -0.519*** (-55.59) 0.102*** (4.67) -0.036*** (-11.71) 1.635*** (43.52) 0.484** (2.46) Y Y 0.2891 58471
-0.003 (-0.68) -0.451 (-0.85) -0.248* (-1.77) 0.002 (0.10) -0.006** (-2.07) 0.006 (0.55) -0.016 (-1.05) 0.019*** (12.02) -0.014*** (-5.95) 0.001 (0.04) -0.007* (-1.89) 0.054*** (3.56) -0.064*** (-3.37) -1.078*** (-7.29) -0.515*** (-31.65) 0.092*** (3.49) -0.032*** (-10.22) 1.486*** (30.20) 0.847** (2.27) Y Y Y Firm 0.3052 58471
0.003 (0.50) -1.028 (-1.50) -0.303* (-1.76) 0.000 (-0.02) -0.006* (-1.68) -0.004 (-0.28) -0.046** (-2.31) 0.017*** (9.33) -0.029*** (-6.38) 0.011 (0.81) -0.019*** (-3.53) 0.044*** (2.71) -0.048** (-2.06) -0.808*** (-4.03) -0.762*** (-32.24) 0.056* (1.94) -0.016*** (-4.24) 1.105*** (19.13) 1.098** (2.39) Y Y Y Firm 0.2284 29221
0.003 (0.53) -0.319 (-0.46) -0.041 (-0.24) -0.003 (-0.11) -0.003 (-0.75) 0.004 (0.27) -0.060*** (-3.16) 0.021*** (11.50) -0.048*** (-13.14) 0.001 (0.07) -0.014*** (-2.67) 0.076*** (4.49) -0.015 (-0.64) -0.818*** (-4.59) -1.008*** (-22.65) 0.072** (2.48) -0.017*** (-4.13) 1.170*** (19.41) 0.696 (1.43) Y Y Y Firm 0.2426 29173
-0.001 (-0.17) -0.992 (-1.52) -0.193 (-1.16) 0.034 (1.14) -0.004 (-1.10) 0.023* (1.75) -0.064*** (-3.65) 0.018*** (9.06) -0.019*** (-4.91) 0.010 (0.69) 0.002 (0.31) 0.064*** (3.83) -0.020 (-0.90) -0.571*** (-3.72) -0.729*** (-31.56) 0.039 (1.30) -0.033*** (-7.44) 1.151*** (20.20) 0.658 (1.47) Y Y Y Firm 0.2389 29159
0.014 (1.65) 0.356 (0.50) -0.289 (-1.42) 0.016 (0.46) -0.007* (-1.83) 0.014 (0.83) -0.041** (-1.96) 0.018*** (9.35) -0.016*** (-5.00) -0.004 (-0.26) -0.001 (-0.24) 0.040** (2.13) -0.071*** (-2.69) -1.021*** (-4.94) -0.489*** (-22.19) 0.105*** (3.11) -0.033*** (-7.82) 1.315*** (20.05) 0.164 (0.33) Y Y Y Firm 0.2995 29195
Firm leverage Market-to-book Log(market cap) Profitability Log(firm age) Cash holding Tangibility Dividend yield Institutional ownership
Past return
Ac ce p
Institutional turnover
Return volatility Const
Year-fixed effects State-fixed effects Industry-fixed effects Clustering Adjusted R-squared Number of Obs.
cr
us
Urban dummy
an
Local unemployment rate
M
Log(local household income)
d
Local senior fraction
te
Local male fraction
ip t
(1)
Table III Local Equity Market Participation and Stock Illiquidity: Main Results This table presents our main results of the impact of local equity market participation on stock liquidity. The dependent variable is the Amihud illiquidity measure. It is defined as the square root of the average daily price impact of trading in each year. Column (1) is the base-line specification. In column (2) we add more controls on local characteristics including local male 37
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fraction, local senior fraction, local household income, local unemployment rate and an urban dummy. From column (3) we cluster the standard errors at firm level. We add more firm level controls in column (4). In column (5) we add state fixed effects. In column (6) we further include industry fixed effects (two-digit sic) and credit rating fixed effects (investment grade/junk/non-rated). ***, ** and * represent significance levels at 1%, 5% and 10% respectively using robust standard errors with t-statistics given in parentheses. (4) -0.304*** (-3.69)
-0.007* (-1.74)
-0.009** (-2.22) -0.822* (-1.91) 0.020 (0.15) 0.071*** (3.02) 0.011*** (3.43) -0.012 (-1.18) 0.272*** (12.81) -0.009*** (-4.81) -0.366*** (-112.41) -0.125*** (-5.45) 0.066*** (14.48) -0.339*** (-16.14)
-0.009 (-1.08) -0.822 (-0.89) 0.020 (0.07) 0.071 (1.50) 0.011* (1.87) -0.012 (-0.59) 0.272*** (7.22) -0.009** (-2.53) -0.366*** (-50.49) -0.125*** (-3.48) 0.066*** (7.19) -0.339*** (-9.49)
-0.007 (-0.83) -0.570 (-0.63) -0.005 (-0.02) 0.064 (1.38) 0.010* (1.69) -0.006 (-0.30) 0.283*** (7.57) 0.002 (0.60) -0.331*** (-42.33) 0.044 (1.23) 0.059*** (6.41) -0.311*** (-8.53) -0.072** (-2.01) -0.338 (-1.02) -0.375*** (-11.56) -0.755*** (-9.77) -0.162*** (-21.27) -0.132 (-1.08) Y Y Firm 0.4680 58471
Local male fraction Local senior fraction Log(local household income) Local unemployment rate
0.271*** (12.80) -0.009*** (-4.84) -0.366*** (-112.90) -0.127*** (-5.55) 0.067*** (14.74) -0.336*** (-16.18)
Market-to-book Log(market cap) Profitability Log(firm age)
te
Cash holding
d
Firm leverage
M
Urban dummy
Tangibility
Ac ce p
Dividend yield Institutional ownership Institutional turnover Past return
Return volatility
Exchange dummies Year-fixed effects State-fixed effects Industry-fixed effects Credit rating-fixed effects Clustering Adjusted R-squared Number of Obs.
Y Y 0.4547 58471
Y Y 0.4548 58471
Y Y Firm 0.4548 58471
(5) -0.620*** (-5.72)
(6) -0.624*** (-5.95)
-0.011 (-0.96) 1.278 (0.92) 0.124 (0.33) 0.077 (1.27) 0.003 (0.47) -0.061** (-2.27) 0.279*** (7.44) 0.003 (0.84) -0.332*** (-42.74) 0.045 (1.25) 0.058*** (6.24) -0.308*** (-8.33) -0.073** (-2.00) -0.320 (-0.98) -0.359*** (-11.11) -0.758*** (-9.82) -0.162*** (-21.27) -0.121 (-0.99) Y Y Y Firm 0.4699 58471
-0.011 (-1.04) 1.466 (1.11) 0.163 (0.45) 0.118** (2.02) 0.004 (0.64) -0.084*** (-3.24) 0.276*** (6.78) 0.021*** (5.45) -0.393*** (-46.53) 0.136*** (3.70) 0.040*** (4.48) -0.243*** (-6.54) -0.034 (-0.76) -0.715** (-2.02) -0.252*** (-7.77) -0.703*** (-9.43) -0.146*** (-20.06) 0.125 (1.03) Y Y Y Y Y Firm 0.4903 58471
ip t
(3) -0.321*** (-3.82)
cr
(2) -0.321*** (-8.37)
us
Controls Log(local population)
(1) -0.348*** (-11.23)
an
Local white percentage
Table IV Local Equity Market Participation and Stock Illiquidity: Robustness Checks This table provides robustness checks on the relationship between local equity market participation and stock illiquidity. Panel A is for sub-sample analysis by county population and sample period. In Panel B we employ different measures of stock illiquidity. In Panel C we use alternative measures on local equity market participation. Panel D presents the results of between-effects and 38
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firm fixed effects regressions. In Panel E we separate the sample by firm age and examine stocks never moving headquarters during the sample period. We always utilize the “Full specification” as in column (6) of Table III and cluster the standard errors at firm level. For brevity we do not report the control variables.
Panel A: Sample Separation by County Population and Sample Period
Y Firm 29478
cr
Y Firm 28993
Y Firm 28343
Y Firm 30128
an
Full Specification Clustering Number of Obs.
Separate by sample period before 1998 after 1998 (3) (4) -0.742*** -0.452*** (-4.31) (-4.17)
us
Local white percentage
Separate by local population above median blow median (1) (2) -0.678*** -0.566*** (-3.45) (-3.92)
ip t
In this panel we separate the sample by local population and sample period. In columns (1) and (2) we separate the sample by the size of local population (above/below median). In columns (3) and (4) we analyze the subsamples by sample period (before 1998/after 1998).
Panel B: Other Illiquidity Measures
M
In this panel we use different measures of stock illiquidity as dependent variables. We employ four measures from column (1) to column (4). C^bma is the coefficient on the change in the trade direction indicator in the basic market-adjusted model. C^roll is the Roll (1984) trading cost estimator excluding quote mid-points. Gamma0 and Gamma1 are the intercept and slope coefficient in a latent common factor model to estimate the effective costs of trading. We obtain these measures from Joel Hasbrouck’s website. Gamma1 (4) -0.182*** (-3.65)
Y Firm 58471
Y Firm 58471
Y Firm 58471
Y Firm 58471
d
Gamma0 (3) -0.583*** (-4.61)
Ac ce p
Full Specification Clustering Number of Obs.
C^roll (2) -0.515*** (-3.83)
te
Local white percentage
C^bma (1) -0.651*** (-4.95)
Table IV (Cont’d) Panel C: Alternative Measures on Local Equity Market Participation In this panel we use alternative measures on local equity market participation. In columns (1) and (2), local white percentage is defined as log(local white population*local average income/total local book assets). In columns (3) and (4), local white 39
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percentage is defined as log(local white population*local average income/total local market assets). The dependent variable in column (1) and (3) is the Amihud illiquidity measure. The dependent variable in column (3) and (4) is the coefficient on the change in the trade direction indicator in the basic market-adjusted model C^bma.
Y Firm 58471
Y Firm 58471
Y Firm 58471
Y Firm 58471
cr
Full Specification Clustering Number of Obs.
Normalized by market assets Amihud Illiquidity C^bma (3) (4) -0.033*** -0.036*** (-6.05) (-5.42)
ip t
Local white percentage
Normalized by book assets Amihud Illiquidity C^bma (1) (2) -0.028*** -0.031*** (-5.00) (-4.50)
us
Panel D: Between-Effects and Firm Fixed-effects Regression
firm-fixed effects (4) -0.452** (-2.59)
Y 58471
Y 58471
Y Firm 58471
M
Y 58471
population average (3) -0.498*** (-6.28)
te
Full Specification Clustering Number of Obs.
between effects: wls (2) -0.627*** (-5.76)
d
Local white percentage
between effects (1) -0.709*** (-5.51)
an
This panel performs between effects and firm fixed effects regression of stock illiquidity on local equity market participation. The dependent variable is Amihud illiquidity. Column (1) fits random-effects models by using the between regression estimator. Given the unbalanced panel, column (2) performs between effects estimation with weighted least squares rather than the default OLS. Column (3) fits random-effects models using the population-averaged estimator. In column (4) we run OLS regressions with firm fixed effects and standard errors clustered at firm level.
Panel E: Sample Separation by Firm Age and Firms Never Moving Headquarters
Ac ce p
In this panel, column (1) is for the sub-sample with firm age more than 10 years. Column (2) is for the sub-sample with firm age less than 10 years. Columns (3) and (4) are based on the firms that never move headquarters during the sample period. The dependent variable in columns (1)-(3) is the Amihud illiquidity measure while in column (4) it is the Gibbs estimate of C^bma.
Local white percentage Full Specification Clustering Number of Obs.
Separate by firm age more than less than 10 years 10 years (1) (2) -0.683*** -0.510*** (-4.29) (-4.35) Y Firm 24166
Y Firm 34305
Firms never moving headquarters Amihud illiquidity C^bma (3) -0.731 *** (-6.11)
(4) -0.754*** (-5.09)
Y Firm 47145
Y Firm 47145
Table V Local Equity Market Participation and Stock Illiquidity: Sub-sample Comparisons In this table we compare the effects of local equity market participation on stock illiquidity in different sub-samples. We separate the sample by book size, institutional ownership, price, P/E ratio, analyst coverage, return volatility, the “only-game-in-town” effect and local household income volatility. We always utilize the “full specification” as in column (6) of Table III and cluster the standard errors at firm level. For brevity we do not report the control variables. We perform Chi-squared 40
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test to evaluate the differences in coefficients between sub-samples. ***, ** and * represent significance levels at 1%, 5% and 10% respectively.
Panel A: Separate by Size and Institutional Ownership In Panel A, we separate the full sample by the size of book assets (columns (1) and (2), above median/below median), and the level of institutional ownership (columns (3) and (4), above median/below median).
Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
Y Firm 29236
Y Firm 29235
ip t
Y Firm 29236
us
Local white percentage
Separate by institutional ownership above median below median (3) (4) -0.116** -0.797*** (-2.35) (-4.89)
cr
Separate by book size above median below median (1) (2) -0.189*** -0.665*** (-3.89) (-4.56)
9.62***
Y Firm 29235
16.57***
an
Panel B: Separate by Price and P/E-ratio
M
In Panel B, we separate the full sample by the maximum of stock price in the previous year (columns (1) and (2), above median/below median), and the price earnings ratio (columns (3) and (4), above median/below median).
Firm Y 29240
Firm Y 29231
Separate by P/E above median below median (3) (4) -0.387*** -0.759*** (-4.39) (-5.46) Firm Y 29032
18.21***
Firm Y 29174 7.76***
Ac ce p
Clustering Full Specifications Number of Obs. Chi-square test: difference in coefficients
te
Local white percentage
d
Separate by Price above median below median (1) (2) -0.059* -0.713*** (-1.73) (-4.67)
Table V (Cont’d) Panel C: Separate by Analyst Coverage and Return Volatility In Panel C, we separate the full sample by the number of analyst coverage (columns (1) and (2), above 1/below 1), and return volatility (columns (3) and (4), above median/below median). 41
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Separate by Analyst Coverage more than 1 less than 1 (1) (2)
Clustering Full Specifications Number of Obs. Chi-square test: difference in coefficients
-0.112*** (-2.60)
-0.780*** (-5.08)
-0.740*** (-5.67)
-0.480*** (-4.13)
Firm Y 35912
Firm Y 22559
Firm Y 29236
Firm Y 29235
18.44***
ip t
Local white percentage
Separate by Return Volatility above median below median (3) (4)
3.32*
cr
Panel D: Separate by Only-game-in-town Effects and Local Household Income Volatility
us
In Panel D, we separate the full sample by the only-game-in-town effects (columns (1) and (2), above median/below median), and the local household income volatility (column (3) and (4), above median/below median). The only-game-in-town effect is measured as log(county population*average income/county book assets). Local household income volatility is calculated as the volatility of the median county household income during the sample period.
d
Firm Y 29212
4.16**
Separate by Local Household Income Volatility above median below median (3) (4)
-0.186 (-1.27)
-0.766*** (-5.22)
-0.160 (-0.66)
Firm Y 29193
Firm Y 29214
Firm Y 29171 4.64**
Ac ce p
te
Clustering Full Specifications Number of Obs. Chi-square test: difference in coefficients
-0.627*** (-3.64)
M
Local white percentage
an
Separate by Only-game-in-town Effects above median below median (1) (2)
Table VI Local Equity Market Participation and Stock Illiquidity: Firms Moving Headquarters In this table we analyze the change of stock illiquidity and the change of local equity market participation for the firms that re-allocated headquarters during the sample period. Historical firm locations come from Compact Disclosure. We denote a firm as a mover if the location of headquarter in year t is in different counties from its location in year t-1. The dependent variable is the difference of stock illiquidity between year t+1 and year t-1. The right-hand side variables are also changes from year t-1 to 42
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year t+1. The dependent variable in column (1) and (2) is Amihud illiquidity, while it is C^bma, C^Roll, Gamma0 and Gamma1 respectively from column (3) to column (6). Year, state and industry fixed effects are always included. ***, ** and * represent significance levels at 1%, 5% and 10% respectively using robust standard errors with t-statistics given in parentheses. Change in Stock Illiquidity
Amihud Illiquidity (1) (2)
C^bma (3)
C^Roll (4)
Gamma0 (5)
Gamma1 (6)
-0.909*** (-2.79)
-0.970*** (-2.91)
-1.186*** (-2.67)
-1.161** (-2.13)
-0.749 (-1.58)
-0.772* (-1.79)
Change in Log(local population)
-0.065** (-1.96) 0.165 (0.05) -1.095 (-1.01) 0.229 (1.08) 0.025 (0.89) -0.183 (-1.61) 0.226 (1.01) -0.465*** (-8.87) 0.088*** (3.63) 0.317** (2.25) 0.002 (0.01)
-0.080** (-2.43) 2.659 (0.84) -0.904 (-0.82) 0.227 (1.08) 0.025 (0.89) -0.181* (-1.72) 0.205 (0.92) -0.500*** (-9.20) 0.064** (2.36) 0.340** (2.42) 0.111 (0.48) 0.771* (1.92) -6.619*** (-3.08) 0.378 (1.56) -0.321 (-0.89) 0.142*** (2.67) 1.212* (1.80) 1.443*** (3.54) Y Y 0.4552 878
-0.077* (-1.81) 5.012 (1.06) -0.622 (-0.40) -0.080 (-0.28) 0.011 (0.27) -0.326* (-1.87) 0.718*** (2.73) -0.563*** (-7.58) 0.023 (0.76) 0.105 (0.53) -0.042 (-0.14) 0.308 (0.50) -8.956*** (-2.97) 0.221 (0.66) -0.591 (-1.27) 0.158** (2.50) 2.100** (2.30) 0.333 (0.74) Y Y 0.4254 878
-0.155*** (-2.76) 0.042 (0.12) -0.873 (-0.15) 1.165 (0.58) 0.045 (0.91) -0.220 (-0.99) 0.441 (1.42) -0.632*** (-6.77) 0.030 (0.87) -0.124 (-0.49) -0.354 (-0.86) -0.031 (-0.05) -6.511*** (-2.92) 0.219 (0.50) -0.687 (-1.21) 0.236** (2.59) 1.084 (1.04) 1.486*** (2.66) Y Y 0.3834 878
-0.056 (-1.26) 2.002 (0.40) -2.132 (-1.35) -0.224 (-0.79) -0.004 (-0.09) -0.252 (-1.41) 0.659** (2.41) -0.559*** (-7.78) 0.054 (1.48) 0.077 (0.37) 0.036 (0.11) 0.763 (1.30) -9.304*** (-3.33) 0.524 (1.65) -0.454 (-0.99) 0.119* (1.88) 2.564*** (2.87) 0.840* (1.77) Y Y 0.4093 878
-0.051 (-1.19) 5.635 (1.28) 1.449 (1.29) 0.203 (0.87) 0.029 (0.90) -0.093 (-0.74) -0.036 (-0.17) -0.038 (-0.67) -0.024 (-1.04) -0.308 (-1.36) -0.125 (-0.38) -1.134*** (-2.78) -1.387 (-0.93) -0.503** (-1.97) -0.166 (-0.53) 0.007 (0.11) 1.441** (2.02) -0.872** (-2.27) Y Y 0.2636 878
Change in firm leverage Change in Log(market cap) Change in market-to-book Change in profitability Change in cash holding Change in tangibility Change in dividend yield
Ac ce p
Change in institutional ownership Change in institutional turnover Change in yearly return
Change in return volatility Const
Exchange Dummies Year & State & Industry-fixed Effects Adj. R-squared Number of Obs.
