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Do demand curves for stocks slope down?
The Quarterly Review of Economics and Finance 48 (2008) 641–651
Do demand curves for stocks slope down? Evidence from aggregate data Xuejing Xing ∗ University of Missouri-Rolla, School of Management and Information Systems, Department of Economics and Finance, 103 Harris Hall, 1870 Miner Circle, Rolla, MO 65409, United States Received 15 August 2005; received in revised form 20 March 2006; accepted 27 March 2006 Available online 15 May 2006
Abstract We examine whether the aggregate demand curve for stocks is downward sloping. As a proxy for aggregate demand, we use net outflows (dividends plus repurchases less net issues) from the stock market scaled by the previous year’s market capitalization. To disentangle the information and price pressure effects from the demand curve effects, we use an information-free demographic variable as an instrument and look at the relation between annual changes in aggregate demand and excess market return. We find that informationfree changes in the annual aggregate demand for stocks do not lead to changes in the annual excess market return. This finding supports long-term horizontal demand curves for stocks. © 2006 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved. JEL classification: G10; G14 Keywords: Demand curves for stocks; Instrumental variables; Excess market returns; Population age
1. Introduction Several empirical studies report evidence of downward sloping demand curves for individual stocks.1 Such evidence poses a serious challenge to modern finance theory, which generally assumes horizontal demand curves for stocks: any supply or demand shocks that are devoid of information have no effect on the prevailing price.
∗
Tel.: +1 573 341 6495; fax: +1 573 341 4866. E-mail address: [email protected]. 1 See, for example, Mikkelson and Partch (1985), Shleifer (1986), Lynch and Mendenhall (1997), and Wurgler and Zhuravskaya (2002). 1062-9769/$ – see front matter © 2006 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.qref.2006.03.001
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For two reasons, these previous findings might be subject to alternative explanations. First, the events examined in these studies are not free of information. Second, the price revisions following these events are sometimes attributable to temporary price pressure effects. Thus, whether demand curves for stocks slope down is still an open question. In this paper we examine the aggregate demand curve for stocks. The aggregate demand curve is interesting because a horizontal aggregate demand curve is a sufficient condition for horizontal individual demand curves.2 In addition, focusing on the aggregate demand curve provides us with at least two advantages (see Cha & Lee, 2001). First, we can avoid a possible small sample problem by examining the aggregate demand curve. Second, when we use aggregate data in which substitutions among individual stocks matter little, we can test for a horizontal aggregate demand curve for stocks without relying on the substitution effect. As a proxy for the aggregate demand for stocks, we follow Goyal (2004) and use net outflows (dividends plus repurchases less net issues) from the stock market scaled by the previous year’s market capitalization. Capital flows have been widely used as a measure of demand for equity assets in the literature.3 In our setting, greater demand by investors to buy and hold equities would lead to more issues, less dividends, less repurchases, and as a result, a reduction in net outflows from the market. On the other hand, lower demand for equities would result in less issues, more dividends, more repurchases, and as a consequence, an increase in net outflows from the market. Thus, net outflows from the stock market can serve as a measure of aggregate demand for stocks. However, net outflows can be related to market fundamentals. For example, investors may increase (decrease) their demand for stocks if they expect improving (deteriorating) states in market fundamentals. Consequently, outflows from the stock market may be lower (higher) when market fundamentals look promising (discouraging). Given this correlation between net outflows and fundamentals, conventional ordinary least-squares (OLS) regression of market returns on net outflows without accounting for the effects of fundamentals, which are largely unobservable, will produce a biased estimate of the relation between the two variables. The bias makes it unlikely to tell whether the OLS relation reflects the effects of demand curve or information. To disentangle the information effect from the demand curve effect, we use a demographic variable, the fraction of middle-aged (45–64 years) people, as an instrument. This instrumental variable is valid, because it is significantly correlated with net outflows from the stock market but not with market fundamentals (Goyal, 2004). To avoid the temporary price pressure effects, we focus on the potential relation between annual percentage outflows from the market and annual excess market returns. The temporary price pressure should matter little in annual data. Thus, our research design enables us to overcome the two major problems noted above that have hampered prior empirical studies on demand curves for stocks. We use two alternative econometric strategies, both of which yield consistent results. Our instrumental variable (IV) estimations indicate that exogenous changes in percentage net outflows from the market do not lead to changes in future excess market returns. Our vector autoregressive (VAR) estimations show that information-free changes in percentage outflows do 2 If the aggregate demand curve is horizontal, all individual demand curves must be horizontal. If some individual demand curves are downward sloping, to have a horizontal aggregate demand curve some other individual demand curves have to be upward sloping, which is unlikely. See, for example, Cha and Lee (2001). 3 For example, Cha and Lee (2001) use equity mutual fund flows as a proxy for the aggregate demand for stocks in the U.S. stock market. In Brennan and Cao (1997), Griffin, Nardari, and Stulz (2004), and Froot and Ramadorai (2005), cross-border equity flows reflect the demand for international stocks.
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not Granger-cause changes in excess market returns. These results suggest a horizontal aggregate demand curve for stocks. The paper is organized as follows. Section 2 reviews the related literature. Section 3 describes the data. Section 4 presents two empirical frameworks we use to examine the effects of changes in aggregate demand on market returns. Section 5 presents our empirical results. Section 6 concludes. 2. Prior research There is a stream of empirical literature on demand curves for stocks. Typically, previous studies examine certain events that change the demand for certain individual stocks and look at the effects of such changes on stock returns. These studies have produced mixed results. Scholes (1972) examines the price impact of secondary equity distributions. He finds a negative price response following secondary equity distributions, but the response depends on the seller’s identity. Thus, Scholes finds some evidence of downward sloping demand curves, but he is not able to distinguish between the effects of demand curves and information. Mikkelson and Partch (1985) reexamine secondary equity distributions. They find that the price impact is larger in absolute value for larger offerings, which is consistent with downward sloping demand curves. Shleifer (1986) is among the first to examine the price impact of adding stocks to a widely followed stock market index. After stocks are added to an index, index funds following the index will buy these stocks and thus the demand for the stocks will increase. If the demand curves for these stocks slope down, one should expect to observe a share price increase following the announcement of the inclusion. Shleifer finds a permanent price increase for stocks added to the S&P 500 index and argues that this price increase is consistent with downward sloping demand curves. Lynch and Mendenhall (1997) also report significant, positive abnormal returns following additions into the S&P 500 and interpret the finding as evidence for downward sloping demand curves. Chakrabarti, Huang, Jayaraman, and Lee (2002) examine changes in the MSCI country indexes for 29 countries between 1998 and 2001. They find that the inclusion of a stock generally leads to higher returns following the announcement and that deletions lead to significant negative returns that continue after the change date. Their results suggest downward sloping demand curves in international stock markets. Wurgler and Zhuravskaya (2002) classify firms added to the S&P index by whether or not they have close substitutes. Consistent with the hypothesis that excess demand curves slope downward, the inclusion effect is greater for firms that lack close substitutes, where it is riskier for arbitrageurs to keep demand curves elastic. However, Chen, Noronha, and Singal (2004) find that the price effects of changes to the S&P 500 index are asymmetric: there is a permanent increase in the price of added firms but not a permanent decline for deleted firms. Such an asymmetric price response questions the validity of the downward sloping demand curve hypothesis. Kaul, Mehrotra, and Morck (2000) examine a unique event at the Toronto Stock Exchange (TSE). They find that following the TSE’s redefinition of the public float, which increases the demand for 31 stocks in the TSE 300 index, affected stocks experience statistically significant excess returns of 2.3% during the event week. They interpret their results as supportive of downward sloping demand curves for stocks. These event studies on individual demand curves are subject to two major difficulties. First, events such as additions of stocks to indexes might not be free of information. If this is the case, then the price response following the additions may simply reflect the effects of information other than demand curves. For example, Kaul et al. (2000) argue that adding a stock to the S&P 500 reveals
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favorable information about the expected longevity (and therefore the financial health) of the firm. Denis, McConnell, Ovtchinnikov, and Yu (2003) suggest that inclusion in the S&P 500 may improve future cash flows, because included firms may be forced to perform more efficiently and make more value-enhancing decisions due to more effective monitoring by investors and analysts. Consistent with this hypothesis, they find that newly included companies experience significant increases in earnings forecasts and significant improvements in realized earnings. Dhillon and Johnson (1991) and Jain (1987) also provide empirical evidence on the information hypothesis. Dhillon and Johnson find that prices of the included firms’ bonds, which are not tracked by index funds, increase on the announcement date. Jain documents positive abnormal returns when stocks are added to supplementary indexes that are not tracked by index funds. Second, temporary price pressures following demand changes may also contribute to the price revisions subsequent to such events as additions to indexes. Harris and Gurel (1986) suggest that suppliers of liquidity can demand higher prices during the temporary surge in demand from index funds at the time of the inclusion. Once index funds have achieved their desired portfolio positions and abnormal demand has disappeared, prices should return to their normal levels. Consistent with this hypothesis, Harris and Gurel (1986), Beneish and Whaley (1996), and Lynch and Mendenhall (1997) find at least partial price reversals in the post-addition windows they study. Despite such formidable difficulties in examining individual demand curves for stocks and some obvious comparative advantages of using the aggregate data, the aggregate demand curve has received little attention in the literature. To the best of our knowledge, the only study that looks at the aggregate curve is that of Cha and Lee (2001). Using equity mutual fund flows as a proxy for aggregate demand for stocks, they find that changes in aggregate demand for stocks do not affect stock market prices in the presence of fundamentals. Thus, Cha and Lee’s finding is consistent with a horizontal aggregate demand curve for stocks. Clearly, previous studies have not been able to provide unequivocal evidence on the slope of demand curves for stocks. Thus, the question of whether demand curves for stocks slope down remains open. 3. Data 3.1. Net outflows from the stock market As detailed in Goyal (2004), the net outflows from the stock market can be computed as follows using CRSP data. For each stock i, net outflow during month t is calculated as Net Outflowit = −Market Capit + Market Capit−1 × (1 + Rit ),
(1)
where Market Capit is the market capitalization at the end of month t and Rit is the return during month t. For new issues in month t, only the first term presents in the equation. For delisting, only the second term without Rit appears in the equation. Net outflows from the market in month t are the sum of outflows of all stocks on CRSP in the same month, Net Outflowt =
Nt
Net Outflowsit ,
i=1
where Nt is the number of stocks in month t on CRSP.
(2)
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Net outflows during the year (Net OutflowT ) are the sum of outflows during the past 12 months, Net OutflowT =
12
Net Outflowst .