0.537 (1.60) Y Y 0.4203 893
cr
us
Change in urban dummy
an
Change in local unemployment rate
M
Change in Log(local household income)
d
Change in local senior fraction
te
Change in local male fraction
ip t
Change in local white percentage
Table VII Local Equity Market Participation and Stock Illiquidity: Additional Evidence This table provides additional evidence on the effect of local equity market participation on stock illiquidity. We relate local education level and local white percentage with stock illiquidity. Panel A runs county-level regressions of local education level on local population characteristics. We measure local education level as the percentage of population completing college in each county. The data on county education level come from the Economic Research Service in the US Department of Agriculture. 43
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In Panel B, we examine the effects of local education level and local white percentage on stock illiquidity. The dependent variables in columns (1) and (2) are the Amihud illiquidity measure. The dependent variables in columns (3) and (4) are the Gibbs estimate of trading costs C^bma. In columns (2) and (4) we include both local educational level and local white percentage in the regression. We always utilize the “Full specification” as in column (6) of Table III and cluster the standard errors at firm level. For brevity we do not report the control variables.
ip t
In Panel C, we perform the analyses at the county level. Specifically, for each county, we calculate the average illiquidity and average firm characteristics across all the firms headquartered in the county. There are 5399 county-year observations in the regressions. The dependent variable is the average firm illiquidity in the county. ***, ** and * represent significance levels at 1%, 5% and 10% respectively using robust standard errors with t-statistics given in parentheses.
(2)
0.044*** (8.78)
0.049*** (10.03)
Log(local population)
Local senior fraction
Local unemployment rate
te
d
0.234*** (54.85) 0.0147 5382
M
Log(local household income)
Year fixed effects State-fixed effects R-squared Number of Obs.
(4)
0.072*** (14.99)
0.071*** (9.90) -0.003*** (-5.23) -0.444*** (-6.05) -0.126*** (-8.17) 0.013*** (4.17) -0.001*** (-4.03) 0.399*** (9.07) Y Y 0.6964 5382
an
Local male fraction
Const
(3)
us
Local white percentage
(1)
cr
Panel A: Local Education Level and Local Population Characteristics
0.241*** (51.40) Y 0.0564 5382
0.252*** (51.36) Y Y 0.6801 5382
Ac ce p
Panel B: Local Education Level and Stock Illiquidity
Local education level
Amihud Illiquidity (2)
(3)
(4)
-1.085*** (-3.72)
-0.342 (-1.07) -0.570*** (-4.95)
-0.829** (-2.22)
0.033 (0.08) -0.661*** (-4.40)
Y Firm 58277
Y Firm 58277
Y Firm 58277
Y Firm 58277
Local white percentage Full Specifications Clustering Number of Obs.
C^bma
(1)
Table VII (Cont’d) Panel C: County-Level Analysis
Local white percentage
(1)
(2)
(3)
(4)
-0.208*** (-3.79)
-0.340*** (-4.10)
-0.360*** (-3.30)
-0.334*** (-3.12)
44
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0.005 (0.44)
-0.001 (-0.10) -2.122* (-1.69) 0.755** (2.21) 0.083 (1.25) -0.015** (-2.23) -0.032 (-1.17)
0.004 (0.35) -2.003 (-1.63) 0.699** (2.08) 0.067 (0.99) -0.016** (-2.36) -0.021 (-0.81)
0.434*** (4.08) 0.003 (0.32) -0.397*** (-34.06) 0.113 (0.96) 0.054*** (3.33) -0.354*** (-3.23)
0.445*** (4.08) 0.007 (0.69) -0.403*** (-32.38) 0.073 (0.62) 0.040** (2.25) -0.365*** (-3.31)
0.430*** (3.97) 0.008 (0.75) -0.406*** (-32.28) 0.096 (0.80) 0.044** (2.43) -0.374*** (-3.37)
0.388*** (3.58) 0.024* (1.95) -0.369*** (-26.73) 0.371*** (2.97) 0.054*** (2.83) -0.370*** (-3.41) 0.031 (0.36) -1.922* (-1.65) -0.235*** (-3.40) -0.538** (-2.29) -0.286*** (-9.41) 0.645 (1.55) Y Y 0.554 5399
Local male fraction Local senior fraction Log(local household income) Local unemployment rate
Market-to-book Log(market cap) Profitability Log(firm age) Cash holding
an
Average firm characteristic Firm leverage
us
Urban dummy
M
Tangibility Dividend yield
Return volatility
Ac ce p
Year-fixed effects State-fixed effects Adjusted R-squared Number of Obs.
te
Past return
d
Institutional ownership Institutional turnover
ip t
0.007 (0.79)
cr
County characteristics Log(local population)
Y 0.517 5399
Y Y 0.536 5399
Y Y 0.538 5399
Table VIII Local Equity Market Participation and Stock Illiquidity: Retail Investor Channel In this table, we strengthen the argument that local equity market participation affects stock liquidity through the retail investor channel. Specifically, we use an instrumental variable regression approach to link stock illiquidity with the measure of retail trading fraction as constructed in Table II, where we instrument the retail trading fraction by the local white percentage. We present the second-stage regression results. In columns (1)-(3), we report the results for the subsample of firms with high retail concentration, such as small firms, firms with low institutional ownership and stocks with low price. In columns (4)-(6), we 45
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report the results for the subsample of firms with low retail concentration, such as large firms, firms with high institutional ownership and stocks with high price. The procedure for the sample splits is the same as before. All specifications include year, industry, exchange and credit rating fixed effects.
Log(local household income) Local unemployment rate Urban dummy Firm leverage Market-to-book Log(market cap) Profitability Log(firm age)
te
Cash holding
Ac ce p
Tangibility Dividend yield
Institutional ownership Institutional turnover Past return
Return volatility
Full Specifications Clustering F-statistics (First-stage) Number of Obs.
ip t -0.001 (-0.07) -0.010 (-0.19) -0.015 (-0.98) -0.661 (-0.65) -0.662 (-1.24) 0.017 (0.70) 0.048 (0.92) 0.021*** (4.35) -0.097** (-2.36) -0.189*** (-4.10) 0.023 (1.58) -0.091* (-1.84) -0.074 (-1.16) -0.975 (-1.48) -0.497* (-1.87) 0.492 (0.75) -0.104*** (-2.81) 1.472 (1.04) Y Firm 2.97 29,298
cr
-0.000 (-0.02) 0.139 (0.98) -0.012 (-0.75) -0.090 (-0.03) -0.239 (-0.33) -0.027 (-0.43) 0.085 (0.78) 0.072*** (2.60) -0.750*** (-26.17) 0.093 (1.32) 0.077** (2.35) 0.060 (0.66) -0.068 (-0.62) -1.281 (-1.29) -3.383*** (-3.16) -0.346** (-2.47) -0.321*** (-5.82) 5.043*** (3.25) Y Firm 10.87 29,140
us
Local senior fraction
-0.018 (-0.53) -0.038 (-0.37) -0.032 (-0.97) -1.086 (-0.52) -0.677 (-1.02) 0.003 (0.06) 0.369* (1.87) 0.063** (1.98) -0.012 (-0.10) -0.340 (-1.29) 0.067 (1.02) -0.075 (-0.64) -0.152 (-1.02) -2.642 (-1.25) -1.335 (-1.21) 1.267 (0.67) -0.210 (-1.34) 5.038 (0.96) Y Firm 1.01 29,250
0.011 (0.44) -0.006 (-0.05) -0.010 (-0.76) 1.939 (0.68) 0.323 (0.46) -0.115** (-2.03) 0.179** (2.04) 0.092*** (4.29) -0.739*** (-16.58) 0.122* (1.95) 0.010 (0.47) 0.042 (0.46) -0.037 (-0.38) -1.628 (-1.48) -5.133*** (-4.61) -0.154 (-1.14) -0.271*** (-9.59) 4.518*** (3.80) Y Firm 14.80 29,173
an
Local male fraction
0.005 (0.18) -0.019 (-0.14) -0.023 (-1.31) -0.429 (-0.15) -0.558 (-0.74) -0.127* (-1.91) 0.240** (2.49) 0.177*** (6.50) -0.961*** (-21.21) 0.371*** (5.48) -0.021 (-0.86) 0.122 (1.43) -0.262* (-1.84) -2.439 (-1.64) -3.135*** (-2.63) -0.147 (-0.99) -0.197*** (-5.87) 4.996*** (3.11) Y Firm 8.94 29,221
M
Controls Log(local population)
Low Retail Concentration Large High Inst. High Size Ownership Price (4) (5) (6) -3.398 -1.429 -0.546 (-0.98) (-1.38) (-1.49)
d
Retail trading fraction (instrumented by local white percentage)
High Retail Concentration Small Low Inst. Low Size Ownership Price (1) (2) (3) -4.532*** -3.981*** -4.469*** (-2.96) (-3.70) (-3.09)
0.006 (0.97) -0.007 (-0.28) -0.002 (-0.47) -0.395 (-0.87) -0.137 (-0.91) -0.011 (-0.99) 0.019 (0.93) 0.004 (1.09) -0.066*** (-6.54) 0.030 (1.51) 0.014*** (4.12) 0.006 (0.24) -0.003 (-0.14) -0.517 (-1.38) -0.370*** (-2.92) -0.116 (-0.66) -0.015*** (-3.92) 0.039 (0.07) Y Firm 4.98 29,331
Table IX Market Microstructure Explanations: Link to Admati and Pfleiderer (1988) This table aims to offer market microstructure explanations on the effects of local equity market participation on stock illiquidity. In particular we link our results to Admati and Pfleiderer (1988) by separating the sample by the number of local institutional investors. Since CDA/Spectrum 13F does not provide the locations of institutional investors, we collect the zip-codes of the locations of money managers from their SEC filings from Morningstar Document Research. The corresponding values of latitude and longitude are obtained from the Gazetteer Files of Census 2000. For each firm-year, we count the number of 46
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institutional investors which are located within a 250-mile radius around the firm’s headquarter. In Panels A, C and D, the dependent variable in columns (1) and (2) is the Amihud illiquidity. The dependent variable in column (3) and column (4) is the Gibbs estimate of trading costs C^bma. We always utilize the “Full specification” as in column (6) of Table III and cluster the standard errors at firm level. For brevity we do not report the control variables. We perform Chi-squared test to evaluate the differences in coefficients between sub-samples. ***, ** and * represent significance levels at 1%, 5% and 10% respectively.
ip t
Panel A: Separate by the Number of Local Institutional Investors: Overall
below median (4)
-0.425** (-2.58) Y Firm 27494
-0.974*** (-4.38) Y Firm 30380
-0.289 (-1.45) Y Firm 27494
3.64**
5.66**
M
Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
-0.857*** (-5.07) Y Firm 30380
an
Local white percentage
C^bma
above median (3)
us
Amihud Illiquidity above median below median (1) (2)
cr
In Panel A, we separate the full sample by the number of local institutional investors (above median/below median), where we include all local institutional investors with different investment and trading styles.
Panel B: Correlations with Separation Indicator Based on Firm Characteristics
Indicator by the number of local institutional investors of different styles (above median 1, below median 0) Investment style: Investment style: Trading style: Trading style: Small Large Transient Non-transient 0.01 0.04 -0.06 0.00 0.03 0.04 -0.02 0.01 0.04 0.05 -0.02 0.00 -0.01 0.02 -0.04 0.01 -0.01 0.00 -0.03 -0.03 -0.01 -0.07 0.06 -0.04 -0.11 -0.02 0.07 -0.12 0.07 -0.15 0.07 -0.04
Ac ce p
Indicator by firm characteristics (above median 1, below median 0)
te
d
Panel B reports the correlations between indicators created by the number of local institutional investors and indicators created based on firm characteristics. We distinguish institutional investors by their investment (small/large) and trading styles (transient/non-transient). The style classifications are obtained from Brian Bushee’s website. For example, the indicator (size) is 1 if book size is above sample median and 0 otherwise. The indicator (investment style: small) is 1 if the number of local small-style institutional investors is above sample median and 0 otherwise. Other indicator variables are similarly defined.
Size Institutional ownership Price P/E Analyst coverage Return volatility Only-game-in-town Local household income volatility
Table IX (Cont’d) Panel C: Sample Separation by Local Institutional Investors: Investment Styles In Panel C, we separate the full sample by the number of local institutional investors (above median/below median) based on their investment styles (small/large). Panel B1 is based on the number of local institutional investors with investment style toward small stocks. Panel B2 is based on the number of local institutional investors with investment style toward large stocks. The style classifications are obtained from Brian Bushee’s website. 47
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Panel C1: Separate by the Number of Local Institutional Investors: (Investment Style: Small)
Full Specifications Number of Obs. Chi-square test: difference in coefficients
C^bma above median (3) -1.038*** (-4.70) Y 29758
below median (4) -0.162 (-0.68) Y 28028 7.20***
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Local white percentage
Amihud Illiquidity above median below median (1) (2) -0.927*** -0.349* (-5.49) (-1.88) Y Y 29758 28028 5.32**
Panel C2: Separate by the Number of Local Institutional Investors: (Investment Style: Large)
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below median (4) -0.504*** (-2.76) Y 26565
0.26
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Full Specifications Number of Obs. Chi-square test: difference in coefficients
C^bma
above median (3) -0.638*** (-3.26) Y 31143
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Local white percentage
Amihud Illiquidity above median below median (1) (2) -0.723*** -0.528*** (-4.81) (-3.53) Y Y 31143 26565 0.90
Panel D: Sample Separation by Local Institutional Investors: Trading Styles
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In Panel C, we separate the full sample by the number of local institutional investors (above median/below median) based on their trading styles (transient/non-transient). Panel C1 is based on the number of local transient institutional investors. Panel C2 is based on the number of local non-transient institutional investors. The style classifications are from Brian Bushee’s website.
Panel D1: Separate by the Number of Local Institutional Investors: (Trading Style: Transient)
Local white percentage
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Full Specifications Number of Obs. Chi-square test: difference in coefficients
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Amihud Illiquidity above median below median (1) (2) -0.757*** -0.242 (-5.77) (-1.24) Y Y 31376 26212 4.83**
C^bma above median (3) -0.745*** (-4.38) Y 30380
below median (4) -0.289 (-1.45) Y 27494 5.66**
Panel D2: Separate by the Number of Local Institutional Investors: (Trading Style: Non-transient)
Local white percentage
Full Specifications Number of Obs. Chi-square test: difference in coefficients
Amihud Illiquidity above median below median (1) (2) -0.818*** -0.548*** (-4.78) (-3.36) Y Y 30397 27477 1.39
C^bma above median (3) -0.932*** (-4.11) Y 30380
below median (4) -0.428** (-2.19) Y 27494 3.02*
Table X Local Equity Market Participation, Stock Illiquidity and Firm Value This table presents the results of firm value on local equity market participation and stock illiquidity. The dependent variable is firm’s market-to-book ratio. Panel A is our main specification. Columns (1)-(3) are OLS regressions using the same set of control variable as in Table 48
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III. We control for local characteristics, such as population, household income, unemployment rate, gender, age and an urban dummy, as well as firm specific variables including leverage, market size, profitability, firm age, cash holding, tangibility, dividend yield, institutional ownership, stock turnover, past return and return volatility. All right-hand side variables are lagged values. Columns (4)-(6) are instrumental variables (IV) regressions. In columns (4) and (5) we instrument stock illiquidity with local white percentage. In column (6) we use both local white percentage and local education level as instruments. We report the
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F-statistic of the weak-identification test to test the presence of weak instrument and the Hansen J statistic (p-value) to justify the validity of instruments. Panel B is for robustness checks. We run between-effects and firm fixed effects regression of firm value on local equity market participation. Column (1) fits random-effects models by using the between regression estimator. Given the unbalanced
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panel, column (2) performs between effects estimation with weighted least squares rather than the default OLS. Column (3) fits
the sake of brevity we don’t report the control variables in the regression.
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random effects models using the population-averaged estimator. In column (4) we run OLS regressions with firm fixed effects and standard errors clustered at firm level. Here the “Full Specification” refers to the specification as in Panel A column (3). For Panel C to Panel E compare the effects in different sub-samples. In Panel C we separate the full sample by the size of
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book assets (columns (1) and (2)), and the level of institutional ownership (columns (3) and (4)). Panel D separates the sample by the number of local institutional investors and by their investment styles. Column (1) and (2) are based on the number of local small-style institutional investors. Columns (3) and (4) are based on the number of local large-style institutional investors.
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Panel E separates the sample by the number of local institutional investors and by their trading styles. Column (1) and (2) are based on the number of local transient institutional investors. Columns (3) and (4) are based on the number of local non-transient institutional investors. We perform Chi-squared test to evaluate the differences in coefficients between sub-samples. Standard errors are clustered at firm level in all specifications. ***, ** and * represent significance levels at 1%,
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5% and 10% respectively.
Table X (Cont’d) Panel A: Main Specification (1)
OLS (2)
(3)
(4)
IV Regression (5)
(6)
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0.716*** (3.91)
0.681*** (4.05)
Amihud illiquidity
Local senior fraction Log(local household income) Local unemployment rate Urban dummy Firm leverage Log(market cap) Profitability Log(firm age) Cash holding
-0.018 (-0.90) -5.389** (-2.46) -0.577 (-1.07) -0.021 (-0.21) -0.023** (-2.27) 0.130*** (3.14) -0.048*** (-3.16) -0.052 (-0.80) 0.274*** (25.74) -0.648*** (-7.03) 2.259*** (25.68) 0.085* (1.81) -4.589*** (-9.65) -0.823*** (-12.01) 0.267*** (2.77) 1.689*** (9.51) 0.333*** (19.78) 2.165 (1.46) Y Y Firm 0.2479 56537
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Tangibility Dividend yield
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Institutional ownership
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Institutional turnover Past return
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Return volatility
Exchange dummies Year & State-fixed effects Industry & Rating-fixed effects Clustering Weak identification test: F-statistic Hansen J (p-value) Adjusted R-squared Number of Obs.