(3)
t=1
A negative number for net outflows indicates that the net issuing activity exceeds dividends paid out. That is, negative (positive) outflows imply net inflows into (outflows from) the stock market. The net outflows from the U.S. stock market as computed in Eq. (3) for the years from 1927 to 1998 are available in Table 2 of Goyal (2004). We follow Goyal (2004) and use net outflows during the year scaled by the previous year’s market capitalization as a proxy for the aggregate demand for stocks, Aggregate DemandT =
Net OutflowT . Market CapT −1
(4)
Thus, negative (positive) Aggregate Demand suggests an increase (decrease) in aggregate demand for stocks. 3.2. The population age variable and excess market returns We collect population age data from various issues of Historical Statistics of the United States: Statistical Abstracts (U.S. Census Bureau). We are particularly interested in the fraction of middleaged (45–64 years) people. We obtain this fraction from these sources for the years from 1927 to 1998. The annual excess market return is the value-weighted return on all NYSE, AMEX, and NASDAQ stocks (from CRSP) minus the Treasury bill rate (from Ibbotson Associates), which is available at Professor Kenneth R. French’s website (http://mba.tuck.dartmouth.edu/pages/faculty/ ken.french/data library.html). 3.3. Descriptive statistics Table 1 presents two panels of descriptive statistics on the data. Panel A shows changes in the key variables over time. Panel B provides means, medians, standard deviations, and maximum and minimum values for the variables. Panel A indicates that the variables have substantial variations over time. For example, the excess market return is as high as 28.26% in 1950, but it is −30.77% in 1930. The aggregate demand for stocks (percentage outflows from the market) is −7.09% in 1930, but 4.02% in 1940. The fraction of middle-aged people seems to increase until the 1970s and then drops slightly. Panel B further confirms the observation we make in Panel A. The mean market return is 9.12% from 1927 to 1998, with a standard deviation of 20.59%, suggesting substantial variations. The mean aggregate demand for stocks is 1.43%, indicating that on average, there is an outflow from the stock market from 1927 to 1998. The mean fraction of middle-aged people is 19.41% with a standard deviation of 1.1%. This lesser variation in the age variable is not surprising, because demographics are known to change slowly.
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Table 1 Descriptive statistics 1930
1940
1950
1960
1970
1980
1990
1998
(Panel A) End of decade (sample period) numbers Excess market return (%) −30.77 −6.93 Aggregate demand (%) −7.09 4.02 Percent of middle-aged people 17.50 17.50
28.26 3.82 19.81
−1.73 0.89 20.39
−5.65 −0.09 20.57
22.10 2.09 19.60
−13.84 3.56 18.50
19.42 −0.79 21.20
(Panel B) Summary statistics Excess market return (%) Aggregate demand (%) Percent of middle-aged people
Mean
Standard deviation
Median
Minimum
Maximum
9.12 1.43 19.41
20.59 3.04 1.10
11.17 1.68 19.81
−44.80 −12.01 17.50
56.08 7.37 21.20
Excess market return is the value-weighted return on all NYSE, AMEX, and NASDAQ stocks (from CRSP) minus the Treasury bill rate (from Ibbotson Associates). Aggregate demand for stocks is the percentage outflows from the stock market, which are available in Table 2 of Goyal (2004) and can be computed as follows using CRSP data:
12 Nt
Aggregate DemandT =
t=1
i=1
(−Market Capit + Market Capit−1 (1 + Retit )) Market CapT −1
,
where Aggregate DemandT is the aggregate percentage outflows from the market in year T, Nt the number of stocks in month t on CRSP, Market Capit the market capitalization for stock i in month t, Retit the return for stock i and month t, and Market CapT−1 is the previous year’s total market capitalization of the CRSP index. The percentage of middle-aged people is the fraction of people in the population who are between 45 and 64 years. All variables are of annual frequency. The sample covers the period 1927–1998.
4. Econometric methods 4.1. IV estimations To examine the slope of demand curves for stocks, we need to look at the relation between changes in aggregate demand and changes in excess market return. The most straightforward way to relate aggregate demand, D, to excess market return, R, is to apply a simple model as follows: R = a1 + a2 × D + a3 × E[F ] + u,
(5)
where E[ ] is the expectations operator and F represents market fundamentals. If E[D, u] = E[E[F], u] = 0, both a2 and a3 can be estimated consistently by OLS. However, because E[F] is largely unobservable, we are forced to estimate R = a1 + a2 × D + e,
(6)
where e = u + a3 × E[F]. Because D is likely to be correlated with E[F], E[D, e] is not zero and an OLS estimated a2 will be a biased and inconsistent estimator of the relation between uninformed aggregate demand and excess market return. However, if we can find an instrumental variable, Z, that is uncorrelated with unobserved market fundamentals (and hence with the error term e), but correlated with D, then we can estimate the parameters of D = d1 + d2 × Z + v,
(7)
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and a reduced-form excess return equation ˆ + w, R = k1 + k2 × D
(8)
ˆ denotes the fitted values of D in (7) and the error term w includes the effect of unobserved where D ˆ E[F ]] = E[D, ˆ w] = 0. fundamentals. Now the parameters of (8) will be unbiased because E[D, Traditional lifecycle investment models suggest such an instrumental variable. These models typically maintain that an increase (decrease) in the middle-aged population leads to an increase (decrease) in demand for equities, with the absence of changes in fundamentals (see, for example, Bakshi & Chen, 1994; Goyal, 2004). Goyal (2004) shows that indeed, the fraction of middleaged (45–64 years) people is significantly and negatively correlated with net outflows from the market. Because there is no obvious reason to believe that population age structure could directly affect future cash flows generated by firms and the discount rate, the age variable should not be correlated with market fundamentals. Therefore, the fraction of middle-aged people can serve as a plausible instrument.4 The estimates of the parameters in Eqs. (7) and (8) can be obtained by running a classical IV estimation, using two-stage least squares (2SLS).5 However, the instrument might be suspected as weak, because they are only mildly correlated with aggregate demand for stocks (the Spearman correlation coefficient between net outflows from the market and the fraction of middle-aged people, although marginally significant, is −0.191), possibly biasing the parameter estimates. To check whether this is the case, we apply a limited information maximum likelihood (LIML) estimator, which yields essentially the same results (not reported for brevity, but available upon request). This consistency, coupled with the obvious difference between OLS and 2SLS results, suggests that the instrument might be strong enough to produce reliable 2SLS estimates. 4.2. VAR estimations An alternative way to examine the aggregate demand curve for stocks is to test whether changes in aggregate demand Granger-cause changes in excess market returns. If the aggregate demand curve is downward sloping, we expect to find a Granger-causality running from changes in aggregate demand for stocks to changes in excess market return. To examine this causality, we apply the following bivariate VAR model: ⎧ n n ⎪ ⎪ ⎪ R = ω + α R + βj DT −j + εT , ⎪ T j T −j ⎪ ⎨ j=1 j=1 (9) n n ⎪ ⎪ ⎪ DT = ω + ⎪ αj DT −j + βj RT −j + εT , ⎪ ⎩ j=1
j=1
4 According to the same line of argument, the fraction of old people (65 years and over) in the population could be another potential instrument. However, Goyal (2004) finds that although the fraction of old people seems to be positively correlated with net outflows from the stock market, the relation is not statistically significant (see Table 4 in Goyal, 2004, p. 127). We also observe that the correlation between the fraction of old people and net outflows is much weaker than that between the fraction of middle-aged people and net outflows. For these reasons, we believe that the fraction of middle-aged people is a stronger instrument and therefore we prefer to use it in our analysis. 5 The 2SLS technique is widely used in dealing with the omitted variable bias. See, for example, Ginsburgh and Ours (2003).
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Table 2 The effects of changes in aggregate demand for stocks on excess market returns Constant (Panel A) Percentage net outflows as the explanatory variable Estimation of Eq. (6) OLS 0.0513** (2.03) IV estimation of Eqs. (6) and (7) 2SLS
0.0333 (0.41)
(Panel B) Year-to-year changes in percentage outflows as the explanatory variable Estimation of Eq. (6) OLS 0.0839*** (3.41) IV estimation of Eqs. (6) and (7) 2SLS
0.0951* (1.96)
Aggregate demand
2.5262*** (3.36) 3.7553 (0.71)
0.5367 (0.60) −10.5173 (−0.53)
The table reports the results of regressing excess market returns on changes in the aggregate demand for stocks. As proxies for changes in the aggregate demand for stocks, we use either the percentage net outflows from the stock market (in Panel A) or year-to-year changes in these percentage outflows (in Panel B). In all regressions, excess market returns lead aggregate demand by one year. The fraction of middle-aged (45–64 years) people serves as an instrumental variable in the simultaneous equations. t-Statistics are given between parentheses under coefficient estimates. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.
where RT is excess market return and DT represents changes in aggregate demand for stocks in year T. A problem with the VAR model is that it cannot disentangle the effects of demand curves from the effects of information, because changes in aggregate demand are likely to be related to market fundamentals. To filter out the information contents of changes in aggregate demand, we first regress these changes on the lagged fraction of middle-aged people. We then use the predicted values in this regression to replace changes in aggregate demand, DT , in the VAR model. After estimating the model, we can use standard F-tests to assess the predictive power of lagged changes in aggregate demand for excess market returns and vice versa. If the F-statistic for the predictive power of lagged values of DT (RT ) on RT (DT ) is significant, then we can say that DT (RT ) Granger-causes RT (DT ). 5. Empirical results 5.1. IV estimation results Table 2 reports results from the estimations of Eqs. (6) and (7). In all regressions, the dependent variable, excess stock returns, leads the explanatory variables by one year. We focus on predictive regressions, because contemporaneously it is not clear whether stock returns affect demand or demand affects stock returns, but the causality is stronger in predictive regressions.6 Indeed, Goyal (2004) finds that the percentage outflows from the market can predict next year’s stock market returns. In Panel A of Table 2, the explanatory variable is the percentage net outflows from the market as expressed in Eq. (4). The OLS regression shows that there is a statistically significant relation 6
We thank the referee for this point.
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between lagged percentage outflows from the market and excess market returns. This finding is consistent with that in Goyal (2004). However, as we discuss in Section 4, this result has nothing to say about the aggregate demand curve for stocks because unadjusted net outflows from the market may partially reflect information about market fundamentals. 2SLS estimations paint a very different picture on the predictive power of net outflows from the market for excess market returns. As we show in Panel A of Table 2, when we use the fraction of middle-aged people as an instrumental variable, we find no relation between the percentage net outflows from the market and next year’s excess market returns. In Panel B of Table 2, the explanatory variable is the year-to-year changes in the percentage net outflows from the market. Both OLS and 2SLS estimations indicate that the year-to-year changes in the percentage outflows are not related to next year’s excess market returns in any significant way. Overall, the results in Table 2 illustrate the potential difficulty of making causal inferences when one attempts to use the OLS approach to examine the relation between changes in aggregate demand and excess market returns. The results also show that the use of the fraction of middleaged people as an instrumental variable appears to mitigate this difficulty. Further, the findings in Table 2 suggest that the aggregate demand curve for stocks is horizontal, which is consistent with the results of Cha and Lee (2001). 5.2. VAR estimation results Table 3 presents our VAR estimation results. The results in Panel A of Table 3 show that when we use raw (unadjusted) percentage outflows from the market as a measure of aggregate demand for stocks, the null hypothesis that net outflows do not Granger-cause excess market returns is rejected at the 1% level. This result is robust to different numbers of lags. The finding is consistent with the OLS predictive regression result in Table 2 in that raw percentage outflows, which are highly likely to partially reflect information, have predictive power for future market returns. The result also appears to be consistent with that of Goyal (2004) who uses a nontraditional VAR model and finds that net outflows from the market help predict excess returns on the S&P 500 index. In addition, the results in Panel A of Table 3 show that annual excess market returns do not Granger-cause either themselves or net outflows from the market, but lagged net outflows are related to future outflows. However, Panel B of Table 3 tells a different story. In Panel B, we use the predicted values of regressing raw percentage outflows on the fraction of middle-aged people and find that changes in aggregate demand for stocks induced by changes in an information-free variable do not Grangercause excess market returns. This result holds when we use different numbers of lags. Thus, our VAR results further confirm our findings from estimating simultaneous equations, which show that information-free changes in aggregate demand for stocks do not lead to changes in market returns, thereby suggesting a horizontal aggregate demand curve for stocks. 6. Summary and conclusions In this paper we examine whether demand curves for stocks are downward sloping. We focus on the aggregate demand curve for stocks because a horizontal aggregate curve is sufficient for horizontal individual curves. As a measure of aggregated demand for stocks, we follow Goyal (2004) and use net outflows (dividends plus repurchases less net issues) from the stock market scaled by the previous year’s market capitalization.