-0.004 (-0.22) -4.185** (-2.01) -0.684 (-1.35) -0.118 (-1.23) -0.017* (-1.82) 0.135*** (3.50) -0.054*** (-3.72) 0.247*** (3.45) 0.319*** (28.23) -0.645*** (-7.19) 1.949*** (22.73) 0.174*** (2.90) -3.057*** (-8.34) -0.868*** (-12.98) 0.249** (2.61) 1.025*** (5.86) 0.325*** (19.54) 1.442 (1.00) Y Y Y Firm 0.2826 56537
2.275 (1.49) Y Y Firm 0.2214 56537
-1.175*** (-3.28)
-1.174*** (-3.28)
-0.031 (-1.28) -3.911 (-1.57) -0.465 (-0.69) 0.053 (0.45) -0.020 (-1.59) 0.073 (1.39) 0.268** (2.19) -0.124 (-1.03) -0.738*** (-8.05) 0.022 (0.81) 1.838*** (12.16) -0.005 (-0.08) -4.966*** (-7.16) -1.253*** (-8.17) -0.680** (-2.18) 0.146** (2.47) 1.448*** (6.13) 5.293** (2.39) Y Y Firm 28.76 56537
-0.017 (-0.72) -2.528 (-1.03) -0.547 (-0.85) -0.005 (-0.04) -0.014 (-1.17) 0.052 (1.03) 0.552*** (4.46) -0.123 (-0.91) -0.699*** (-8.07) -0.010 (-0.46) 1.647*** (13.05) 0.143* (1.83) -3.877*** (-5.88) -1.179*** (-9.50) -0.585** (-2.06) 0.178*** (3.71) 1.093*** (4.92) 4.735** (2.23) Y Y Y Firm 30.16 56537
-0.017 (-0.72) -2.559 (-1.05) -0.552 (-0.86) -0.003 (-0.02) -0.014 (-1.17) 0.051 (1.01) 0.555*** (4.53) -0.122 (-0.91) -0.701*** (-8.11) -0.009 (-0.43) 1.647*** (13.03) 0.144* (1.84) -3.841*** (-5.86) -1.183*** (-9.49) -0.579** (-2.04) 0.178*** (3.69) 1.119*** (5.02) 4.714** (2.23) Y Y Y Firm 15.34 0.028(0.86) 56537
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Local male fraction
-0.012 (-0.59) -5.484** (-2.43) -0.621 (-1.12) -0.012 (-0.12) -0.023** (-2.23) 0.122*** (2.86) -0.107*** (-7.21) -0.022 (-0.34) 0.231*** (26.94) -0.624*** (-6.76) 2.395*** (26.92)
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Log(local population)
-1.220*** (-3.32)
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0.766*** (4.08)
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Local white percentage
Table X (Cont’d) Panel B: Between-Effects and Firm Fixed-effects Regression
Local white percentage
between effects (1) 0.879***
between effects: wls (2) 0.817***
population average (3) 0.534***
firm-fixed effects (4) 0.733**
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Full Specification Clustering Number of Obs.
(4.25)
(4.71)
(3.80)
(2.16)
Y 56537
Y 56537
Y 56537
Y Firm 56537
Y Firm 28278
Y Firm 28260
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6.65***
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Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
Separate by institutional ownership above median below median (3) (4) 0.191 0.887*** (0.97) (3.83) Y Firm 28331
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Local white percentage
Separate by book size above median below median (1) (2) 0.053 0.751*** (0.31) (3.32)
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Panel C: Separate by Size and Institutional Ownership
Y Firm 28207
5.95**
Panel D: Separate by the Number of Local Institutional Investors (Investment Style)
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Y Firm 29281
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Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
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Local white percentage
Investment style: small above median below median (1) (2) 0.913*** 0.100 (3.08) (0.38) Y Firm 27565
Investment style: large above median below median (3) (4) 1.079*** 0.593*** (4.38) (2.68) Y Firm 30813
4.21**
Y Firm 26141 2.45
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Panel E: Separate by the Number of Local Institutional Investors (Trading Style)
Local white percentage
Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
Trading style: transient above median below median (1) (2) 0.969*** 0.113 (4.37) (0.41) Y Firm 30812
Y Firm 25821 5.87**
Trading style: non-transient above median below median (3) (4) 0.763*** 0.582*** (2.61) (2.76) Y Firm 29894
Y Firm 27060 0.27
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S1062-9769(16)00019-3 http://dx.doi.org/doi:10.1016/j.qref.2016.02.005 QUAECO 908
To appear in:
The
Received date: Revised date: Accepted date:
2-9-2014 23-11-2015 1-2-2016
Quarterly
Review
of
Economics
and
Finance
Please cite this article as: Zhang, L.,Local Equity Market Participation and Stock Liquidity, Quarterly Review of Economics and Finance (2016), http://dx.doi.org/10.1016/j.qref.2016.02.005 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Highlights
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We study the relationship between local retail participation and stock liquidity. We use county‐level racial composition to proxy for local retail participation. Stocks in counties with a higher white percentage are more liquid. This effect is stronger among stocks with a high retail concentration. Noise trading increases liquidity under endogenous informed trading.
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Local Equity Market Participation and Stock Liquidity
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Nanyang Business School
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Lei Zhang *
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Abstract: This paper investigates the relationship between local equity market participation and stock liquidity. We use county-level racial composition as a proxy for local retail participation. We find that stocks headquartered in counties with a higher white percentage are more liquid. This effect is stronger among stocks with a high retail concentration (i.e., small size, low institutional ownership, low price). Our findings support Admati and Pfleiderer (1988) that noise trading increases liquidity under endogenous informed trading, i.e., the impact on liquidity is stronger when there are more institutional investors located nearby, especially the ones with “small” investment styles and “transient” trading styles.
Keywords: stock liquidity, retail investors, local bias, noise trading
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JEL Classification: G10, G11, G14, G34
Please address all correspondence to Lei Zhang, Division of Banking and Finance, Nanyang Technological University, 639798, Singapore. Email: [email protected]. I am grateful to Joel Hasbrouck for providing data on stock liquidity measures and Brian Bushee for the classifications of institutional investors. I would like to thank Stephen Dimmock, David Hirshleifer and Rajesh Aggrawal for their helpful comments and suggestions. All errors and omissions are my own.
Introduction Liquidity plays an increasingly important role in empirical asset pricing, market microstructure and
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corporate finance. This paper examines the relationship between local equity market participation and stock liquidity to understand how the trading of local retail investors affects stock liquidity. Two strands of literature have made this study highly worthwhile. First, recent studies show that the tendency to invest in
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local stocks (“local bias”) is even more pronounced for individual investors than for institutional investors. Grinblatt and Keloharju (2000) use a Finnish dataset to find that individual investors tend to hold the
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stocks of companies with nearby headquarters. Using data from a large U.S. discount brokerage, Ivković
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and Weisbenner (2005) find that U.S. retail investors exhibit a strong preference for local investments. They document that the average share of local investments (defined as investments in companies
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headquartered within 250 miles from the investor) is around 30%, which is nearly 20% higher than the average percentage for all firms headquartered within 250 miles from the household. Similar to ownership,
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Loughran and Schultz (2004) provide evidence that retail trading is concentrated in local stocks as well. They find that the trading volume of West Coast companies is lower than that of East Coast firms in the
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bed or on their way to work at that time.
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morning. A direct explanation is that investors in the West Coast, who trade West Coast stocks, are still in
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Though the local bias of individual investors is ubiquitously documented, the origin of this bias is in debate. One argument is that local retail investors have superior information on local firms. Ivković and Weisbenner (2005) show that local investments of individual investors outperform their non-local investments, suggesting that local individual investors possess and exploit their information advantage. However, with the same data, by accounting for the contemporaneous correlation in the cross-section of stock returns, Seasholes and Zhu (2009) find that portfolios of local stocks do not significantly outperform portfolios of remote stocks. In fact, after adjusting for the local benchmark, local investments of individuals’ portfolio even underperform the non-local investments. Their results bring into question the information superiority hypothesis and support the familiarity hypothesis, which argues that investors over-weight local stocks not because they are better informed but because they are familiar with them 1
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(Huberman (2001)). Grinblatt and Keloharju (2001a) also provide support to the familiarity hypothesis by showing that Finnish investors are more likely to trade stocks of firms that share the investors’ same language and cultural background.
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Second, suppose that local retail investors trade because of familiarity instead of information-based reasons; in the market-microstructure literature, such trading is usually referred as “noise trading” or
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“liquidity trading”. In fact, noise trading was proposed as a solution to the “no-trade” results of Grossman
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and Stiglitz (1980) and the information acquisition paradox of Grossman (1976). Noise trading keeps the price from fully revealing and makes the rational expectations equilibrium compatible with costly
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endogenous information acquisition.
Although noise trading constitutes a fundamental component of many microstructure models, the
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theories offer different predictions on how noise trading may affect stock liquidity. In Glosten and Milgrom’s (1985) model, an increased proportion of noise trading decreases the market maker’s adverse
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selection costs and thus increases stock liquidity. However, the amount of informed trading could be
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endogenously determined. Kyle’s (1985) model suggests that the effects of increased informed trading
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exactly offset those of increased noise trading and that liquidity is invariant to changes in the level of noise trading. In Admati and Pfleiderer (1988)’s model, even with endogenous informed trading, an equilibrium exists in which noise trading increases stock liquidity. Under their framework, increased noise trading increases the profits of informed traders while at the same time attracting more informed traders with the same information. Therefore, each informed trader must trade more aggressively to exploit the short-lived information and, as a result, compete away their information advantage. In equilibrium, the market maker faces a smaller adverse selection problem when noise trading increases. Surprisingly, little empirical evidence exists to clearly identify the impact of noise trading on liquidity. One approach is to look for unexpected large shifts in noise trading. Greene and Smart (1999) use the “Investment Dartboard” column in The Wall Street Journal as a natural experiment to evaluate the effect of 2
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the change of noise trading on the change of liquidity. They find that the bid-ask spread decreases and the trading volume increases after analysts recommend stocks in this column. Their evidence is consistent with the predictions of Glosten and Milgrom (1985) and Admati and Pfleiderer (1988).
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In this paper, we aim to analyze how the (noise) trading of local retail investors affects stock liquidity. Our approach is to identify an exogenous measure of the supply of local retail trading, which is less likely
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to be driven by firm-specific characteristics that may affect firms’ demand for liquidity. We start from the
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existing literature that documents a striking racial difference in risky asset holdings (Blau and Graham (1990), Choudhury (2002), Hong, Kubik and Stein (2004)). The difference holds after controlling for racial
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differences in income and other demographic factors (Blau and Graham (1990)). Choudhury (2002) uses the Health and Retirement Study (HRS) 1992 survey data to show that at every income and education level,
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the percentage of minority households who own risky assets is much smaller than that of white households. Hong, Kubik and Stein (2004) show that, on average, the stock-market participation rate for white
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households is 34.3%, compared to 9.2% for minority households. Even at the highest quintile of wealth
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distribution, the stock-market participation rate of white households remains much higher than that of
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minority households (57.5% vs. 35.6%)1.
We then provide evidence that there are large cross-sectional variations in the white percentage among U.S. counties. On average, the proportion of white population is 81%, and it ranges from 25% to 99% with a standard deviation of 12%. It is higher in northern states and lower in southern states. However, this is not merely a state phenomenon. For example, for counties in the state of California, the white percentage ranges from 56% to 98% with a mean of 76%. For counties in the state of New York, the white percentage ranges from 52% to 98% with a mean of 78%. Given that white households are more likely to 1
Many factors might contribute to the low market participation rate of minority households. Brimmer (1998) shows that African American households are more risk-averse than white households. African American households who do have a margin of funds to invest typically prefer safer assets, such as checking accounts or real estate, when compared with white households. Another possibility is that of cultural or intergenerational reasons. African Americans are more willing to invest in real estate and certificates of deposit because those industries have marketed their services to African Americans and have agents who are themselves African American. Mabry (1999) points out that African Americans shy away from stocks partly because of their mistrust of Wall Street.
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participate in the stock market than minority households and that local investors tend to invest and trade in local stocks for non information-based reasons, it is reasonable to use the county-level white percentage as a proxy for the level of noise trading by local retail investors. The cross-sectional variations in the local
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white percentage allow us to study the relationship between the local retail participation and stock liquidity and to understand the impact of noise trading on liquidity.
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If the local white percentage is indeed a valid proxy for local retail trading, given the proximity
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ownership or trading behavior of retail investors, it should be positively related to the fraction of trading volume associated with retail investors. Because the number of trades by institutions and individuals is not
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publicly available, we follow Shu (2006) and Hvidkjaer (2008) to infer the fraction of the institutional trading volume and subtract it from 1 to obtain the fraction of retail trading volume. We document a strong
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positive relationship between the local white percentage and the fraction of retail trading volume. The effect is especially strong for stocks with a high retail concentration (small size, low institutional
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streams.
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ownership, and low price) and for stocks headquartered in counties with more volatile household income
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Next, we run our main regressions of stock liquidity on the local white percentage. We find that stocks headquartered in counties with a higher white percentage are more liquid. This result is robust to the inclusion of state, industry and credit rating fixed effects, controls for firm-specific characteristics, controls for other local demographic characteristics and alternative measures of liquidity. The result is not only statistically significant but also economically relevant. A one standard deviation (11.9%) increase in the local white percentage increases the liquidity of a stock by 10.4%. This result survives our robustness checks and holds for sub-samples separated by county population, sample period and firm age. It also holds for the sample of firms that never move headquarters during the sample period. The result remains strongly significant in between-effects regressions and firm fixed-effects regressions. More importantly, we perform a series of sub-sample comparisons. The impact of local white 4
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percentage on stock liquidity is stronger for stocks with a potentially high retail concentration (i.e., small size, low institutional ownership, low price, value, high volatility and scarce analyst coverage). It is especially stronger for firms located in the “only-game-in-town” counties and in counties with more
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volatile household income streams. The differences in coefficients between sub-samples are always significant. We also analyze the change of stock liquidity and the change in the local white percentage for a
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small group of firms that moved their headquarters. Consistently we document an increase in stock
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liquidity when they move from a county with a low white percentage to another county with a high white percentage.
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The result for sub-sample comparisons strengthens our belief that the impact of the local concentration of white people on stock liquidity indeed occurs through the channel of retail trading by
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local investors. The findings are consistent with market microstructure models that predict a positive relationship between noise trading and stock liquidity (Glosten and Milgrom (1985) and Admati and
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Pfleiderer (1988)). The key difference between the two models is that in Admati and Pfleiderer’s (1988)
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model, the informed trading is endogenously determined. Given that endogenous informed trading seems
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to be a rather reasonable assumption, we expect the effect of local retail participation on liquidity to be dependent on the level of competition among informed traders. It is generally accepted that institutional investors possess an information advantage over locally headquartered stocks (Coval and Moskowitz (1999, 2001)). Institutional investors that tilt their portfolios toward small stocks and have short investment horizons are more active to collect information and pursue information-based trading (Coval and Moskowitz (1999), Baik, Kang and Kim (2009), Yan and Zhang (2009)). We therefore perform sample separation by the number of local institutional investors and distinguish institutional investors by their investment style (small/large) and trading style (transient/non-transient). The results strongly support Admati and Pfleiderer (1988) in that the impact of local retail participation on stock liquidity is stronger when there are more institutional investors located 5
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close-by, especially more local institutional investors with “small” investment styles and “transient” trading styles. Our paper makes several contributions to the literature. To the best of our knowledge, this study is the
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first to document the interesting effect of local retail participation on stock liquidity. The measure of the local white percentage is unlikely to be driven by unobserved firm-specific characteristics. We show that it
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serves as a good proxy for the level of local noise trading by retail investors. From this perspective, our
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work offers the first large-sample cross-sectional analysis of the effect of noise trading on stock liquidity. Second, our work complements the existing literature on how geographical locations affect stock
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liquidity. Loughran and Schultz (2005) show that stocks located in urban areas are more liquid. Even after controlling for the urban effect, the coefficient on the local white percentage remains strongly significant.
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We also show that the local education level is not a driver of our result. On the contrary, the variable of the local white percentage completely absorbs the explanatory power of local education level.
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Third, our paper complements the emerging literature that explores the impact of local demographics
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on corporate financial policies. Becker, Ivkovic and Weisbenner (2011) find that the fraction of local
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seniors is an important determinant of corporate dividend policy. The focus of this paper is on local retail participation and stock liquidity.
Fourth, we add more evidence to the literature that evaluates the relationship between stock liquidity and firm performance. Using the local white percentage as an instrument for stock liquidity, we find results consistent with those of Fang, Noe and Tice (2009), i.e., that stock liquidity positively affects firm value. We show that the beneficial effect of liquidity is directly related to the level of local noise trading and the competition among local institutional investors. Collectively, our paper adds to the understanding of the way that retail investor trading and local bias affect stock liquidity and firm value. The rest of the paper proceeds as follows. In the next section, we describe the data and variable definitions. Section 3 reports the summary statistics and the results on the fraction of retail trading volume. 6
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Section 4 presents our main results on stock liquidity and robustness checks. We also provide sub-sample comparisons and present the results for firms that move headquarters. In section 5, we offer market microstructure explanations of our results. In section 6, we focus on local retail participation, stock
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liquidity and firm value. Section 7 concludes.
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II. Data and Variable Definitions
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II.A Sample Selection
We compile our data from several sources. We first describe the county-level data. The county-level data
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come from the US Census Bureau Population Estimates Program from 1990 to 2005. The Population Estimates Program publishes total resident population estimates and demographic components of change
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(births, deaths, and migration) each year. It also publishes the estimates according to demographic characteristics (age, sex, race, and Hispanic origin) for the nation, states, and counties.2
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We only include the county populations aged from 35 to 85 years. The purpose of this requirement is
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to focus on the age groups that are most likely to own and trade stocks.3 We require the size of the county
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population to be larger than 50,000.4 In addition, the data on household income come from the US Census Bureau SAIPE (Small Area Income and Poverty Estimates) datasets from 1990 to 2005. The data on historical local unemployment rates come from the Bureau of Labor Statistics. Daily and monthly stock returns come from the Center for Research in Security Prices (CRSP), and firm-level accounting information is from the CRSP/Compustat Merged database. We only include stocks
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These estimates are provided with the assistance of the Federal State Cooperative Program for Population Estimates (FSCPE). The Census Bureau begins the process of preparing population estimates by updating population information from the most recent census with information found in the annual administrative records of Federal and state agencies. The Federal agencies provide tax records, Medicare records and some vital statistical information. The FSCPE agencies supply vital statistics and information about group quarters such as college dorms or prisons. The Census Bureau and FSCPE members combine census and administrative records information to produce current population estimates that are consistent with the last decennial census counts. 3 We also try to change this requirement into age groups of 20-85, 25-85 or 30-85 years. All of the results are consistent and available upon request. In all later references, “population” refers to the selected age group of 35 to 85 years. 4 All of the results are consistent if we change this requirement into above 10,000 or above 100,000.
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with book values larger than 5 million dollars to ensure that our results are not driven by tiny stocks. Our information on historical firm locations (county, state, zip code) comes from Compact Disclosures from 1990 to 2004. The corresponding values of latitude and longitude are obtained from the Gazetteer Files of
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Census 2000. Quarterly equity holdings of institutional investors come from Thomason CDA/Spectrum (13F). The locations (zip codes) of institutional investors come from their SEC filings. The various
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measures of stock illiquidity are obtained from Joel Hasbrouck’s website.5
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II.B Variable Definitions
We first describe the measures of our dependent variable: stock illiquidity. We use the Amihud (2002)
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illiquidity measure as our main variable. Hasbrouck (2009) and Goyenko, Holden and Trzcinka (2008) find
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that the Amihud illiquidity measure is highly correlated with the high-frequency TAQ-based measures and effectively captures the price impact of trading.6 The Amihud illiquidity measure averages the square root
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of the ratio of the absolute price change divided by the daily dollar volume over each day in year t. It is
te
calculated as
Ac ce p
Illiquidity i , t
1 Dt
(1000 *
D ays t
|daily return| |daily dollar volum e|
),
where Dt is the number of days in year t. We use three other popular illiquidity measures as robustness checks. The second illiquidity measure is the estimate of the coefficient ci in the following market-adjusted model:
pi ,t ci qi ,t i rm ,t ui ,t
,
where pi ,t is the log of the price and qi ,t is a trade direction indicator that takes the values +1 or -1 for buys and sells, respectively. rm ,t is the market return and ui ,t is the residual independent of qi ,t and 5
http://pages.stern.nyu.edu/~jhasbrou/Research/GibbsEstimates2006/Liquidity%20estimates%202006.htm. Goyenko, Holden and Trzcinka (2008) conclude that the literature has generally not been mistaken in the assumption that (low-frequency) liquidity proxies measure liquidity. 6
8
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rm ,t . The trade direction indicators qi ,t are unobserved because the model is estimated from daily CRSP
data, and the coefficient estimates are obtained using the Gibbs sampler. The third measure is Roll’s (1984) measure of effective trading costs, estimated as the square root of
ip t
the negative of the auto-covariance of daily log returns, which is set to zero when the sample
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auto-covariance is positive. The fourth measure is the intercept 0i and slope coefficient 1i from the
ci ,t 0i 1i zt ,
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latent common factor model given by the following equations:
an
pi ,t ( 0i 1i zt )qi ,t i rm ,t ui ,t ,
where zt is the latent common factor. All four measures are winsorized such that our results are not
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driven by outliers. We do not include Pastor and Stambaugh’s (2003) gamma measure because the authors caution against its use as a liquidity measure for individual stocks.7
d
The major independent variable is what we call the “local white percentage,” which represents the
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proportion of the white population in each county. Other county-level variables are defined as follows.