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Table 3 VAR tests for Granger-causality between changes in aggregate stock demand and excess market returns Lags
Dependent variable: market returns
Dependent variable: aggregate demand
Aggregate demand
Market return
Aggregate demand
Market return
F-statistics
F-statistics
p-Value
F-statistics
p-Value
F-statistics
p-Value
0.8479 0.3813 0.4742
0.4330 0.7668 0.7545
8.7015 9.6755 1.5601
0.0004 0 0.1968
1.8954 0.8164 0.0087
0.1585 0.4897 0.4950
70 69 68
(Panel B) Using predicted outflows 2 0.9172 0.4047 1.0243 3 1.0986 0.3565 0.4227 4 0.8633 0.4913 0.4528
0.3648 0.7374 0.7700
471.9542 289.8647 206.8602
0 0 0
0.0023 0.6224 1.4208
0.9977 0.6032 0.2383
70 69 68
p-Value
(Panel A) Using raw outflows 2 10.6601 0.0001 3 6.3611 0.0008 4 3.7517 0.0087
Observations
The table reports the estimation results of the following bivariate VAR model:
⎧ n n ⎪ ⎪ ⎪ + βj DT −j + εT , R = ω + α R j T −j ⎪ ⎨ T j=1 n
j=1 n
⎪ ⎪ ⎪ αj DT −j + βj RT −j + εT , DT = ω + ⎪ ⎩ j=1
j=1
where RT denotes excess market return and DT represents aggregate demand for stocks in year T. Panel A uses raw percentage outflows from the market (as a proxy for aggregate stock demand), which could reflect information. Panel B uses predicted percentage outflows that we obtain from regressing percentage outflows on the lagged fraction of middleaged (45–64 years) people. Reported in this table are F-statistics for the joint significance of separate, lagged right-hand variables and their corresponding p-values.
Since outflows from the market are likely to be correlated with market fundamentals, we use the fraction of middle-aged people as an instrumental variable to filter out the information contents of net outflows from the market. This instrument is valid because it is correlated with net outflows from the market, but not with market fundamentals (Goyal, 2004). To avoid the influence of temporary price pressures, we use annual data. Our IV and VAR estimation results indicate that information-free changes in aggregate demand for stocks do not lead to changes in excess market returns. This finding provides empirical support for the idea of horizontal demand curves for stocks, which is one of the most important assumptions in finance. References Bakshi, G., & Chen, Z. (1994). Baby boom, population aging, and capital markets. Journal of Business, 67, 165–202. Beneish, M., & Whaley, R. (1996). An anatomy of the ‘S&P game’: The effects of changing the rules. Journal of Finance, 51, 1909–1930. Brennan, M., & Cao, H. (1997). International portfolio investment flows. Journal of Finance, 52, 1851–1880. Cha, H., & Lee, B. (2001). The market demand curve for common stocks: Evidence from equity mutual fund flows. Journal of Financial and Quantitative Analysis, 36, 195–220. Chakrabarti, R., Huang, Jayaraman, W., & Lee, J. (2002). Do international investors’ demand curves for stocks slope down too? Working paper. Georgia Institute of Technology. Chen, H., Noronha, G., & Singal, V. (2004). The price response to S&P 500 index additions and deletions: Evidence of asymmetry and a new explanation. Journal of Finance, 59, 1901–1929.
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Denis, D., McConnell, J., Ovtchinnikov, A., & Yu, Y. (2003). S&P 500 index additions and earnings expectations. Journal of Finance, 58, 1821–1840. Dhillon, U., & Johnson, H. (1991). Changes in the Standard and Poor’s 500 list. Journal of Business, 64, 75–85. Froot, K., & T. Ramadorai (2005). The information content of international portfolio flows. Working Paper. Harvard University/University of Oxford. Ginsburgh, V., & Ours, J. (2003). Expert opinion and compensation: Evidence from a musical competition. American Economic Review, 93, 289–298. Goyal, A. (2004). Demographics, stock market flows, and stock returns. Journal of Financial and Quantitative Analysis, 39, 115–142. Griffin, J., Nardari, F., & Stulz, R. (2004). Are daily cross-border equity flows pushed or pulled? The Review of Economics and Statistics, 86, 641–657. Harris, L., & Gurel, E. (1986). Price and volume effects associated with changes in the S&P list: New evidence for the existence of price pressures. Journal of Finance, 41, 815–829. Jain, P. (1987). The effect on stock price of inclusion in or exclusion from the S&P 500. Financial Analysts Journal, 43, 58–65. Kaul, A., Mehrotra, V., & Morck, R. (2000). Demand curves for stocks do slope down: New evidence from an index weights adjustment. Journal of Finance, 55, 893–912. Lynch, A., & Mendenhall, R. (1997). New evidence on stock price effects associated with changes in the S&P 500 index. Journal of Business, 70, 351–383. Mikkelson, W., & Partch, M. (1985). Stock price effects and costs of secondary distributions. Journal of Financial Economics, 14, 165–194. Scholes, M. (1972). The market for securities: Substitution versus price pressure and the effects of information on share prices. Journal of Business, 45, 179–211. Shleifer, A. (1986). Do demand curves for stocks slope down? Journal of Finance, 41, 579–590. Wurgler, J., & Zhuravskaya, K. (2002). Does arbitrage flatten demand curves for stocks? Journal of Business, 75, 583–608.
Do demand curves for stocks slope down? Evidence from aggregate data Xuejing Xing ∗ University of Missouri-Rolla, School of Management and Information Systems, Department of Economics and Finance, 103 Harris Hall, 1870 Miner Circle, Rolla, MO 65409, United States Received 15 August 2005; received in revised form 20 March 2006; accepted 27 March 2006 Available online 15 May 2006
Abstract We examine whether the aggregate demand curve for stocks is downward sloping. As a proxy for aggregate demand, we use net outflows (dividends plus repurchases less net issues) from the stock market scaled by the previous year’s market capitalization. To disentangle the information and price pressure effects from the demand curve effects, we use an information-free demographic variable as an instrument and look at the relation between annual changes in aggregate demand and excess market return. We find that informationfree changes in the annual aggregate demand for stocks do not lead to changes in the annual excess market return. This finding supports long-term horizontal demand curves for stocks. © 2006 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved. JEL classification: G10; G14 Keywords: Demand curves for stocks; Instrumental variables; Excess market returns; Population age
1. Introduction Several empirical studies report evidence of downward sloping demand curves for individual stocks.1 Such evidence poses a serious challenge to modern finance theory, which generally assumes horizontal demand curves for stocks: any supply or demand shocks that are devoid of information have no effect on the prevailing price.
∗
Tel.: +1 573 341 6495; fax: +1 573 341 4866. E-mail address: [email protected]. 1 See, for example, Mikkelson and Partch (1985), Shleifer (1986), Lynch and Mendenhall (1997), and Wurgler and Zhuravskaya (2002). 1062-9769/$ – see front matter © 2006 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.qref.2006.03.001
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For two reasons, these previous findings might be subject to alternative explanations. First, the events examined in these studies are not free of information. Second, the price revisions following these events are sometimes attributable to temporary price pressure effects. Thus, whether demand curves for stocks slope down is still an open question. In this paper we examine the aggregate demand curve for stocks. The aggregate demand curve is interesting because a horizontal aggregate demand curve is a sufficient condition for horizontal individual demand curves.2 In addition, focusing on the aggregate demand curve provides us with at least two advantages (see Cha & Lee, 2001). First, we can avoid a possible small sample problem by examining the aggregate demand curve. Second, when we use aggregate data in which substitutions among individual stocks matter little, we can test for a horizontal aggregate demand curve for stocks without relying on the substitution effect. As a proxy for the aggregate demand for stocks, we follow Goyal (2004) and use net outflows (dividends plus repurchases less net issues) from the stock market scaled by the previous year’s market capitalization. Capital flows have been widely used as a measure of demand for equity assets in the literature.3 In our setting, greater demand by investors to buy and hold equities would lead to more issues, less dividends, less repurchases, and as a result, a reduction in net outflows from the market. On the other hand, lower demand for equities would result in less issues, more dividends, more repurchases, and as a consequence, an increase in net outflows from the market. Thus, net outflows from the stock market can serve as a measure of aggregate demand for stocks. However, net outflows can be related to market fundamentals. For example, investors may increase (decrease) their demand for stocks if they expect improving (deteriorating) states in market fundamentals. Consequently, outflows from the stock market may be lower (higher) when market fundamentals look promising (discouraging). Given this correlation between net outflows and fundamentals, conventional ordinary least-squares (OLS) regression of market returns on net outflows without accounting for the effects of fundamentals, which are largely unobservable, will produce a biased estimate of the relation between the two variables. The bias makes it unlikely to tell whether the OLS relation reflects the effects of demand curve or information. To disentangle the information effect from the demand curve effect, we use a demographic variable, the fraction of middle-aged (45–64 years) people, as an instrument. This instrumental variable is valid, because it is significantly correlated with net outflows from the stock market but not with market fundamentals (Goyal, 2004). To avoid the temporary price pressure effects, we focus on the potential relation between annual percentage outflows from the market and annual excess market returns. The temporary price pressure should matter little in annual data. Thus, our research design enables us to overcome the two major problems noted above that have hampered prior empirical studies on demand curves for stocks. We use two alternative econometric strategies, both of which yield consistent results. Our instrumental variable (IV) estimations indicate that exogenous changes in percentage net outflows from the market do not lead to changes in future excess market returns. Our vector autoregressive (VAR) estimations show that information-free changes in percentage outflows do 2 If the aggregate demand curve is horizontal, all individual demand curves must be horizontal. If some individual demand curves are downward sloping, to have a horizontal aggregate demand curve some other individual demand curves have to be upward sloping, which is unlikely. See, for example, Cha and Lee (2001). 3 For example, Cha and Lee (2001) use equity mutual fund flows as a proxy for the aggregate demand for stocks in the U.S. stock market. In Brennan and Cao (1997), Griffin, Nardari, and Stulz (2004), and Froot and Ramadorai (2005), cross-border equity flows reflect the demand for international stocks.