Ac ce p
“Log(local population)” is the natural log of the size of a county’s population. “Local male fraction” is the fraction of the male population. “Local senior fraction” is the fraction of the population that is more than 65 years old as in Becker, Ivković and Weisbenner (2011). “Log(local household income)” is the natural log of the median household income in each county. “Local unemployment rate” is the historical county-level unemployment rate.
Next, we define firm-specific control variables. We first construct an urban dummy. A stock is defined as an urban stock if the company is headquartered less than 100 miles from the center of one of the thirty largest US cities according to the 2000 census. The market value of assets is defined as stock price 7
Goyenko, Holden and Trzcinka (2008) find that the Pastor and Stambaugh Gamma and the Amivest illiquidity measure have little to do with price impact benchmarks used in the literature.
9
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(data199) * shares outstanding (data25) + short term debt(data34) + long term debt(data9) + preferred stock liquidation value (data10) – deferred taxes and investment tax credits (data35). The market-to-book ratio is defined as the market value of assets divided by the book assets (data6). Total debt is defined as
ip t
long-term debt (data9) + short-term debt (data34). Firm leverage is total debt divided by book assets (data6). The market cap is the natural log of a firm’s market value of equity (data199*data25). Profitability
cr
is the operating income before depreciation (data13) divided by the book assets (data6). Firm age is
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defined as the natural log of the number of years since a firm first appears in the CRSP. Cash holding is cash and short-term investments (data1) divided by book assets (data6). Tangibility is net PPE (data8)
an
divided by book assets (data6). The dividend yield is calculated as cash dividends per share (data26)/stock price (data199). Institutional ownership is the fraction of institutional holdings, calculated from the
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Thomason CDA/Spectrum Institutional Ownership Database (13F).
We follow Gaspar, Massa and Matos (2005) to define institutional turnover. We first calculate the
d
portfolio churn rate of institutional investors, which captures how frequently an investor rotates his
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positions on all of the stocks of his portfolio. If we denote the set of companies held by investor i as Q, the
Ac ce p
churn rate of investor i at quarter s is
CR i , s
jQ
N i , j , s P j , s N i , j , s 1 P j , s 1 N i , j , s 1 P j , s
N i , j , s 1 P j , s 1 N i , j , s P j , s
,
2
jQ
where Pj , s and N i , j , s represent the price and number of shares of stock j held by investor i at quarter s. Let wi , j ,t be the weight of investor j’s holding stock i held by institutional investors at quarter t. The investor turnover of stock i is the weighted average of the total portfolio churn rates of its investors over the previous four quarters: T u rn o veri , t
j S i
1 w i , j ,t 4
4
CR r 1
j ,t r 1
.
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The yearly return is the cumulative stock return over the year. The return volatility is the standard deviation of monthly stock returns in the year. Analyst coverage is the total number of analysts covering the stock in a year.8
ip t
III. Preliminary Analysis
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III.A Summary Statistics
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We provide summary statistics of our sample in Table I. Panel A presents summary statistics of the main variables. We report the number of observations, the mean, the 5-percent quantile, the 95-percent quantile
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and the standard deviation. For the full sample, the mean of local white percentage is 81.4% with a standard deviation of 11.9%. The 5-percent quantile is 57.3%, and the 95-percent quantile is 96.5%. The
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data show a large cross-sectional variation in the local white percentage across U.S. counties. Panel B presents the correlation matrix of local population characteristics. We report the correlations among the
d
local white percentage, the size of the local population, the local male fraction, the local senior fraction, the
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local household income and the local unemployment rate. We find that the local white percentage is
Ac ce p
negatively correlated with the size of the local population (-0.29), positively correlated with the local male fraction (0.26), positively correlated with the local household income (0.21) and negatively correlated with the local unemployment rate (-0.30).
As expected, the local white percentage is relatively higher for northern states than that of southern states due to historical reasons. To determine whether this is simply a state effect, we summarize the variable of local white percentage by states in Panel C. We report the state name, the state code, the mean, the median, the minimum, the maximum, the standard deviation and the number of observations for each state. We conclude that the variations of the local white percentage are not merely a state effect. We find
8
The data on analyst coverage come from the Thomson I/B/E/S database.
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that even for large states, such as California, there still exist considerable county variations in the percentage of local white population. The percentage ranges from 56% to 98% with a mean of 76%. For counties in the state of New York, the local white percentage ranges from 52% to 98% with a mean of 78%.
ip t
In southern states, such as the state of South Carolina, it ranges from 56% to 91% with a mean of 79%.
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III.B Fraction of Retail Trading Volume
We argue from the existing literature that the local white percentage serves as a measure of the rate of local
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equity market participation of retail investors. If it is indeed a valid proxy, given the proximity ownership/trading behavior of retail investors, it should be positively related to the fraction of trading
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volume associated with retail investors.
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In Table II, we explore the impact of local retail investor characteristics on the cross-section of retail trading volume. Because the number of trades by institutions and individuals is not publicly available, we
d
follow Shu (2006) and Hvidkjaer (2008) to infer the fraction of institutional trading volume and subtract it
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from 1 to obtain the fraction of the retail trading volume. We first calculate the fraction of institutional
Ac ce p
trading volume using the following formula:
N
InstitutionalFractioni ,t
| IO j 1
i , j ,t
IOi , j ,t 1 |
TOi ,t
,
where IOi , j ,t is institution j’s ownership of stock i for quarter t, calculated as institution j’s holdings of stock i divided by i’s shares outstanding at the end of quarter t. TOi ,t is the total turnover of stock i in quarter t. We then calculate the fraction of retail trading volume as RetailFractioni ,t 1 InstitutionalFractioni ,t .
Because the measure of institutional trading fraction double-counts the trades between institutions, the
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institutional fraction ranges from 0 to 2; therefore, the retail fraction ranges from -1 to 1.9 In Table II, the dependent variable is the yearly average of the retail fraction. Columns (1) and (2) are based on the full sample. Column (1) is the baseline specification. We control for state fixed effects;
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county-level characteristics, such as population, gender, age, household income, unemployment rate and an urban dummy, as well as firm-specific variables, including leverage, market-to-book, market capitalization,
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profitability, firm age, cash holding, tangibility and the dividend yield. We further control for institutional
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ownership, institutional turnover, past return and return volatility. All variables on the right-hand side are lagged values. From column (2), we include industry fixed effects at the two-digit SIC level and cluster the
an
standard errors at the firm level. Columns (3)-(6) are based on sub-samples where retail trading is more likely to be evident. Column (3) is for the sub-sample where the firm’s book asset is below the sample
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median. Column (4) is for the sub-sample where the firm’s institutional ownership is below the sample median. Column (5) is for the sub-sample where the maximum price in the previous year is below the
d
sample median. Column (6) is for the sub-sample where the volatility of local household income during
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the sample period is above the sample median.
Ac ce p
The results justify our claim that the local white percentage measures the level of the local equity market participation of retail investors. We document a strong positive relationship between the local white percentage and the fraction of retail trading volume. The effect is stronger for stocks with potentially high retail concentration (small size, low institutional ownership and low price) and especially stronger for stocks headquartered in counties with more volatile household income streams. It makes intuitive sense that in areas where investors have a higher need to trade because of volatile income, the impact of the local white percentage on the retail trading volume is indeed higher. Unreported results show that the effect is also stronger in counties where the “only-game-in-town” effect of Hong, Kubik and Stein (2008) is higher. For other demographic characteristics, the local unemployment rate is negatively related to retail trading 9
The average institutional trading fraction in our sample is 57%, consistent with the findings of Shu (2006) and Hvidkjaer (2008).
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volume. The local senior fraction is negatively related to the retail trading volume, consistent with the finding of Becker, Ivković and Weisbenner (2011) that seniors are largely buy-and-hold investors with longer investment horizons. For firm-specific control variables, market cap, institutional ownership and
ip t
dividend yield are negatively related to retail trading volume, whereas return volatility, cash holding and
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market-to-book are positively related to retail trading volume.
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IV. Results on Stock Liquidity IV.A Main Results
an
Having established that the local white percentage serves as a nice proxy for the level of local retail participation, and there are enough cross-sectional variations for us to exploit, we now present our main
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results of stock liquidity on the local white percentage.
We report the findings in Table III. The dependent variable is the Amihud illiquidity measure, which
d
is defined as the square root of the average daily price impact of trading in each year. Column (1) is the
te
baseline specification. In column (2), we add more controls on local characteristics including the local
Ac ce p
male fraction, the local senior fraction, the local household income, the local unemployment rate and an urban dummy. From column (3), we cluster the standard errors at the firm level. We add more firm-level controls in column (4). In column (5), we add state fixed effects. In column (6), we further include industry fixed effects (two-digit sic) and a credit rating fixed effect (investment grade/junk/non-rated). ***, ** and * represent significance levels of 1%, 5% and 10%, respectively, using robust standard errors with t-statistics given in parentheses.
The results show that the local white percentage is strongly and negatively related to stock illiquidity. We always include exchange dummies (NYSE/AMEX/NASDAQ) to control for the microstructural differences among exchanges that might affect stock liquidity (Pagano and Roell (1996)). We include 14
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industry and state dummies to control for other unobserved industry and state effects (for instance, tax reasons as in Ivković, Poterba and Weisbenner (2005)) that affect the trading behavior of individual investors. We include credit rating dummies to control for the effects of credit ratings on stock liquidity
ip t
(Odders-White and Ready (2006)). In the most complete specification, i.e., in column (6) of Table III, the coefficient on the local white percentage is -0.624 with a t-statistic of -5.95, which is not only statistically
cr
significant but also economically relevant. A one-standard-deviation (11.9%) increase in the local white
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percentage increases the liquidity of a stock by 10.4%.10
The results for control variables are interesting. We find that the size of the local population and the
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local male fraction are negatively related to stock illiquidity, whereas the local household income11 and local unemployment rate are positively related to stock illiquidity. However, these relationships lose most
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of their statistical significance when we cluster the standard errors at the firm level and include more firm-specific controls. The urban dummy is significantly negatively related to stock illiquidity, which is
d
consistent with Loughran and Schultz (2005). Consistent with Gopalan, Kadan and Pevzner (2009), the
te
market cap is negatively related to stock illiquidity, leverage is positively related to stock illiquidity and the
Ac ce p
cash holding is negatively related to stock illiquidity. Consistent with intuition, firm age is positively related to stock illiquidity, whereas institutional ownership, institutional turnover and past stock return are negatively related to stock illiquidity. IV.B Robustness Checks
Next, we provide some robustness checks on the positive relationship between local retail participation and stock liquidity. We report the results in Table IV. Panel A is for sub-sample analysis. We separate the sample by the local population and the sample period. In columns (1) and (2), we separate the sample by 10
To put this number into perspective, we find that a one-standard-deviation increase in cash holdings increases stock liquidity by 7%, a one-standard-deviation increase in the historical return increases stock liquidity by 11%, and a one-standard-deviation increase in firm leverage reduces stock liquidity by 7%. 11 This finding is to some extent consistent with Becker, Cronqvist and Fahlenbrach (2010) in that the presence of local wealthy individual blockholders reduces stock liquidity.
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the size of local population (above/below median)12. In columns (3) and (4), we analyze the subsamples separated by sample period (before 1998/after 1998). We always utilize the “full specification”, as in column (6) of Table III, and cluster the standard errors at the firm level. For brevity, we do not report the
ip t
control variables. We find consistent results in both sub-samples separated by the size of population. The impact of the
cr
local white percentage is higher in relatively more populous counties. We also try to break the sample up
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by the number of firms in each state-year and find similar results in both subsamples (unreported), which means that the impact of local retail participation holds for both large and small states. Interestingly, the
an
effect is stronger in the before-1998 period, which is consistent with the idea that in the pre-Internet age, the “locality” of retail trading should be higher.
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In Panel B of Table IV, we use different measures of stock illiquidity as dependent variables. We employ four measures from column (1) to column (4). C^bma is the coefficient on the change in the trade
d
direction indicator in the basic market-adjusted model. C^roll is the Roll (1984) trading cost estimator
te
excluding quote mid-points. Gamma0 and Gamma1, respectively, are the intercept and slope coefficient in
Ac ce p
a latent common factor model to estimate the effective costs of trading. Consistent with the results on the Amihud illiquidity measure, in all specifications, the variable of the local white percentage is significantly and negatively related to stock illiquidity.
In Panel C of Table IV, we use alternative measures of the local white percentage. In columns (1) and (2), the local white percentage is defined as log(1+county white population*county average income/county book assets). County book assets are the sum of book assets for all firms headquartered in the county. In columns (3) and (4), the local white percentage is defined as log(1+county white population*county average income/county market assets). County market assets are the sum of market assets for all firms
12 The sample median of the county population is 457,216. Unreported results show that even among counties with a population size above the sample median, the percentage of local white population ranges from 52% to 95% with a mean of 78%.
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headquartered in the county, which is along the line of Hong, Kubik and Stein (2008) and represents the per-asset supply of local white households. We find that in all specifications, the alternative measures of local white percentage are significantly and negatively related to stock illiquidity. However, the drawbacks
ip t
of these variables are that they depend on both the supply and the demand of liquidity by local firms. They are not a purely supply-based measure. We therefore always use them as robustness checks.
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In Panel D of Table IV, we perform between-effects and firm fixed effects regression of stock
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illiquidity on the local white percentage. The dependent variable is Amihud illiquidity. Column (1) fits random effects models using the between regression estimator. Given the unbalanced panel, column (2)
an
performs between-effects estimation with weighted least squares rather than the default OLS. Column (3) fits random-effects models using the population-averaged estimator. In column (4), we run OLS
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regressions with firm fixed effects and cluster the standard errors at the firm level. In all specifications, we find consistent results that the local white percentage is significantly and negatively related to stock
d
illiquidity. For the regression with firm-fixed effects, the significance of the local white percentage is
te
weaker than before (coefficient of -0.452 with a t-statistic of -2.59), which actually supports our claim that
variations.
Ac ce p
the explanatory power of the local white percentage mostly comes from cross-sections instead of time
Panel E of Table IV deals with the concerns that liquid firms self-select themselves into counties with a high local white percentage. We therefore separate the sample by firm age and also focus on the firms that never move headquarters. Later, we will examine the change of liquidity for firms that do move their headquarters. Column (1) is for the sub-sample with a firm age of more than 10 years. Column (2) is for the sub-sample with å firm age of less than 10 years. Columns (3) and (4) are for the firms that never move headquarters during the sample period. The dependent variable is the Amihud illiquidity measure in columns (1)-(3), whereas in column (4), it is the Gibbs estimate of C^bma. We find that the result holds for both sub-samples separated by firm age and is actually slightly higher for relatively older firms. The effect 17
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of the local white percentage on illiquidity remains strongly negative for the sub-sample of firms that never move headquarters during the sample period. IV.C Sub-sample Comparisons
ip t
Because we argue that our result is driven by local retail trading, it should be stronger along the
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dimensions where the effects of individual investors are expected to be more evident. We therefore separate the sample according to those dimensions and compare the impact of the local white percentage
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between sub-samples. We consider firm characteristics including firm size, institutional ownership, price, P/E ratio, analyst coverage and return volatility.13 We also consider county characteristics such as the
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“only-game-in-town” effect and average household income volatility.
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We report the results in Table V. In Panel A, we separate the full sample by the size of book assets (columns (1) and (2), above median/below median), and the level of institutional ownership (columns (3)
d
and (4), above median/below median). In Panel B, we separate the full sample by the maximum stock price
te
in the previous year (columns (1) and (2), above median/below median), and the price-earnings ratio (columns (3) and (4), above median/below median). In Panel C, we separate the full sample by the level of
Ac ce p
analyst coverage (columns (1) and (2), above 1/below 1), and the return volatility (columns (3) and (4), above median/below median). In Panel D, we separate the full sample by the only-game-in-town effects (columns (1) and (2), above median/below median), and the local household income volatility (columns (3) and (4), above median/below median). The only-game-in-town effect is measured as log(county population*average income/county book assets). Local household income volatility is calculated as the volatility of a county’s average household income during the sample period. We always utilize the “full specification” as in column (6) of Table III and cluster the standard errors at the firm level. For brevity, we
13
Kumar and Lee (2006) suggest that small-cap stocks, value stocks, stocks with lower institutional ownership and lower-priced stocks are more likely to have a higher retail concentration. Kumar (2009) shows that individual investors prefer stocks with lottery features (low price, high volatility).
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do not report the control variables. We perform a Chi-squared test to evaluate the differences in coefficients between sub-samples. ***, ** and * represent significance levels of 1%, 5% and 10%, respectively.
ip t
The results strongly support our claim that the effect of the local white percentage on stock liquidity is due to local retail trading. We find that the impact of the local white percentage on stock illiquidity is
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significantly stronger for small firms (-0.665 vs. -0.189), for firms with low institutional ownership (-0.797
us
vs. -0.116), for stocks with a low price (-0.713 vs. -0.059), for stocks with a low P/E ratio (-0.759 vs. -0.387), for stocks with low analyst coverage (-0.780 vs. -0.112), and for stocks with high return volatility
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(-0.740 vs. -0.480). Consistent with the previous results on retail trading volume, the impact of the local white percentage on stock illiquidity is significantly stronger for counties with a higher
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“only-game-in-town” effect (-0.627 vs. -0.186) and for counties with more volatile household income
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IV.D Firms Moving Headquarters
d
streams (-0.766 vs. -0.160). The differences in coefficients between sub-samples are always significant.
We now analyze the change of stock illiquidity and the change of local retail participation for the firms that
Ac ce p
re-allocated headquarters during the sample period. We report the results in Table VI. Historical firm locations come from Compact Disclosure. We denote a firm as a mover if the location of its headquarters in year t is in a different county from its location in year t-1. The dependent variable is the difference in stock illiquidity between year t+1 and year t-1. The right-hand-side variables also change from year t-1 to year t+1. The dependent variable in columns (1) and (2) is Amihud illiquidity, and it is C^bma, C^Roll, Gamma0 and Gamma1 from column (3) to column (6), respectively. Year, state and industry fixed effects are always included. Among the movers, on average, the change in Amihud illiquidity is -0.0049, and the change in the local white percentage is 1.8%. The relationship between the change in stock illiquidity and the change in 19
Page 21 of 53
the local white percentage is negative and significant, which means that when firms move from counties with a low share of white population to counties with a high share of white population, there is an increase in stock liquidity. The results are not only statistically significant but also economically relevant. A
ip t
one-standard-deviation increase in the change of local white percentage (16.1%) increases stock liquidity by 20.5% (column 2 of Table VI). The results for other illiquidity measures are consistent.