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not Granger-cause changes in excess market returns. These results suggest a horizontal aggregate demand curve for stocks. The paper is organized as follows. Section 2 reviews the related literature. Section 3 describes the data. Section 4 presents two empirical frameworks we use to examine the effects of changes in aggregate demand on market returns. Section 5 presents our empirical results. Section 6 concludes. 2. Prior research There is a stream of empirical literature on demand curves for stocks. Typically, previous studies examine certain events that change the demand for certain individual stocks and look at the effects of such changes on stock returns. These studies have produced mixed results. Scholes (1972) examines the price impact of secondary equity distributions. He finds a negative price response following secondary equity distributions, but the response depends on the seller’s identity. Thus, Scholes finds some evidence of downward sloping demand curves, but he is not able to distinguish between the effects of demand curves and information. Mikkelson and Partch (1985) reexamine secondary equity distributions. They find that the price impact is larger in absolute value for larger offerings, which is consistent with downward sloping demand curves. Shleifer (1986) is among the first to examine the price impact of adding stocks to a widely followed stock market index. After stocks are added to an index, index funds following the index will buy these stocks and thus the demand for the stocks will increase. If the demand curves for these stocks slope down, one should expect to observe a share price increase following the announcement of the inclusion. Shleifer finds a permanent price increase for stocks added to the S&P 500 index and argues that this price increase is consistent with downward sloping demand curves. Lynch and Mendenhall (1997) also report significant, positive abnormal returns following additions into the S&P 500 and interpret the finding as evidence for downward sloping demand curves. Chakrabarti, Huang, Jayaraman, and Lee (2002) examine changes in the MSCI country indexes for 29 countries between 1998 and 2001. They find that the inclusion of a stock generally leads to higher returns following the announcement and that deletions lead to significant negative returns that continue after the change date. Their results suggest downward sloping demand curves in international stock markets. Wurgler and Zhuravskaya (2002) classify firms added to the S&P index by whether or not they have close substitutes. Consistent with the hypothesis that excess demand curves slope downward, the inclusion effect is greater for firms that lack close substitutes, where it is riskier for arbitrageurs to keep demand curves elastic. However, Chen, Noronha, and Singal (2004) find that the price effects of changes to the S&P 500 index are asymmetric: there is a permanent increase in the price of added firms but not a permanent decline for deleted firms. Such an asymmetric price response questions the validity of the downward sloping demand curve hypothesis. Kaul, Mehrotra, and Morck (2000) examine a unique event at the Toronto Stock Exchange (TSE). They find that following the TSE’s redefinition of the public float, which increases the demand for 31 stocks in the TSE 300 index, affected stocks experience statistically significant excess returns of 2.3% during the event week. They interpret their results as supportive of downward sloping demand curves for stocks. These event studies on individual demand curves are subject to two major difficulties. First, events such as additions of stocks to indexes might not be free of information. If this is the case, then the price response following the additions may simply reflect the effects of information other than demand curves. For example, Kaul et al. (2000) argue that adding a stock to the S&P 500 reveals
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favorable information about the expected longevity (and therefore the financial health) of the firm. Denis, McConnell, Ovtchinnikov, and Yu (2003) suggest that inclusion in the S&P 500 may improve future cash flows, because included firms may be forced to perform more efficiently and make more value-enhancing decisions due to more effective monitoring by investors and analysts. Consistent with this hypothesis, they find that newly included companies experience significant increases in earnings forecasts and significant improvements in realized earnings. Dhillon and Johnson (1991) and Jain (1987) also provide empirical evidence on the information hypothesis. Dhillon and Johnson find that prices of the included firms’ bonds, which are not tracked by index funds, increase on the announcement date. Jain documents positive abnormal returns when stocks are added to supplementary indexes that are not tracked by index funds. Second, temporary price pressures following demand changes may also contribute to the price revisions subsequent to such events as additions to indexes. Harris and Gurel (1986) suggest that suppliers of liquidity can demand higher prices during the temporary surge in demand from index funds at the time of the inclusion. Once index funds have achieved their desired portfolio positions and abnormal demand has disappeared, prices should return to their normal levels. Consistent with this hypothesis, Harris and Gurel (1986), Beneish and Whaley (1996), and Lynch and Mendenhall (1997) find at least partial price reversals in the post-addition windows they study. Despite such formidable difficulties in examining individual demand curves for stocks and some obvious comparative advantages of using the aggregate data, the aggregate demand curve has received little attention in the literature. To the best of our knowledge, the only study that looks at the aggregate curve is that of Cha and Lee (2001). Using equity mutual fund flows as a proxy for aggregate demand for stocks, they find that changes in aggregate demand for stocks do not affect stock market prices in the presence of fundamentals. Thus, Cha and Lee’s finding is consistent with a horizontal aggregate demand curve for stocks. Clearly, previous studies have not been able to provide unequivocal evidence on the slope of demand curves for stocks. Thus, the question of whether demand curves for stocks slope down remains open. 3. Data 3.1. Net outflows from the stock market As detailed in Goyal (2004), the net outflows from the stock market can be computed as follows using CRSP data. For each stock i, net outflow during month t is calculated as Net Outflowit = −Market Capit + Market Capit−1 × (1 + Rit ),
(1)
where Market Capit is the market capitalization at the end of month t and Rit is the return during month t. For new issues in month t, only the first term presents in the equation. For delisting, only the second term without Rit appears in the equation. Net outflows from the market in month t are the sum of outflows of all stocks on CRSP in the same month, Net Outflowt =
Nt
Net Outflowsit ,
i=1
where Nt is the number of stocks in month t on CRSP.
(2)
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Net outflows during the year (Net OutflowT ) are the sum of outflows during the past 12 months, Net OutflowT =
12
Net Outflowst .
(3)
t=1
A negative number for net outflows indicates that the net issuing activity exceeds dividends paid out. That is, negative (positive) outflows imply net inflows into (outflows from) the stock market. The net outflows from the U.S. stock market as computed in Eq. (3) for the years from 1927 to 1998 are available in Table 2 of Goyal (2004). We follow Goyal (2004) and use net outflows during the year scaled by the previous year’s market capitalization as a proxy for the aggregate demand for stocks, Aggregate DemandT =
Net OutflowT . Market CapT −1
(4)
Thus, negative (positive) Aggregate Demand suggests an increase (decrease) in aggregate demand for stocks. 3.2. The population age variable and excess market returns We collect population age data from various issues of Historical Statistics of the United States: Statistical Abstracts (U.S. Census Bureau). We are particularly interested in the fraction of middleaged (45–64 years) people. We obtain this fraction from these sources for the years from 1927 to 1998. The annual excess market return is the value-weighted return on all NYSE, AMEX, and NASDAQ stocks (from CRSP) minus the Treasury bill rate (from Ibbotson Associates), which is available at Professor Kenneth R. French’s website (http://mba.tuck.dartmouth.edu/pages/faculty/ ken.french/data library.html). 3.3. Descriptive statistics Table 1 presents two panels of descriptive statistics on the data. Panel A shows changes in the key variables over time. Panel B provides means, medians, standard deviations, and maximum and minimum values for the variables. Panel A indicates that the variables have substantial variations over time. For example, the excess market return is as high as 28.26% in 1950, but it is −30.77% in 1930. The aggregate demand for stocks (percentage outflows from the market) is −7.09% in 1930, but 4.02% in 1940. The fraction of middle-aged people seems to increase until the 1970s and then drops slightly. Panel B further confirms the observation we make in Panel A. The mean market return is 9.12% from 1927 to 1998, with a standard deviation of 20.59%, suggesting substantial variations. The mean aggregate demand for stocks is 1.43%, indicating that on average, there is an outflow from the stock market from 1927 to 1998. The mean fraction of middle-aged people is 19.41% with a standard deviation of 1.1%. This lesser variation in the age variable is not surprising, because demographics are known to change slowly.