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IV.E Further Evidence
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Finally, we provide some further evidence on the effect of local retail participation on stock illiquidity. Hong, Kubik and Stein (2004) document that college graduates are more likely to participate in the equity
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market. It is therefore worthwhile to jointly consider the impact of the local education level, the local white
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percentage and stock liquidity. We measure the local education level as the percentage of adults in each county with some college education, which includes those who completed one to three years of college.
d
The data at the county education level come from the Economic Research Service in the US Department of
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Agriculture. Moreover, we perform another robustness check to our mail results, by examining the relationship between local equity market participation and stock illiquidity at the county level.
Ac ce p
We present the results in Table VII. Panel A runs county-level regressions of local education level on local population characteristics. In Panel B, we examine the effects of local education level and local racial composition on stock illiquidity. The dependent variable in columns (1) and (2) is the Amihud illiquidity measure. The dependent variable in columns (3) and (4) is the Gibbs estimate of trading costs C^bma. In columns (2) and (4), we include both the local educational level and the local white percentage in the regression. We always utilize the “full specification” as in column (6) of Table III and cluster the standard errors at the firm level. In Panel C, we perform the analyses at the county level. Specifically, for each county, we calculate the average illiquidity and average firm characteristics across all the firms headquartered in the county. The dependent variable is the average firm illiquidity in the county. There are 20
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5399 county-year observations in the regressions. Indeed, the local education level and the local white percentage are positively related with a correlation of 0.27. The positive relationship still holds in county-level multivariate regressions, but the
ip t
local white percentage alone only explains about 1.5% of the local education level in terms of R-squared. Panel B shows that, if put separately, the local educational level is significantly and negatively related to
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stock illiquidity. However, if we put the local white percentage and the local education level together in the
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regression, the former entirely absorbs the explanatory power of the latter. For the Amihud illiquidity measure, the t-statistic of the local education level drops from -3.72 to -1.07 on the inclusion of local
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whites. We find a similar pattern for C^bma. In both cases, the variable of local white percentage remains highly significant as before. This result further strengthens our belief that the measure for the local white
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percentage indeed captures the level of retail participation in the equity market.14 Also, the county-level results are consistent with the previous ones that local equity market participation is strongly negatively
te
d
related to the county-average stock illiquidity.
Ac ce p
V. Market Microstructure Explanations
In the previous section, we established a positive relationship between the local white percentage and stock liquidity. We argue that the channel is generated through the trading of local retail investors. We now provide additional tests to strengthen this argument. Specifically, we use an instrumental variables regression approach to relate stock illiquidity to the measure of retail trading fraction as previously constructed in Table II, where we instrument the retail trading fraction by the variable of local white percentage. Effectively, this represents a two-stage OLS regression: in the first stage, we use the local white percentage to explain retail trading fraction of the stock, and in the second stage, we use the
14 It is also consistent with the findings of Choudhury (2002) that at every education level, the ownership of risky assets by white households is much higher than that by minority households.
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explainable (predicted) part of retail trading fraction by the local white percentage from the first stage regression to explain stock illiquidity. We report these results in Table VIII. In columns (1)-(3), we report the results for the subsample of
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firms with high retail concentration, such as small firms, firms with low institutional ownership and stocks with low price. In columns (4)-(6), we report the results for the subsample of firms with low retail
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concentration, such as large firms, firms with high institutional ownership and stocks with high price. The
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results strongly support our argument that local equity market participation affects stock liquidity through the trading of local retail investors. The instrumented retail trading fraction is highly negatively related to
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stock illiquidity for stocks with high retail concentration, i.e., small firms, firms with low institutional ownership and stock with low prices. The F-statistics in the first-stage regressions are around 10 showing
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that the instrument is not weak. On the contrary, the results are not significant for stocks with low retail concentration with the F-statistics well below 5, showing that local retail participation may not be large
d
enough to drive the overall retail trading of these stocks.
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Next, Seasholes and Zhu (2009) show that local individual investors tend to invest and trade local
Ac ce p
stocks because of familiarity instead of information-based reasons. In the market-microstructure literature, such trading is usually referred as “noise trading”. Therefore, our findings are consistent with market microstructure models that predict a positive relationship between noise trading and stock liquidity (Glosten and Milgrom (1985) and Admati and Pfleiderer (1988)).15 The key difference between the two models is that in Admati and Pfleiderer (1988), the informed trading is endogenously determined. In AP (1988), with endogenous informed trading, an equilibrium exists in which noise trading increases stock liquidity. Under their framework, increased noise trading increases the profits of informed traders while at the same time attracting more informed traders with the 15
Greene and Smart (1999) use the “Investment Dartboard” column on The Wall Street Journal as a natural experiment to evaluate the effect of the change of noise trading on the change of liquidity. Their evidence is consistent with Glosten and Milgrom (1985) and Admati and Pfleiderer (1988), but they do not perform further tests to distinguish between the two models.
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same information. Therefore, the informed trader must trade more aggressively to exploit the short-lived information and as a result compete away the information advantage. In equilibrium, the market maker faces a smaller adverse selection problem when noise trading increases.
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Given that endogenous informed trading seems to be a rather reasonable assumption, we expect the effect of local retail participation to be dependent on the level of competition among informed traders. It is
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generally accepted that institutional investors possess an information advantage over locally headquartered
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stocks (Coval and Moskowitz (1999, 2001)). Institutional investors that tilt their portfolios toward small stocks and have short investment horizons are more active in collecting information and pursuing
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information-based trading (Coval and Moskowitz (1999, 2001), Baik, Kang and Kim (2009), Yan and Zhang (2009)). We therefore perform sample separation by the number of local institutional investors and
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compare the effect of local retail participation in different sub-samples. We also distinguish institutional investors by their investment style (small/large) and trading style (transient/non-transient).
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We report the results in Table IX. Because CDA/Spectrum 13F does not provide the locations of
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institutional investors, we collect the zip codes of the locations of money managers from their SEC filings
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downloaded from Morningstar Document Research.16 The corresponding values of latitude and longitude are obtained from the Gazetteer Files of Census 2000. We obtain the style classifications of institutional investors from Brian Bushee’s website. The investment style is “small” if the Bushee investor style classification is “SGR” (small growth) or “SVA” (small value). The investment style is “large” if the Bushee investor style classification is “LGR” (large growth) or “LVA” (large value). The trading style is “transient” if the Bushee investor type classification is “TRA” (transient). The trading style is “non-transient” if the Bushee investor type classification is “QIX” (quasi-indexed) or “DED” (dedicated)17.
16
Morningstar Document Research provides the SEC filings of money managers from 1998 onward. For the sample period before 1998, we backfill the information on institutional locations using the 1998 data. 17 The detailed definitions of style classifications can be found in Bushee (2001), Bushee and Noe (2000) and from his website at: http://acct3.wharton.upenn.edu/faculty/bushee/.
23
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For each firm-year, we count the number of institutional investors that are located within a 250-mile radius18 around the firm’s headquarter. In Panels A, C and D, the dependent variable in columns (1) and (2) is the Amihud illiquidity. The dependent variable in column (3) and column (4) is the Gibbs estimate of
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trading costs, C^bma. We always utilize the “full specification” as in column (6) of Table III and cluster the standard errors at the firm level. For brevity, we do not report the control variables. We perform a
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Chi-squared test to evaluate the differences in coefficients between sub-samples.
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The results are consistent with AP (1988). In Panel A, the impact of local noise trading on stock liquidity is much stronger for stocks with more institutional investors nearby. Panels C and D show that it
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is especially true for local institutional investors that are expected to be more active in information acquisition, that is, investors with a “small” investment style (Panel C1) or “transient” trading style (Panel
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D1). If we separate the sample by local institutional investors with a “large” investment style (Panel C2) or a “non-transient” trading style (Panel D2), there is no significant difference between the sub-samples.
d
We have shown that local noise trading has a stronger impact on stock liquidity for stocks with a high
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retail concentration. To determine whether the results are actually driven by some firm characteristics, in
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Panel B of Table IX, we report the correlations between indicators created by the number of local institutional investors and indicators created by firm characteristics. For example, the indicator (firm size) is 1 if the book size is above the sample median and 0 otherwise. The indicator (investment style: small) is 1 if the number of local small-style institutional investors is above the sample median and 0 otherwise. Other indicator variables are similarly defined. For indicators based on firm characteristics, we consider firm size, institutional ownership, price, P/E ratio, analyst coverage, return volatility, the “only-game-in-town” effect and local household income volatility. We find that indicators of the local number of institutional investors are not correlated with indicators
18 The use of a 250-mile radius follows Ivković and Weisbenner (2005) in that it can be seen as a plausible upper bound on the span of local information.
24
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of firm characteristics. The indicator (investment style: small) has a correlation of 0.01 with the indicator (size), 0.03 with the indicator (institutional ownership), 0.04 with the indicator (price), -0.01 with the indicator (P/E), -0.01 with the indicator (analyst coverage), -0.01 with the indicator (return volatility),
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-0.11 with the indicator (only-game-in-town), and a correlation of 0.07 with the indicator (local household
local institutional investors are not driven by firm characteristics.
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VI. Results on Firm Value
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income volatility). Therefore, we conclude that the sub-sample differences according to the number of
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In this section, we extend our analysis to determine whether local retail participation directly affects firm value. Fang, Noe and Tice (2009) investigate the relationship between stock liquidity and firm
M
performance. They document that liquid firms have better performance measured by the market-to-book ratio. They argue that the beneficial effect of liquidity is generated through a feedback channel where
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liquidity stimulates the entry of informed investors who make prices more informative to stakeholders.
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From AP (1988), if in equilibrium an increase in noise trading stimulates the entry and competition of
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informed traders, resulting in an increase in stock liquidity, we should expect a positive relationship between local retail participation and firm value. We report the results in Table X. Panel A is our main specification. Columns (1)-(3) are OLS regressions using the same set of control variables as in Table III. We control for local characteristics, such as population, household income, unemployment rate, gender, age and an urban dummy, as well as firm-specific variables, including leverage, market size, profitability, firm age, cash holding, tangibility, dividend yield, institutional ownership, stock turnover, past return and return volatility. All right-hand-side variables are lagged values. Columns (4)-(6) are instrumental variable (IV) regressions. In columns (4) and (5), we instrument stock illiquidity with the local white percentage. In column (6), we use both the local white percentage and the local education level as instruments. We report the F-statistic of the 25
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weak-identification test to test the presence of a weak instrument and the Hansen J statistic (p-value) of the over-identification test to justify the validity of instruments. For robustness checks, in Panel B of Table X, we run between-effects and firm fixed effects
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regressions of firm value on local equity market participation. Column (1) fits random effects models by using the between regression estimator. Given the unbalanced panel, column (2) performs a
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between-effects estimation with weighted least squares rather than the default OLS. Column (3) fits
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random-effects models using the population-averaged estimator. In column (4), we run OLS regressions with firm fixed effects and standard errors clustered at firm level. Here the “Full Specification” refers to
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the specification as in Panel A, column (3). For the sake of brevity, we do not report the control variables in the regression.
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The results are interesting. The variable of the local white percentage is significantly and positively related to firm value. A one-standard-deviation increase in the local white percentage increases the firm’s
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market-to-book ratio by 5% (column 3 of Panel A). The IV regressions confirm the findings of Fang, Noe
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and Tice (2009) that stock liquidity increases firm performance. This result is robust to the inclusion of
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industry and state fixed effects as well as credit rating fixed effects. The F-statistic of the weak-identification test is always above 10, and the Hansen J-statistic (p-value) is 0.028 (0.86). Therefore, the quality of instruments is very high. The result for between-effects regressions remains highly significant. Consistent with before, the magnitude of the fixed effect regression is reduced, but it remains significant with a t-statistic of 2.16, which means that the explanatory power of local white percentage mostly comes from the cross-sections instead of time variations. From Panel C to Panel E in Table X, we perform sub-sample comparisons. In Panel C, we separate the sample according to the size of book assets (columns (1) and (2)), and the level of institutional ownership (columns (3) and (4)). Panel D separates the sample according to the number of local institutional investors and their investment styles. Columns (1) and (2) are based on the number of local 26
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small-style institutional investors. Columns (3) and (4) are based on the number of local large-style institutional investors. Panel E separates the sample by the number of local institutional investors and by their trading styles. Columns (1) and (2) are based on the number of local transient institutional investors.
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Columns (3) and (4) are based on the number of local non-transient institutional investors. We perform a Chi-squared test to evaluate the differences in coefficients between the sub-samples. The standard errors
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are clustered at the firm level in all specifications.
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Consistent with the previous findings on stock liquidity, the effect of the local white percentage on firm value is stronger for small firms and firms with low institutional ownership. More importantly, the
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result is stronger for stocks surrounded by more local institutional investors who are supposed to be more active in information acquisition, i.e., investors with “small” investment or “transient” trading styles. Our
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results complement those of Fang, Noe and Tice (2009) in that the beneficial effect of liquidity on firm value is directly linked to the level of local noise trading and competition among local institutional
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VII. Conclusion
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investors.
In this paper, we investigate the relationship between local equity market participation of retail investors and stock liquidity. We use county-level racial composition as a proxy for the level of local retail trading. Our work is built upon existing studies that document striking racial differences in risky asset holdings and proximity investment behavior of individual investors. We provide evidence that there are large cross-sectional variations in racial compositions among U.S. counties, which allows us to exploit the cross-sectional differences such that we can investigate how local retail trading affects stock liquidity. We find that stocks headquartered in counties with a higher percentage of local white population are more liquid. The impact is stronger for stocks with a high retail concentration (i.e., small-size, low institutional ownership, low price, value, high volatility and scarce analyst coverage); it is also stronger for 27
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firms located in the “only-game-in-town” counties and in counties with more volatile household income streams. Our findings are consistent with microstructure models that predict a positive relationship between
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noise trading and stock liquidity. In particular, we provide evidence supporting the predictions of Admati and Pfleiderer (1988) that the impact of local retail participation on stock liquidity is stronger when there
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are more institutional investors located close by, especially local institutional investors with “small”
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investment styles and “transient” trading styles.
Finally, we document a positive effect of local retail participation on firm value, supporting the view
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that liquidity increases firm performance. Collectively, our results add to the understanding of how retail
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investor trading and local bias affect stock liquidity and firm value.
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Black, F., 1986, Noise, Journal of Finance, 41, 529–543. Becker, B. O., Zoran Ivković, and Scott Weisbenner, 2011, Local dividend clienteles, The Journal of Finance 66, 655-683.
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Blau, Francine D. and John W. Graham, 1990, Black-White Differences in Wealth and Asset Composition, Quarterly Journal of Economics, 105, 321-339.
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Brimmer, Andrew, 1998, Income, Wealth, and Investment Behavior in the Black Community, American Economic Review, 78, 151-155. Bushee, Brian, 1999, A Taxonomy of Institutional Investors: How Investor Behavior Matters, Investor Relations Quarterly 2, 13-18.
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Bushee Brian and Noe, Chris, 2000, Corporate Disclosure Practices, Institutional Investors, and Stock Return Volatility, Journal of Accounting Research, 171-202. Campbell, J. Y., 2006, Household Finance, Journal of Finance, 61, 1553–1604.
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Chordia, T., and A. Subrahmanyam, 2004, Order Imbalance and Individual Stock Returns: Theory and Evidence, Journal of Financial Economics, 72, 485–518.
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Choudhury, S., 2002, Racial and Ethnic Differences in Wealth Holdings and Portfolio Choices, Division of Economic Research, Social Security Administration Working Paper.
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Coval, J., Moskowitz, T., 1999. Home bias at home: Local equity preference in domestic portfolios. Journal of Finance 54, 2045-2073. Coval, J., Moskowitz, T., 2001. The geography of investment: Informed trading and asset prices. Journal of Political Economy 109, 811-841.
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Fang, V., Noe, T., and Tice, S., 2009, Stock Market Liquidity and Firm Value, Journal of Financial Economics 94, 150-169. Gaspar, J. M., Massa, M., and Matos, P., Shareholder Investment Horizons and the Market for Corporate Control, Journal of Financial Economics 76, 135-165. Glosten, L. R., and P. R. Milgrom, 1985, Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders, Journal of Financial Economics, 14, 71–100. Gopalan, R., Kadan, O. and Pevzner, M., Investment Decisions, Asset Liquidity and Stock Liquidity, working paper, 2009. Goyenko, R.Y., Holden, C.W. and Trzcinka, C.A., Do Liquidity Measures Measure Liquidity?, Journal of Financial Economics, forthcoming. Griffin, J. M., J. H. Harris, and S. Topaloglu, 2003, The Dynamics of Institutional and Individual Trading, Journal of Finance, 58, 2285–2320. Grinblatt, M., and M. Keloharju, 2000, The Investment Behavior and Performance of Various Investor Types: A Study of Finland’s Unique Data Set, Journal of Financial Economics, 55, 43–67. Grinblatt, M., and M. Keloharju, 2001a, How Distance, Language, and Culture Influence Stockholdings and Trades, Journal of Finance, 56, 1053–1073. 29
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Grinblatt, M., and M. Keloharju, 2001b, What Makes Investors Trade?, Journal of Finance, 56, 589–616. Grossman, S. J., 1976, On the Efficiency of Competitive Stock Markets Where Traders have Diverse Information, Journal of Finance, 31, 573-585. Grossman, S.J. and Stiglitz, J.E., 1980, “On the Impossibility of Informationally Efficient Markets”, American Economic Review 70, 393-408.
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Guiso, Luigi, Paola Sapienza, and Luigi Zingales, 2004, Does local financial development matter?, Quarterly Journal of Economics 119, 929-969. Hasbrouck, J., 2009, Trading Costs and Returns for US Equities: Estimating Effective Costs from Daily Data, Journal of Finance, forthcoming.
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Hong, Harrison, Jeffrey Kubik, and Jeremy Stein, 2008, The only game in town: Stock price consequences of local bias, Journal of Financial Economics 90, 20-37.
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Hong, H., Kubik, J., Stein, J., 2004, Social interaction and stock market participation, Journal of Finance 59, 137-163. Huberman, Gur, 2001, Familiarity breeds investment, Review of Financial Studies 14, 659-680.
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Hvidkjaer, Soeren, 2008, Small Trades and the Cross-Section of Stock Returns, Review of Financial Studies 21, 1123-1151.
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Ivković, Z., and S. J. Weisbenner, 2005, Local Does as Local Is: Information Content of the Geography of Individual Investors’ Common Stock Investments, Journal of Finance, 60, 267–306. Ivković, Z., James Poterba, and Scott Weisbenner, 2005, Tax-motivated trading by individual investors, American Economic Review 95, 1605-1630.
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Loughran, Tim, and Paul Schulz, 2005, Liquidity: Urban versus rural firms, Journal of Financial Economics 78, 341-374.
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Loughran, T., Schultz, P., 2004. Weather, stock returns, and the impact of localized trading behavior. Journal of Financial and Quantitative Analysis 39, 343-364. Kyle, A. S., 1985, Continuous Auctions and Insider Trading, Econometrica, 53, 1315–1335.
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Kumar, Alok and Lee, Charles, 2006, Retail Investor Sentiment and Return Comovements, Journal of Finance 61, 2451-2486. Kumar, Alok, 2009, Who Gambles in the Stock Market?, Journal of Finance 64, 1889-1933. Massa, Massimo, and Andrei Simonov, 2006, Hedging, familiarity and portfolio choice, Review of Financial Studies 19, 633-685. Mabry, Tristan, 1999, Black Investors Shy Away from Stocks, Wall Street Journal, May 14. Nofsinger, J. R., and R. W. Sias, 1999, Herding and Feedback Trading by Institutional and Individual Investors, Journal of Finance, 54, 2263–2295. Odean, T., 1999, Do Investors Trade Too Much?, American Economic Review, 89, 1279–1298. Odders-White, E. and Ready, M., 2006, Credit Ratings and Stock Liquidity, Review of Financial Studies, 19, 119-157. Pagano, M., and Roell, A., 1996, Transparency and liquidity: a comparison of auction and dealer markets with informed trading, Journal of Finance,51, 579–611. Roll, R., 1984, A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market, Journal of Finance, 39, 1127–1139. 30
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Seasholes, M., Zhu, N., 2009, Is There Information in the Local Portfolio Choices of Individuals, Journal of Finance, forthcoming. Shleifer, A., and R. W. Vishny, 1997, The Limits of Arbitrage, Journal of Finance, 52, 35–55. Shu, T., 2006, Trader Composition, Price Efficiency, and the Cross-Section of Stock Returns, working paper, University of Texas at Austin.
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Sophie Shive, 2010, “How do Local Investors Affect Prices? Power Outages as a Natural Experiment”, working paper. Yan X. and Zhang Z., 2009, Institutional Investors and Equity Returns: Are Short-term Institutions Better Informed?, Review of Financial Studies, Forthcoming.
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Zhu, N., 2002, The local bias of individual investors. Unpublished working paper, Yale University.
Appendix: Variable Definitions County Level Variables Local white percentage: the percentage of white population in each county. We only include the population aging from 35 to 85. The data comes from the US Census county population estimates datasets from 1990 to 2005. 31
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Log(local population): the natural log of the size of county population aging from 35 to 85. The data comes from the US Census county population estimates datasets from 1990 to 2005. Local male fraction: the percentage of male population in each county. We only include the population aging from 35 to 85. The data comes from the US Census county population estimates datasets from 1990 to 2005. Local senior fraction: the percentage of population more than 65 years old in each county. We only include the population aging from 35 to 85. The data comes from the US Census county population estimates datasets from 1990 to 2005. Log(local household income): the natural log of the median household income in each county. The data come from the US Census Bureau SAIPE (Small Area Income and Poverty Estimates) datasets from 1990 to 2005. Local unemployment rate: the historical rate of unemployment in each county. The data comes from the Bureau of Labor Statistics.
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Firm Specific Variables
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Retail trading fraction: the fraction of trading volume associated with retail investors. We infer the fraction of institutional trading volume from the CDA/Spectrum quarterly institutional holdings data. We first calculate the fraction of institutional trading volume using the following formula: N
j 1
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InstitutionalFractioni ,t
| IO
i , j ,t
IOi , j ,t 1 |
TOi ,t
,
where IOi , j ,t is institution j’s ownership of stock i for quarter t, calculated as institution j’s holdings of stock i divided by i’s shares outstanding at the end of quarter t. TOi ,t is the total turnover of stock i at quarter t. We then
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calculate the fraction of retail trading volume as:
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RetailFractioni ,t 1 InstitutionalFractioni ,t .
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Amihud illiquidity: the Amihud (2000) illiquidity measure averages over each day in year t the square root of the ratio of the absolute price change divided by daily dollar volume. It is calculated as:
Illiquidity i ,t
1 Dt
(1000 *
D ays t
|daily return| |daily do llar volum e|
),
where Dt is the number of days in year t.
C^bma (Gibbs estimate): the estimate of the coefficient ci in the following market-adjusted model:
pi ,t ci qi ,t i rm ,t ui ,t
where pi ,t is the log of the price and qi ,t is a trade direction indicator taking the values +1 or -1 for buys and sells respectively. rm ,t is the market return and ui ,t is the residual independent of qi ,t and rm ,t . The trade direction indicators qi ,t are unobserved because the model is estimated from daily CRSP data, and the coefficient estimates are obtained using the Gibbs sampler. C^roll (Roll’s model): the Roll (1984) measure of effective trading costs, estimated as the square root of the negative of the autocovariance of daily log returns, and set to zero when the sample autocovariance is positive. Gamma0, Gamma1 (Latent Factor Model): the intercept 0i and slope coefficient 1i from the latent common factor model given by the following equations: 32
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ci ,t 0i 1i zt ,
pi ,t ( 0i 1i zt )qi ,t i rm ,t ui ,t ,
where zt is the latent common factor.
jQ
N i , j , s P j , s N i , j , s 1 P j , s 1 N i , j , s 1 P j , s
N i , j , s 1 P j , s 1 N i , j , s P j , s
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CR i , s
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Urban dummy: a stock is defined as an urban stock if the company is headquartered less than 100miles from the center of one of the top thirty largest US cities according to the 2000 census. Market value of assets: stock price (data199) * shares outstanding (data25) + short term debt(data34) + long term debt(data9) + preferred stock liquidation value (data10) – deferred taxes and investment tax credits (data35). Market-to-book: market value of assets/book assets (data6) Firm leverage: (long term debt (data9) + short term debt (data34))/book assets (data6) Market cap: natural log of a firm’s market value of equity (data199*data25) Profitability: operating income before depreciation (data13)/book assets (data6) Firm age: natural log of the number of years since a firm appear in CRSP. Cash holding: cash and short-term investments (data1)/book assets(data6) Tangibility: net PPE (data8)/book assets (data6) Dividend yield: cash dividends per share (data26)/stock price (data199) Institutional ownership: fraction of institutional ownership, calculated from Thomason CDA/Spectrum institutional ownership Database (13F). Institutional turnover: We first define portfolio churn rate of institutional investors. It captures how frequently an investor rotates his positions on all the stocks of his portfolio. Investor-level portfolio information comes from Thomason CDA/Spectrum, a database of quarterly 13-F filings of money managers to the SEC. If we denote the set of companies held by investor i by Q, the churn rate of investor i at quarter s is:
2
te
jQ
,
where Pj , s and N i , j , s represent the price and number of shares of stock j held by investor i at quarter s. Let wi , j , t
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be the weight of investor j’s holding stock i held by institutional investors at quarter t. The investor turnover of stock i is the weighted average of the total portfolio churn rates of its investors over the previous four quarters: T u rn o veri , t
j S i
1 w i , j ,t 4
4
CR r 1
j ,t r 1
.
Past return: cumulative stock returns in the previous year. Return volatility: standard deviation of stock returns in the previous year. Analyst coverage: the total number of analysts covering the stock in a year. The data come from Thomson I/B/E/S database. It is 0 if there is no analyst coverage. State-fixed effects: dummy variables for states. Industry-fixed effects: two-digit sic dummies. Exchange dummies: dummy variables for NYSE, AMEX and NASDAQ respectively. Credit rating dummies: dummy variables for investment grade, junk and non-rated respectively. Table I Summary Statistics Panel A: Summary Statistics of Variables Panel A presents summary statistics of the main variables used in the subsequent analysis. We report the number of observations, 33
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the mean, the 5%-quantile, the 95%-quantile and the standard deviation. “Local” variables are defined at the county level. The data on county population characteristics come from the US Census Bureau population estimates program from 1990 to 2005. The measures on stock illiquidity are obtained from Joel Hasbrouck’s website. Stock returns and firm level accounting information are from CRSP and Compustat. The detailed definitions of each variable can be found in the Appendix. 5%-Quantile
95%-Quantile
Std. Dev.
58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471 58471
81.4% 13.03 47.4% 21.6% 10.66 5.1% 0.73 0.43 0.72 1.35 1.25 1.24 0.91 0.23 5.22 5.12 1.73 0.07 2.16 0.18 0.28 0.01 0.35 0.65 0.12 0.13
57.3% 11.40 45.4% 15.5% 10.24 2.6% 0 -0.47 0.02 0.18 0 0.14 0.15 0 2.28 1.97 0.44 -0.31 0.69 0.08 0.02 0 0.01 0.52 -0.61 0.05
96.5% 14.72 49.4% 28.1% 11.10 8.3% 1 0.96 3.12 4.66 4.69 4.41 2.52 0.63 8.93 8.74 5.08 0.27 3.43 0.68 0.77 0.04 0.81 0.81 1.21 0.24
11.9% 0.92 1.3% 4.2% 0.26 1.8% 0.44 0.45 1.15 1.56 1.66 1.49 0.77 0.21 2.01 2.07 1.71 0.19 0.81 0.22 0.23 0.02 0.26 0.10 0.57 0.06
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Mean
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Local white percentage Log(local population) Local male fraction Local senior fraction Log(local household income) Local unemployment rate Urban dummy Retail trading volume Amihud illiquidity C^bma (Gibbs estimate) C^roll (Roll’s model) Gamma0 (latent factor model) Gamma1 (latent factor model) Firm leverage Log(book value) Log(market cap) Market-to-book Profitability Log(firm age) Cash holding Tangibility Dividend yield Institutional ownership Institutional turnover Past return Return volatility
Number of Obs.
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Variables
Panel B: Correlation Matrix of Local Population Characteristics
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This panel presents the correlation matrix of local population characteristics. We report the correlations among local white percentage, log of local population, local male fraction, local senior fraction, log of local household income and local unemployment rate.
Local white percentage Local population Local male fraction Local senior fraction Local household income Local unemployment rate
Local white percentage
Local population
Local male fraction
Local senior fraction
Local household income
Local unemploym ent rate
1 -0.29 0.26 0.09 0.21 -0.30
1 0.05 -0.08 0.00 0.28
1 -0.61 0.52 -0.21
1 -0.46 0.23
1 -0.48
1
Table I (Cont’d) Panel C: Local White percentage by States This panel presents the summary statistics of local white percentage by states. We report the state name, the state code, the mean, the median, the min, the max and the standard deviation, in the descending order of the number of firm-year observations in each state. 34
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Median
Min
Max
Std. Dev.
Num
CA NY TX MA NJ PA IL FL OH MN CT CO GA MI VA MO WA MD WI NC TN OR IN UT OK AZ NV AL LA NH SC KS KY DE AR NE DC RI IA ID ME HI NM MS VT SD AK ND WV MT
0.765 0.784 0.799 0.911 0.830 0.873 0.786 0.860 0.822 0.915 0.894 0.903 0.638 0.839 0.719 0.714 0.860 0.672 0.891 0.765 0.757 0.911 0.863 0.956 0.840 0.937 0.872 0.703 0.646 0.978 0.792 0.924 0.845 0.815 0.899 0.902 0.325 0.938 0.947 0.973 0.984 0.306 0.932 0.594 0.976 0.972 0.837 0.978 0.941 0.976
0.755 0.700 0.777 0.934 0.838 0.914 0.729 0.872 0.811 0.914 0.892 0.904 0.557 0.884 0.783 0.787 0.853 0.759 0.918 0.761 0.771 0.903 0.898 0.954 0.832 0.936 0.867 0.680 0.715 0.980 0.826 0.937 0.835 0.817 0.948 0.893 0.321 0.930 0.942 0.976 0.985 0.283 0.932 0.562 0.978 0.974 0.836 0.980 0.941 0.976
0.562 0.517 0.660 0.678 0.554 0.518 0.692 0.715 0.724 0.868 0.859 0.834 0.443 0.590 0.425 0.502 0.810 0.305 0.768 0.600 0.539 0.861 0.752 0.933 0.793 0.926 0.815 0.556 0.351 0.958 0.558 0.704 0.815 0.773 0.707 0.876 0.313 0.892 0.923 0.957 0.969 0.234 0.903 0.440 0.963 0.958 0.816 0.970 0.939 0.976
0.982 0.985 0.966 0.983 0.985 0.996 0.990 0.980 0.989 0.982 0.984 0.986 0.972 0.987 0.894 0.975 0.966 0.970 0.993 0.927 0.980 0.985 0.989 0.985 0.921 0.947 0.933 0.926 0.903 0.993 0.906 0.968 0.970 0.846 0.985 0.972 0.351 0.987 0.980 0.984 0.993 0.452 0.974 0.848 0.987 0.986 0.852 0.982 0.942 0.976
0.084 0.106 0.057 0.075 0.084 0.130 0.096 0.058 0.069 0.025 0.020 0.053 0.139 0.125 0.147 0.152 0.041 0.191 0.084 0.077 0.130 0.033 0.083 0.014 0.025 0.005 0.034 0.078 0.181 0.007 0.091 0.047 0.037 0.022 0.094 0.027 0.011 0.031 0.016 0.008 0.006 0.073 0.015 0.117 0.007 0.008 0.011 0.004 0.001 0.000
10676 5542 5213 3395 2881 2714 2643 2303 2131 2011 1716 1503 1393 1197 1051 1023 1003 976 808 790 708 635 604 506 504 490 488 359 344 302 286 256 247 241 202 200 194 194 177 127 101 86 85 70 38 27 16 8 5 2
cr
us
an
M
d
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Mean
Ac ce p
California New York Texas Massachusetts New Jersey Pennsylvania Illinois Florida Ohio Minnesota Connecticut Colorado Georgia Michigan Virginia Missouri Washington Maryland Wisconsin North Carolina Tennessee Oregon Indiana Utah Oklahoma Arizona Nevada Alabama Louisiana New Hampshire South Carolina Kansas Kentucky Delaware Arkansas Nebraska District of Columbia Rhode Island Iowa Idaho Maine Hawaii New Mexico Mississippi Vermont South Dakota Alaska North Dakota West Virginia Montana
State Code
te
State Name
Table II Local Retail Investors and the Cross-section of Retail Trading Volume This table explores the impact of local retail investor characteristics on the cross-section of retail trading volume. We measure retail trading volume as the fraction of trading volume associated with retail investors. Because the number of trades by institutions and individuals is not publicly available, following Shu (2006) and Hvidkjaer (2008) we infer the fraction of 35
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institutional trading volume from the CDA/Spectrum quarterly institutional holdings data. We first calculate the fraction of institutional trading volume using the following formula: N
InstitutionalFractioni ,t
j 1
i , j ,t
IOi , j ,t 1 | ,
TOi ,t
IOi , j ,t is institution j’s ownership of stock i for quarter t, calculated as institution j’s holdings of stock i divided by i’s
shares outstanding at the end of quarter t.
ip t
where
| IO
TOi ,t is the total turnover of stock i at quarter t. We then calculate the fraction of
retail trading volume as:
cr
RetailFractioni ,t 1 InstitutionalFractioni ,t .
an
us
The dependent variable is the yearly average of retail fraction. Columns (1) and (2) are based on the full sample. Column (1) is the base-line specification. We control for state fixed effects, county-level characteristics, such as population, gender, age, household income, unemployment rate and an urban dummy, as well as firm specific variables including leverage, market-to-book, market capitalization, profitability, firm age, cash holding, tangibility and dividend yield. We further control for institutional ownership, institutional turnover, past return and return volatility. All variables on the right-hand side are lagged values. From column (2) we include industry fixed effects at two-digit SIC level and cluster the standard errors at firm level.
Ac ce p
te
d
M
Columns (3)-(6) are based on sub-samples where retail trading are more likely to be evident. Column (3) is for the sub-sample where the firm’s book asset is below the sample median. Column (4) is for the sub-sample where the firm’s institutional ownership is below the sample median. Column (5) is for the sub-sample where the maximum price in the previous year is below the sample median. Column (6) is for the sub-sample where the volatility of local household income is above the sample median. The detailed definitions of each variable can be found in the Appendix. ***, ** and * represent significance levels at 1%, 5% and 10% respectively using robust standard errors with t-statistics given in parentheses.
Table II (Cont’d) Full sample
Sub-sample Low institutional
High local household income
36
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(2)
Small size (3)
ownership (4)
Low price (5)
volatility (6)
Local white percentage
0.130*** (5.28)
0.140*** (3.32)
0.153*** (3.06)
0.206*** (3.94)
0.161*** (3.26)
0.267*** (4.57)
Log(local population)
-0.002 (-0.92) -0.370 (-1.21) -0.248*** (-2.99) 0.026* (1.81) -0.006*** (-3.17) 0.002 (0.23) -0.032*** (-3.38) 0.021*** (19.03) -0.013*** (-9.90) 0.006 (0.65) -0.009*** (-3.91) 0.063*** (6.56) -0.022** (-2.50) -1.068*** (-8.46) -0.519*** (-55.59) 0.102*** (4.67) -0.036*** (-11.71) 1.635*** (43.52) 0.484** (2.46) Y Y 0.2891 58471
-0.003 (-0.68) -0.451 (-0.85) -0.248* (-1.77) 0.002 (0.10) -0.006** (-2.07) 0.006 (0.55) -0.016 (-1.05) 0.019*** (12.02) -0.014*** (-5.95) 0.001 (0.04) -0.007* (-1.89) 0.054*** (3.56) -0.064*** (-3.37) -1.078*** (-7.29) -0.515*** (-31.65) 0.092*** (3.49) -0.032*** (-10.22) 1.486*** (30.20) 0.847** (2.27) Y Y Y Firm 0.3052 58471
0.003 (0.50) -1.028 (-1.50) -0.303* (-1.76) 0.000 (-0.02) -0.006* (-1.68) -0.004 (-0.28) -0.046** (-2.31) 0.017*** (9.33) -0.029*** (-6.38) 0.011 (0.81) -0.019*** (-3.53) 0.044*** (2.71) -0.048** (-2.06) -0.808*** (-4.03) -0.762*** (-32.24) 0.056* (1.94) -0.016*** (-4.24) 1.105*** (19.13) 1.098** (2.39) Y Y Y Firm 0.2284 29221
0.003 (0.53) -0.319 (-0.46) -0.041 (-0.24) -0.003 (-0.11) -0.003 (-0.75) 0.004 (0.27) -0.060*** (-3.16) 0.021*** (11.50) -0.048*** (-13.14) 0.001 (0.07) -0.014*** (-2.67) 0.076*** (4.49) -0.015 (-0.64) -0.818*** (-4.59) -1.008*** (-22.65) 0.072** (2.48) -0.017*** (-4.13) 1.170*** (19.41) 0.696 (1.43) Y Y Y Firm 0.2426 29173
-0.001 (-0.17) -0.992 (-1.52) -0.193 (-1.16) 0.034 (1.14) -0.004 (-1.10) 0.023* (1.75) -0.064*** (-3.65) 0.018*** (9.06) -0.019*** (-4.91) 0.010 (0.69) 0.002 (0.31) 0.064*** (3.83) -0.020 (-0.90) -0.571*** (-3.72) -0.729*** (-31.56) 0.039 (1.30) -0.033*** (-7.44) 1.151*** (20.20) 0.658 (1.47) Y Y Y Firm 0.2389 29159
0.014 (1.65) 0.356 (0.50) -0.289 (-1.42) 0.016 (0.46) -0.007* (-1.83) 0.014 (0.83) -0.041** (-1.96) 0.018*** (9.35) -0.016*** (-5.00) -0.004 (-0.26) -0.001 (-0.24) 0.040** (2.13) -0.071*** (-2.69) -1.021*** (-4.94) -0.489*** (-22.19) 0.105*** (3.11) -0.033*** (-7.82) 1.315*** (20.05) 0.164 (0.33) Y Y Y Firm 0.2995 29195
Firm leverage Market-to-book Log(market cap) Profitability Log(firm age) Cash holding Tangibility Dividend yield Institutional ownership
Past return
Ac ce p
Institutional turnover
Return volatility Const
Year-fixed effects State-fixed effects Industry-fixed effects Clustering Adjusted R-squared Number of Obs.
cr
us
Urban dummy
an
Local unemployment rate
M
Log(local household income)
d
Local senior fraction
te
Local male fraction
ip t
(1)
Table III Local Equity Market Participation and Stock Illiquidity: Main Results This table presents our main results of the impact of local equity market participation on stock liquidity. The dependent variable is the Amihud illiquidity measure. It is defined as the square root of the average daily price impact of trading in each year. Column (1) is the base-line specification. In column (2) we add more controls on local characteristics including local male 37
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fraction, local senior fraction, local household income, local unemployment rate and an urban dummy. From column (3) we cluster the standard errors at firm level. We add more firm level controls in column (4). In column (5) we add state fixed effects. In column (6) we further include industry fixed effects (two-digit sic) and credit rating fixed effects (investment grade/junk/non-rated). ***, ** and * represent significance levels at 1%, 5% and 10% respectively using robust standard errors with t-statistics given in parentheses. (4) -0.304*** (-3.69)
-0.007* (-1.74)
-0.009** (-2.22) -0.822* (-1.91) 0.020 (0.15) 0.071*** (3.02) 0.011*** (3.43) -0.012 (-1.18) 0.272*** (12.81) -0.009*** (-4.81) -0.366*** (-112.41) -0.125*** (-5.45) 0.066*** (14.48) -0.339*** (-16.14)
-0.009 (-1.08) -0.822 (-0.89) 0.020 (0.07) 0.071 (1.50) 0.011* (1.87) -0.012 (-0.59) 0.272*** (7.22) -0.009** (-2.53) -0.366*** (-50.49) -0.125*** (-3.48) 0.066*** (7.19) -0.339*** (-9.49)
-0.007 (-0.83) -0.570 (-0.63) -0.005 (-0.02) 0.064 (1.38) 0.010* (1.69) -0.006 (-0.30) 0.283*** (7.57) 0.002 (0.60) -0.331*** (-42.33) 0.044 (1.23) 0.059*** (6.41) -0.311*** (-8.53) -0.072** (-2.01) -0.338 (-1.02) -0.375*** (-11.56) -0.755*** (-9.77) -0.162*** (-21.27) -0.132 (-1.08) Y Y Firm 0.4680 58471
Local male fraction Local senior fraction Log(local household income) Local unemployment rate
0.271*** (12.80) -0.009*** (-4.84) -0.366*** (-112.90) -0.127*** (-5.55) 0.067*** (14.74) -0.336*** (-16.18)
Market-to-book Log(market cap) Profitability Log(firm age)
te
Cash holding
d
Firm leverage
M
Urban dummy
Tangibility
Ac ce p
Dividend yield Institutional ownership Institutional turnover Past return
Return volatility
Exchange dummies Year-fixed effects State-fixed effects Industry-fixed effects Credit rating-fixed effects Clustering Adjusted R-squared Number of Obs.
Y Y 0.4547 58471
Y Y 0.4548 58471
Y Y Firm 0.4548 58471
(5) -0.620*** (-5.72)
(6) -0.624*** (-5.95)
-0.011 (-0.96) 1.278 (0.92) 0.124 (0.33) 0.077 (1.27) 0.003 (0.47) -0.061** (-2.27) 0.279*** (7.44) 0.003 (0.84) -0.332*** (-42.74) 0.045 (1.25) 0.058*** (6.24) -0.308*** (-8.33) -0.073** (-2.00) -0.320 (-0.98) -0.359*** (-11.11) -0.758*** (-9.82) -0.162*** (-21.27) -0.121 (-0.99) Y Y Y Firm 0.4699 58471
-0.011 (-1.04) 1.466 (1.11) 0.163 (0.45) 0.118** (2.02) 0.004 (0.64) -0.084*** (-3.24) 0.276*** (6.78) 0.021*** (5.45) -0.393*** (-46.53) 0.136*** (3.70) 0.040*** (4.48) -0.243*** (-6.54) -0.034 (-0.76) -0.715** (-2.02) -0.252*** (-7.77) -0.703*** (-9.43) -0.146*** (-20.06) 0.125 (1.03) Y Y Y Y Y Firm 0.4903 58471
ip t
(3) -0.321*** (-3.82)
cr
(2) -0.321*** (-8.37)
us
Controls Log(local population)
(1) -0.348*** (-11.23)
an
Local white percentage
Table IV Local Equity Market Participation and Stock Illiquidity: Robustness Checks This table provides robustness checks on the relationship between local equity market participation and stock illiquidity. Panel A is for sub-sample analysis by county population and sample period. In Panel B we employ different measures of stock illiquidity. In Panel C we use alternative measures on local equity market participation. Panel D presents the results of between-effects and 38
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firm fixed effects regressions. In Panel E we separate the sample by firm age and examine stocks never moving headquarters during the sample period. We always utilize the “Full specification” as in column (6) of Table III and cluster the standard errors at firm level. For brevity we do not report the control variables.
Panel A: Sample Separation by County Population and Sample Period
Y Firm 29478
cr
Y Firm 28993
Y Firm 28343
Y Firm 30128
an
Full Specification Clustering Number of Obs.
Separate by sample period before 1998 after 1998 (3) (4) -0.742*** -0.452*** (-4.31) (-4.17)
us
Local white percentage
Separate by local population above median blow median (1) (2) -0.678*** -0.566*** (-3.45) (-3.92)
ip t
In this panel we separate the sample by local population and sample period. In columns (1) and (2) we separate the sample by the size of local population (above/below median). In columns (3) and (4) we analyze the subsamples by sample period (before 1998/after 1998).
Panel B: Other Illiquidity Measures
M
In this panel we use different measures of stock illiquidity as dependent variables. We employ four measures from column (1) to column (4). C^bma is the coefficient on the change in the trade direction indicator in the basic market-adjusted model. C^roll is the Roll (1984) trading cost estimator excluding quote mid-points. Gamma0 and Gamma1 are the intercept and slope coefficient in a latent common factor model to estimate the effective costs of trading. We obtain these measures from Joel Hasbrouck’s website. Gamma1 (4) -0.182*** (-3.65)
Y Firm 58471
Y Firm 58471
Y Firm 58471
Y Firm 58471
d
Gamma0 (3) -0.583*** (-4.61)
Ac ce p
Full Specification Clustering Number of Obs.
C^roll (2) -0.515*** (-3.83)
te
Local white percentage
C^bma (1) -0.651*** (-4.95)
Table IV (Cont’d) Panel C: Alternative Measures on Local Equity Market Participation In this panel we use alternative measures on local equity market participation. In columns (1) and (2), local white percentage is defined as log(local white population*local average income/total local book assets). In columns (3) and (4), local white 39
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percentage is defined as log(local white population*local average income/total local market assets). The dependent variable in column (1) and (3) is the Amihud illiquidity measure. The dependent variable in column (3) and (4) is the coefficient on the change in the trade direction indicator in the basic market-adjusted model C^bma.
Y Firm 58471
Y Firm 58471
Y Firm 58471
Y Firm 58471
cr
Full Specification Clustering Number of Obs.
Normalized by market assets Amihud Illiquidity C^bma (3) (4) -0.033*** -0.036*** (-6.05) (-5.42)
ip t
Local white percentage
Normalized by book assets Amihud Illiquidity C^bma (1) (2) -0.028*** -0.031*** (-5.00) (-4.50)
us
Panel D: Between-Effects and Firm Fixed-effects Regression
firm-fixed effects (4) -0.452** (-2.59)
Y 58471
Y 58471
Y Firm 58471
M
Y 58471
population average (3) -0.498*** (-6.28)
te
Full Specification Clustering Number of Obs.
between effects: wls (2) -0.627*** (-5.76)
d
Local white percentage
between effects (1) -0.709*** (-5.51)
an
This panel performs between effects and firm fixed effects regression of stock illiquidity on local equity market participation. The dependent variable is Amihud illiquidity. Column (1) fits random-effects models by using the between regression estimator. Given the unbalanced panel, column (2) performs between effects estimation with weighted least squares rather than the default OLS. Column (3) fits random-effects models using the population-averaged estimator. In column (4) we run OLS regressions with firm fixed effects and standard errors clustered at firm level.
Panel E: Sample Separation by Firm Age and Firms Never Moving Headquarters
Ac ce p
In this panel, column (1) is for the sub-sample with firm age more than 10 years. Column (2) is for the sub-sample with firm age less than 10 years. Columns (3) and (4) are based on the firms that never move headquarters during the sample period. The dependent variable in columns (1)-(3) is the Amihud illiquidity measure while in column (4) it is the Gibbs estimate of C^bma.
Local white percentage Full Specification Clustering Number of Obs.
Separate by firm age more than less than 10 years 10 years (1) (2) -0.683*** -0.510*** (-4.29) (-4.35) Y Firm 24166
Y Firm 34305
Firms never moving headquarters Amihud illiquidity C^bma (3) -0.731 *** (-6.11)
(4) -0.754*** (-5.09)
Y Firm 47145
Y Firm 47145
Table V Local Equity Market Participation and Stock Illiquidity: Sub-sample Comparisons In this table we compare the effects of local equity market participation on stock illiquidity in different sub-samples. We separate the sample by book size, institutional ownership, price, P/E ratio, analyst coverage, return volatility, the “only-game-in-town” effect and local household income volatility. We always utilize the “full specification” as in column (6) of Table III and cluster the standard errors at firm level. For brevity we do not report the control variables. We perform Chi-squared 40
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test to evaluate the differences in coefficients between sub-samples. ***, ** and * represent significance levels at 1%, 5% and 10% respectively.
Panel A: Separate by Size and Institutional Ownership In Panel A, we separate the full sample by the size of book assets (columns (1) and (2), above median/below median), and the level of institutional ownership (columns (3) and (4), above median/below median).
Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
Y Firm 29236
Y Firm 29235
ip t
Y Firm 29236
us
Local white percentage
Separate by institutional ownership above median below median (3) (4) -0.116** -0.797*** (-2.35) (-4.89)
cr
Separate by book size above median below median (1) (2) -0.189*** -0.665*** (-3.89) (-4.56)
9.62***
Y Firm 29235
16.57***
an
Panel B: Separate by Price and P/E-ratio
M
In Panel B, we separate the full sample by the maximum of stock price in the previous year (columns (1) and (2), above median/below median), and the price earnings ratio (columns (3) and (4), above median/below median).
Firm Y 29240
Firm Y 29231
Separate by P/E above median below median (3) (4) -0.387*** -0.759*** (-4.39) (-5.46) Firm Y 29032
18.21***
Firm Y 29174 7.76***
Ac ce p
Clustering Full Specifications Number of Obs. Chi-square test: difference in coefficients
te
Local white percentage
d
Separate by Price above median below median (1) (2) -0.059* -0.713*** (-1.73) (-4.67)
Table V (Cont’d) Panel C: Separate by Analyst Coverage and Return Volatility In Panel C, we separate the full sample by the number of analyst coverage (columns (1) and (2), above 1/below 1), and return volatility (columns (3) and (4), above median/below median). 41
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Separate by Analyst Coverage more than 1 less than 1 (1) (2)
Clustering Full Specifications Number of Obs. Chi-square test: difference in coefficients
-0.112*** (-2.60)
-0.780*** (-5.08)
-0.740*** (-5.67)
-0.480*** (-4.13)
Firm Y 35912
Firm Y 22559
Firm Y 29236
Firm Y 29235
18.44***
ip t
Local white percentage
Separate by Return Volatility above median below median (3) (4)
3.32*
cr
Panel D: Separate by Only-game-in-town Effects and Local Household Income Volatility
us
In Panel D, we separate the full sample by the only-game-in-town effects (columns (1) and (2), above median/below median), and the local household income volatility (column (3) and (4), above median/below median). The only-game-in-town effect is measured as log(county population*average income/county book assets). Local household income volatility is calculated as the volatility of the median county household income during the sample period.
d
Firm Y 29212
4.16**
Separate by Local Household Income Volatility above median below median (3) (4)
-0.186 (-1.27)
-0.766*** (-5.22)
-0.160 (-0.66)
Firm Y 29193
Firm Y 29214
Firm Y 29171 4.64**
Ac ce p
te
Clustering Full Specifications Number of Obs. Chi-square test: difference in coefficients
-0.627*** (-3.64)
M
Local white percentage
an
Separate by Only-game-in-town Effects above median below median (1) (2)
Table VI Local Equity Market Participation and Stock Illiquidity: Firms Moving Headquarters In this table we analyze the change of stock illiquidity and the change of local equity market participation for the firms that re-allocated headquarters during the sample period. Historical firm locations come from Compact Disclosure. We denote a firm as a mover if the location of headquarter in year t is in different counties from its location in year t-1. The dependent variable is the difference of stock illiquidity between year t+1 and year t-1. The right-hand side variables are also changes from year t-1 to 42
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year t+1. The dependent variable in column (1) and (2) is Amihud illiquidity, while it is C^bma, C^Roll, Gamma0 and Gamma1 respectively from column (3) to column (6). Year, state and industry fixed effects are always included. ***, ** and * represent significance levels at 1%, 5% and 10% respectively using robust standard errors with t-statistics given in parentheses. Change in Stock Illiquidity
Amihud Illiquidity (1) (2)
C^bma (3)
C^Roll (4)
Gamma0 (5)
Gamma1 (6)
-0.909*** (-2.79)
-0.970*** (-2.91)
-1.186*** (-2.67)
-1.161** (-2.13)
-0.749 (-1.58)
-0.772* (-1.79)
Change in Log(local population)
-0.065** (-1.96) 0.165 (0.05) -1.095 (-1.01) 0.229 (1.08) 0.025 (0.89) -0.183 (-1.61) 0.226 (1.01) -0.465*** (-8.87) 0.088*** (3.63) 0.317** (2.25) 0.002 (0.01)
-0.080** (-2.43) 2.659 (0.84) -0.904 (-0.82) 0.227 (1.08) 0.025 (0.89) -0.181* (-1.72) 0.205 (0.92) -0.500*** (-9.20) 0.064** (2.36) 0.340** (2.42) 0.111 (0.48) 0.771* (1.92) -6.619*** (-3.08) 0.378 (1.56) -0.321 (-0.89) 0.142*** (2.67) 1.212* (1.80) 1.443*** (3.54) Y Y 0.4552 878
-0.077* (-1.81) 5.012 (1.06) -0.622 (-0.40) -0.080 (-0.28) 0.011 (0.27) -0.326* (-1.87) 0.718*** (2.73) -0.563*** (-7.58) 0.023 (0.76) 0.105 (0.53) -0.042 (-0.14) 0.308 (0.50) -8.956*** (-2.97) 0.221 (0.66) -0.591 (-1.27) 0.158** (2.50) 2.100** (2.30) 0.333 (0.74) Y Y 0.4254 878
-0.155*** (-2.76) 0.042 (0.12) -0.873 (-0.15) 1.165 (0.58) 0.045 (0.91) -0.220 (-0.99) 0.441 (1.42) -0.632*** (-6.77) 0.030 (0.87) -0.124 (-0.49) -0.354 (-0.86) -0.031 (-0.05) -6.511*** (-2.92) 0.219 (0.50) -0.687 (-1.21) 0.236** (2.59) 1.084 (1.04) 1.486*** (2.66) Y Y 0.3834 878
-0.056 (-1.26) 2.002 (0.40) -2.132 (-1.35) -0.224 (-0.79) -0.004 (-0.09) -0.252 (-1.41) 0.659** (2.41) -0.559*** (-7.78) 0.054 (1.48) 0.077 (0.37) 0.036 (0.11) 0.763 (1.30) -9.304*** (-3.33) 0.524 (1.65) -0.454 (-0.99) 0.119* (1.88) 2.564*** (2.87) 0.840* (1.77) Y Y 0.4093 878
-0.051 (-1.19) 5.635 (1.28) 1.449 (1.29) 0.203 (0.87) 0.029 (0.90) -0.093 (-0.74) -0.036 (-0.17) -0.038 (-0.67) -0.024 (-1.04) -0.308 (-1.36) -0.125 (-0.38) -1.134*** (-2.78) -1.387 (-0.93) -0.503** (-1.97) -0.166 (-0.53) 0.007 (0.11) 1.441** (2.02) -0.872** (-2.27) Y Y 0.2636 878
Change in firm leverage Change in Log(market cap) Change in market-to-book Change in profitability Change in cash holding Change in tangibility Change in dividend yield
Ac ce p
Change in institutional ownership Change in institutional turnover Change in yearly return
Change in return volatility Const
Exchange Dummies Year & State & Industry-fixed Effects Adj. R-squared Number of Obs.
0.537 (1.60) Y Y 0.4203 893
cr
us
Change in urban dummy
an
Change in local unemployment rate
M
Change in Log(local household income)
d
Change in local senior fraction
te
Change in local male fraction
ip t
Change in local white percentage
Table VII Local Equity Market Participation and Stock Illiquidity: Additional Evidence This table provides additional evidence on the effect of local equity market participation on stock illiquidity. We relate local education level and local white percentage with stock illiquidity. Panel A runs county-level regressions of local education level on local population characteristics. We measure local education level as the percentage of population completing college in each county. The data on county education level come from the Economic Research Service in the US Department of Agriculture. 43
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In Panel B, we examine the effects of local education level and local white percentage on stock illiquidity. The dependent variables in columns (1) and (2) are the Amihud illiquidity measure. The dependent variables in columns (3) and (4) are the Gibbs estimate of trading costs C^bma. In columns (2) and (4) we include both local educational level and local white percentage in the regression. We always utilize the “Full specification” as in column (6) of Table III and cluster the standard errors at firm level. For brevity we do not report the control variables.
ip t
In Panel C, we perform the analyses at the county level. Specifically, for each county, we calculate the average illiquidity and average firm characteristics across all the firms headquartered in the county. There are 5399 county-year observations in the regressions. The dependent variable is the average firm illiquidity in the county. ***, ** and * represent significance levels at 1%, 5% and 10% respectively using robust standard errors with t-statistics given in parentheses.
(2)
0.044*** (8.78)
0.049*** (10.03)
Log(local population)
Local senior fraction
Local unemployment rate
te
d
0.234*** (54.85) 0.0147 5382
M
Log(local household income)
Year fixed effects State-fixed effects R-squared Number of Obs.
(4)
0.072*** (14.99)
0.071*** (9.90) -0.003*** (-5.23) -0.444*** (-6.05) -0.126*** (-8.17) 0.013*** (4.17) -0.001*** (-4.03) 0.399*** (9.07) Y Y 0.6964 5382
an
Local male fraction
Const
(3)
us
Local white percentage
(1)
cr
Panel A: Local Education Level and Local Population Characteristics
0.241*** (51.40) Y 0.0564 5382
0.252*** (51.36) Y Y 0.6801 5382
Ac ce p
Panel B: Local Education Level and Stock Illiquidity
Local education level
Amihud Illiquidity (2)
(3)
(4)
-1.085*** (-3.72)
-0.342 (-1.07) -0.570*** (-4.95)
-0.829** (-2.22)
0.033 (0.08) -0.661*** (-4.40)
Y Firm 58277
Y Firm 58277
Y Firm 58277
Y Firm 58277
Local white percentage Full Specifications Clustering Number of Obs.
C^bma
(1)
Table VII (Cont’d) Panel C: County-Level Analysis
Local white percentage
(1)
(2)
(3)
(4)
-0.208*** (-3.79)
-0.340*** (-4.10)
-0.360*** (-3.30)
-0.334*** (-3.12)
44
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0.005 (0.44)
-0.001 (-0.10) -2.122* (-1.69) 0.755** (2.21) 0.083 (1.25) -0.015** (-2.23) -0.032 (-1.17)
0.004 (0.35) -2.003 (-1.63) 0.699** (2.08) 0.067 (0.99) -0.016** (-2.36) -0.021 (-0.81)
0.434*** (4.08) 0.003 (0.32) -0.397*** (-34.06) 0.113 (0.96) 0.054*** (3.33) -0.354*** (-3.23)
0.445*** (4.08) 0.007 (0.69) -0.403*** (-32.38) 0.073 (0.62) 0.040** (2.25) -0.365*** (-3.31)
0.430*** (3.97) 0.008 (0.75) -0.406*** (-32.28) 0.096 (0.80) 0.044** (2.43) -0.374*** (-3.37)
0.388*** (3.58) 0.024* (1.95) -0.369*** (-26.73) 0.371*** (2.97) 0.054*** (2.83) -0.370*** (-3.41) 0.031 (0.36) -1.922* (-1.65) -0.235*** (-3.40) -0.538** (-2.29) -0.286*** (-9.41) 0.645 (1.55) Y Y 0.554 5399
Local male fraction Local senior fraction Log(local household income) Local unemployment rate
Market-to-book Log(market cap) Profitability Log(firm age) Cash holding
an
Average firm characteristic Firm leverage
us
Urban dummy
M
Tangibility Dividend yield
Return volatility
Ac ce p
Year-fixed effects State-fixed effects Adjusted R-squared Number of Obs.
te
Past return
d
Institutional ownership Institutional turnover
ip t
0.007 (0.79)
cr
County characteristics Log(local population)
Y 0.517 5399
Y Y 0.536 5399
Y Y 0.538 5399
Table VIII Local Equity Market Participation and Stock Illiquidity: Retail Investor Channel In this table, we strengthen the argument that local equity market participation affects stock liquidity through the retail investor channel. Specifically, we use an instrumental variable regression approach to link stock illiquidity with the measure of retail trading fraction as constructed in Table II, where we instrument the retail trading fraction by the local white percentage. We present the second-stage regression results. In columns (1)-(3), we report the results for the subsample of firms with high retail concentration, such as small firms, firms with low institutional ownership and stocks with low price. In columns (4)-(6), we 45
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report the results for the subsample of firms with low retail concentration, such as large firms, firms with high institutional ownership and stocks with high price. The procedure for the sample splits is the same as before. All specifications include year, industry, exchange and credit rating fixed effects.
Log(local household income) Local unemployment rate Urban dummy Firm leverage Market-to-book Log(market cap) Profitability Log(firm age)
te
Cash holding
Ac ce p
Tangibility Dividend yield
Institutional ownership Institutional turnover Past return
Return volatility
Full Specifications Clustering F-statistics (First-stage) Number of Obs.
ip t -0.001 (-0.07) -0.010 (-0.19) -0.015 (-0.98) -0.661 (-0.65) -0.662 (-1.24) 0.017 (0.70) 0.048 (0.92) 0.021*** (4.35) -0.097** (-2.36) -0.189*** (-4.10) 0.023 (1.58) -0.091* (-1.84) -0.074 (-1.16) -0.975 (-1.48) -0.497* (-1.87) 0.492 (0.75) -0.104*** (-2.81) 1.472 (1.04) Y Firm 2.97 29,298
cr
-0.000 (-0.02) 0.139 (0.98) -0.012 (-0.75) -0.090 (-0.03) -0.239 (-0.33) -0.027 (-0.43) 0.085 (0.78) 0.072*** (2.60) -0.750*** (-26.17) 0.093 (1.32) 0.077** (2.35) 0.060 (0.66) -0.068 (-0.62) -1.281 (-1.29) -3.383*** (-3.16) -0.346** (-2.47) -0.321*** (-5.82) 5.043*** (3.25) Y Firm 10.87 29,140
us
Local senior fraction
-0.018 (-0.53) -0.038 (-0.37) -0.032 (-0.97) -1.086 (-0.52) -0.677 (-1.02) 0.003 (0.06) 0.369* (1.87) 0.063** (1.98) -0.012 (-0.10) -0.340 (-1.29) 0.067 (1.02) -0.075 (-0.64) -0.152 (-1.02) -2.642 (-1.25) -1.335 (-1.21) 1.267 (0.67) -0.210 (-1.34) 5.038 (0.96) Y Firm 1.01 29,250
0.011 (0.44) -0.006 (-0.05) -0.010 (-0.76) 1.939 (0.68) 0.323 (0.46) -0.115** (-2.03) 0.179** (2.04) 0.092*** (4.29) -0.739*** (-16.58) 0.122* (1.95) 0.010 (0.47) 0.042 (0.46) -0.037 (-0.38) -1.628 (-1.48) -5.133*** (-4.61) -0.154 (-1.14) -0.271*** (-9.59) 4.518*** (3.80) Y Firm 14.80 29,173
an
Local male fraction
0.005 (0.18) -0.019 (-0.14) -0.023 (-1.31) -0.429 (-0.15) -0.558 (-0.74) -0.127* (-1.91) 0.240** (2.49) 0.177*** (6.50) -0.961*** (-21.21) 0.371*** (5.48) -0.021 (-0.86) 0.122 (1.43) -0.262* (-1.84) -2.439 (-1.64) -3.135*** (-2.63) -0.147 (-0.99) -0.197*** (-5.87) 4.996*** (3.11) Y Firm 8.94 29,221
M
Controls Log(local population)
Low Retail Concentration Large High Inst. High Size Ownership Price (4) (5) (6) -3.398 -1.429 -0.546 (-0.98) (-1.38) (-1.49)
d
Retail trading fraction (instrumented by local white percentage)
High Retail Concentration Small Low Inst. Low Size Ownership Price (1) (2) (3) -4.532*** -3.981*** -4.469*** (-2.96) (-3.70) (-3.09)
0.006 (0.97) -0.007 (-0.28) -0.002 (-0.47) -0.395 (-0.87) -0.137 (-0.91) -0.011 (-0.99) 0.019 (0.93) 0.004 (1.09) -0.066*** (-6.54) 0.030 (1.51) 0.014*** (4.12) 0.006 (0.24) -0.003 (-0.14) -0.517 (-1.38) -0.370*** (-2.92) -0.116 (-0.66) -0.015*** (-3.92) 0.039 (0.07) Y Firm 4.98 29,331
Table IX Market Microstructure Explanations: Link to Admati and Pfleiderer (1988) This table aims to offer market microstructure explanations on the effects of local equity market participation on stock illiquidity. In particular we link our results to Admati and Pfleiderer (1988) by separating the sample by the number of local institutional investors. Since CDA/Spectrum 13F does not provide the locations of institutional investors, we collect the zip-codes of the locations of money managers from their SEC filings from Morningstar Document Research. The corresponding values of latitude and longitude are obtained from the Gazetteer Files of Census 2000. For each firm-year, we count the number of 46
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institutional investors which are located within a 250-mile radius around the firm’s headquarter. In Panels A, C and D, the dependent variable in columns (1) and (2) is the Amihud illiquidity. The dependent variable in column (3) and column (4) is the Gibbs estimate of trading costs C^bma. We always utilize the “Full specification” as in column (6) of Table III and cluster the standard errors at firm level. For brevity we do not report the control variables. We perform Chi-squared test to evaluate the differences in coefficients between sub-samples. ***, ** and * represent significance levels at 1%, 5% and 10% respectively.
ip t
Panel A: Separate by the Number of Local Institutional Investors: Overall
below median (4)
-0.425** (-2.58) Y Firm 27494
-0.974*** (-4.38) Y Firm 30380
-0.289 (-1.45) Y Firm 27494
3.64**
5.66**
M
Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
-0.857*** (-5.07) Y Firm 30380
an
Local white percentage
C^bma
above median (3)
us
Amihud Illiquidity above median below median (1) (2)
cr
In Panel A, we separate the full sample by the number of local institutional investors (above median/below median), where we include all local institutional investors with different investment and trading styles.
Panel B: Correlations with Separation Indicator Based on Firm Characteristics
Indicator by the number of local institutional investors of different styles (above median 1, below median 0) Investment style: Investment style: Trading style: Trading style: Small Large Transient Non-transient 0.01 0.04 -0.06 0.00 0.03 0.04 -0.02 0.01 0.04 0.05 -0.02 0.00 -0.01 0.02 -0.04 0.01 -0.01 0.00 -0.03 -0.03 -0.01 -0.07 0.06 -0.04 -0.11 -0.02 0.07 -0.12 0.07 -0.15 0.07 -0.04
Ac ce p
Indicator by firm characteristics (above median 1, below median 0)
te
d
Panel B reports the correlations between indicators created by the number of local institutional investors and indicators created based on firm characteristics. We distinguish institutional investors by their investment (small/large) and trading styles (transient/non-transient). The style classifications are obtained from Brian Bushee’s website. For example, the indicator (size) is 1 if book size is above sample median and 0 otherwise. The indicator (investment style: small) is 1 if the number of local small-style institutional investors is above sample median and 0 otherwise. Other indicator variables are similarly defined.
Size Institutional ownership Price P/E Analyst coverage Return volatility Only-game-in-town Local household income volatility
Table IX (Cont’d) Panel C: Sample Separation by Local Institutional Investors: Investment Styles In Panel C, we separate the full sample by the number of local institutional investors (above median/below median) based on their investment styles (small/large). Panel B1 is based on the number of local institutional investors with investment style toward small stocks. Panel B2 is based on the number of local institutional investors with investment style toward large stocks. The style classifications are obtained from Brian Bushee’s website. 47
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Panel C1: Separate by the Number of Local Institutional Investors: (Investment Style: Small)
Full Specifications Number of Obs. Chi-square test: difference in coefficients
C^bma above median (3) -1.038*** (-4.70) Y 29758
below median (4) -0.162 (-0.68) Y 28028 7.20***
ip t
Local white percentage
Amihud Illiquidity above median below median (1) (2) -0.927*** -0.349* (-5.49) (-1.88) Y Y 29758 28028 5.32**
Panel C2: Separate by the Number of Local Institutional Investors: (Investment Style: Large)
cr
below median (4) -0.504*** (-2.76) Y 26565
0.26
an
Full Specifications Number of Obs. Chi-square test: difference in coefficients
C^bma
above median (3) -0.638*** (-3.26) Y 31143
us
Local white percentage
Amihud Illiquidity above median below median (1) (2) -0.723*** -0.528*** (-4.81) (-3.53) Y Y 31143 26565 0.90
Panel D: Sample Separation by Local Institutional Investors: Trading Styles
M
In Panel C, we separate the full sample by the number of local institutional investors (above median/below median) based on their trading styles (transient/non-transient). Panel C1 is based on the number of local transient institutional investors. Panel C2 is based on the number of local non-transient institutional investors. The style classifications are from Brian Bushee’s website.
Panel D1: Separate by the Number of Local Institutional Investors: (Trading Style: Transient)
Local white percentage
Ac ce p
Full Specifications Number of Obs. Chi-square test: difference in coefficients
te
d
Amihud Illiquidity above median below median (1) (2) -0.757*** -0.242 (-5.77) (-1.24) Y Y 31376 26212 4.83**
C^bma above median (3) -0.745*** (-4.38) Y 30380
below median (4) -0.289 (-1.45) Y 27494 5.66**
Panel D2: Separate by the Number of Local Institutional Investors: (Trading Style: Non-transient)
Local white percentage
Full Specifications Number of Obs. Chi-square test: difference in coefficients
Amihud Illiquidity above median below median (1) (2) -0.818*** -0.548*** (-4.78) (-3.36) Y Y 30397 27477 1.39
C^bma above median (3) -0.932*** (-4.11) Y 30380
below median (4) -0.428** (-2.19) Y 27494 3.02*
Table X Local Equity Market Participation, Stock Illiquidity and Firm Value This table presents the results of firm value on local equity market participation and stock illiquidity. The dependent variable is firm’s market-to-book ratio. Panel A is our main specification. Columns (1)-(3) are OLS regressions using the same set of control variable as in Table 48
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III. We control for local characteristics, such as population, household income, unemployment rate, gender, age and an urban dummy, as well as firm specific variables including leverage, market size, profitability, firm age, cash holding, tangibility, dividend yield, institutional ownership, stock turnover, past return and return volatility. All right-hand side variables are lagged values. Columns (4)-(6) are instrumental variables (IV) regressions. In columns (4) and (5) we instrument stock illiquidity with local white percentage. In column (6) we use both local white percentage and local education level as instruments. We report the
ip t
F-statistic of the weak-identification test to test the presence of weak instrument and the Hansen J statistic (p-value) to justify the validity of instruments. Panel B is for robustness checks. We run between-effects and firm fixed effects regression of firm value on local equity market participation. Column (1) fits random-effects models by using the between regression estimator. Given the unbalanced
cr
panel, column (2) performs between effects estimation with weighted least squares rather than the default OLS. Column (3) fits
the sake of brevity we don’t report the control variables in the regression.
us
random effects models using the population-averaged estimator. In column (4) we run OLS regressions with firm fixed effects and standard errors clustered at firm level. Here the “Full Specification” refers to the specification as in Panel A column (3). For Panel C to Panel E compare the effects in different sub-samples. In Panel C we separate the full sample by the size of
an
book assets (columns (1) and (2)), and the level of institutional ownership (columns (3) and (4)). Panel D separates the sample by the number of local institutional investors and by their investment styles. Column (1) and (2) are based on the number of local small-style institutional investors. Columns (3) and (4) are based on the number of local large-style institutional investors.
M
Panel E separates the sample by the number of local institutional investors and by their trading styles. Column (1) and (2) are based on the number of local transient institutional investors. Columns (3) and (4) are based on the number of local non-transient institutional investors. We perform Chi-squared test to evaluate the differences in coefficients between sub-samples. Standard errors are clustered at firm level in all specifications. ***, ** and * represent significance levels at 1%,
Ac ce p
te
d
5% and 10% respectively.
Table X (Cont’d) Panel A: Main Specification (1)
OLS (2)
(3)
(4)
IV Regression (5)
(6)
49
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0.716*** (3.91)
0.681*** (4.05)
Amihud illiquidity
Local senior fraction Log(local household income) Local unemployment rate Urban dummy Firm leverage Log(market cap) Profitability Log(firm age) Cash holding
-0.018 (-0.90) -5.389** (-2.46) -0.577 (-1.07) -0.021 (-0.21) -0.023** (-2.27) 0.130*** (3.14) -0.048*** (-3.16) -0.052 (-0.80) 0.274*** (25.74) -0.648*** (-7.03) 2.259*** (25.68) 0.085* (1.81) -4.589*** (-9.65) -0.823*** (-12.01) 0.267*** (2.77) 1.689*** (9.51) 0.333*** (19.78) 2.165 (1.46) Y Y Firm 0.2479 56537
M
Tangibility Dividend yield
d
Institutional ownership
te
Institutional turnover Past return
Const
Ac ce p
Return volatility
Exchange dummies Year & State-fixed effects Industry & Rating-fixed effects Clustering Weak identification test: F-statistic Hansen J (p-value) Adjusted R-squared Number of Obs.
-0.004 (-0.22) -4.185** (-2.01) -0.684 (-1.35) -0.118 (-1.23) -0.017* (-1.82) 0.135*** (3.50) -0.054*** (-3.72) 0.247*** (3.45) 0.319*** (28.23) -0.645*** (-7.19) 1.949*** (22.73) 0.174*** (2.90) -3.057*** (-8.34) -0.868*** (-12.98) 0.249** (2.61) 1.025*** (5.86) 0.325*** (19.54) 1.442 (1.00) Y Y Y Firm 0.2826 56537
2.275 (1.49) Y Y Firm 0.2214 56537
-1.175*** (-3.28)
-1.174*** (-3.28)
-0.031 (-1.28) -3.911 (-1.57) -0.465 (-0.69) 0.053 (0.45) -0.020 (-1.59) 0.073 (1.39) 0.268** (2.19) -0.124 (-1.03) -0.738*** (-8.05) 0.022 (0.81) 1.838*** (12.16) -0.005 (-0.08) -4.966*** (-7.16) -1.253*** (-8.17) -0.680** (-2.18) 0.146** (2.47) 1.448*** (6.13) 5.293** (2.39) Y Y Firm 28.76 56537
-0.017 (-0.72) -2.528 (-1.03) -0.547 (-0.85) -0.005 (-0.04) -0.014 (-1.17) 0.052 (1.03) 0.552*** (4.46) -0.123 (-0.91) -0.699*** (-8.07) -0.010 (-0.46) 1.647*** (13.05) 0.143* (1.83) -3.877*** (-5.88) -1.179*** (-9.50) -0.585** (-2.06) 0.178*** (3.71) 1.093*** (4.92) 4.735** (2.23) Y Y Y Firm 30.16 56537
-0.017 (-0.72) -2.559 (-1.05) -0.552 (-0.86) -0.003 (-0.02) -0.014 (-1.17) 0.051 (1.01) 0.555*** (4.53) -0.122 (-0.91) -0.701*** (-8.11) -0.009 (-0.43) 1.647*** (13.03) 0.144* (1.84) -3.841*** (-5.86) -1.183*** (-9.49) -0.579** (-2.04) 0.178*** (3.69) 1.119*** (5.02) 4.714** (2.23) Y Y Y Firm 15.34 0.028(0.86) 56537
us
Local male fraction
-0.012 (-0.59) -5.484** (-2.43) -0.621 (-1.12) -0.012 (-0.12) -0.023** (-2.23) 0.122*** (2.86) -0.107*** (-7.21) -0.022 (-0.34) 0.231*** (26.94) -0.624*** (-6.76) 2.395*** (26.92)
an
Log(local population)
-1.220*** (-3.32)
ip t
0.766*** (4.08)
cr
Local white percentage
Table X (Cont’d) Panel B: Between-Effects and Firm Fixed-effects Regression
Local white percentage
between effects (1) 0.879***
between effects: wls (2) 0.817***
population average (3) 0.534***
firm-fixed effects (4) 0.733**
50
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Full Specification Clustering Number of Obs.
(4.25)
(4.71)
(3.80)
(2.16)
Y 56537
Y 56537
Y 56537
Y Firm 56537
Y Firm 28278
Y Firm 28260
an
6.65***
cr
Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
Separate by institutional ownership above median below median (3) (4) 0.191 0.887*** (0.97) (3.83) Y Firm 28331
us
Local white percentage
Separate by book size above median below median (1) (2) 0.053 0.751*** (0.31) (3.32)
ip t
Panel C: Separate by Size and Institutional Ownership
Y Firm 28207
5.95**
Panel D: Separate by the Number of Local Institutional Investors (Investment Style)
M
Y Firm 29281
d
Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
te
Local white percentage
Investment style: small above median below median (1) (2) 0.913*** 0.100 (3.08) (0.38) Y Firm 27565
Investment style: large above median below median (3) (4) 1.079*** 0.593*** (4.38) (2.68) Y Firm 30813
4.21**
Y Firm 26141 2.45
Ac ce p
Panel E: Separate by the Number of Local Institutional Investors (Trading Style)
Local white percentage
Full Specifications Clustering Number of Obs. Chi-square test: difference in coefficients
Trading style: transient above median below median (1) (2) 0.969*** 0.113 (4.37) (0.41) Y Firm 30812
Y Firm 25821 5.87**
Trading style: non-transient above median below median (3) (4) 0.763*** 0.582*** (2.61) (2.76) Y Firm 29894
Y Firm 27060 0.27
51
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