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Table 1 Descriptive statistics 1930
1940
1950
1960
1970
1980
1990
1998
(Panel A) End of decade (sample period) numbers Excess market return (%) −30.77 −6.93 Aggregate demand (%) −7.09 4.02 Percent of middle-aged people 17.50 17.50
28.26 3.82 19.81
−1.73 0.89 20.39
−5.65 −0.09 20.57
22.10 2.09 19.60
−13.84 3.56 18.50
19.42 −0.79 21.20
(Panel B) Summary statistics Excess market return (%) Aggregate demand (%) Percent of middle-aged people
Mean
Standard deviation
Median
Minimum
Maximum
9.12 1.43 19.41
20.59 3.04 1.10
11.17 1.68 19.81
−44.80 −12.01 17.50
56.08 7.37 21.20
Excess market return is the value-weighted return on all NYSE, AMEX, and NASDAQ stocks (from CRSP) minus the Treasury bill rate (from Ibbotson Associates). Aggregate demand for stocks is the percentage outflows from the stock market, which are available in Table 2 of Goyal (2004) and can be computed as follows using CRSP data:
12 Nt
Aggregate DemandT =
t=1
i=1
(−Market Capit + Market Capit−1 (1 + Retit )) Market CapT −1
,
where Aggregate DemandT is the aggregate percentage outflows from the market in year T, Nt the number of stocks in month t on CRSP, Market Capit the market capitalization for stock i in month t, Retit the return for stock i and month t, and Market CapT−1 is the previous year’s total market capitalization of the CRSP index. The percentage of middle-aged people is the fraction of people in the population who are between 45 and 64 years. All variables are of annual frequency. The sample covers the period 1927–1998.
4. Econometric methods 4.1. IV estimations To examine the slope of demand curves for stocks, we need to look at the relation between changes in aggregate demand and changes in excess market return. The most straightforward way to relate aggregate demand, D, to excess market return, R, is to apply a simple model as follows: R = a1 + a2 × D + a3 × E[F ] + u,
(5)
where E[ ] is the expectations operator and F represents market fundamentals. If E[D, u] = E[E[F], u] = 0, both a2 and a3 can be estimated consistently by OLS. However, because E[F] is largely unobservable, we are forced to estimate R = a1 + a2 × D + e,
(6)
where e = u + a3 × E[F]. Because D is likely to be correlated with E[F], E[D, e] is not zero and an OLS estimated a2 will be a biased and inconsistent estimator of the relation between uninformed aggregate demand and excess market return. However, if we can find an instrumental variable, Z, that is uncorrelated with unobserved market fundamentals (and hence with the error term e), but correlated with D, then we can estimate the parameters of D = d1 + d2 × Z + v,
(7)
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and a reduced-form excess return equation ˆ + w, R = k1 + k2 × D
(8)
ˆ denotes the fitted values of D in (7) and the error term w includes the effect of unobserved where D ˆ E[F ]] = E[D, ˆ w] = 0. fundamentals. Now the parameters of (8) will be unbiased because E[D, Traditional lifecycle investment models suggest such an instrumental variable. These models typically maintain that an increase (decrease) in the middle-aged population leads to an increase (decrease) in demand for equities, with the absence of changes in fundamentals (see, for example, Bakshi & Chen, 1994; Goyal, 2004). Goyal (2004) shows that indeed, the fraction of middleaged (45–64 years) people is significantly and negatively correlated with net outflows from the market. Because there is no obvious reason to believe that population age structure could directly affect future cash flows generated by firms and the discount rate, the age variable should not be correlated with market fundamentals. Therefore, the fraction of middle-aged people can serve as a plausible instrument.4 The estimates of the parameters in Eqs. (7) and (8) can be obtained by running a classical IV estimation, using two-stage least squares (2SLS).5 However, the instrument might be suspected as weak, because they are only mildly correlated with aggregate demand for stocks (the Spearman correlation coefficient between net outflows from the market and the fraction of middle-aged people, although marginally significant, is −0.191), possibly biasing the parameter estimates. To check whether this is the case, we apply a limited information maximum likelihood (LIML) estimator, which yields essentially the same results (not reported for brevity, but available upon request). This consistency, coupled with the obvious difference between OLS and 2SLS results, suggests that the instrument might be strong enough to produce reliable 2SLS estimates. 4.2. VAR estimations An alternative way to examine the aggregate demand curve for stocks is to test whether changes in aggregate demand Granger-cause changes in excess market returns. If the aggregate demand curve is downward sloping, we expect to find a Granger-causality running from changes in aggregate demand for stocks to changes in excess market return. To examine this causality, we apply the following bivariate VAR model: ⎧ n n ⎪ ⎪ ⎪ R = ω + α R + βj DT −j + εT , ⎪ T j T −j ⎪ ⎨ j=1 j=1 (9) n n ⎪ ⎪ ⎪ DT = ω + ⎪ αj DT −j + βj RT −j + εT , ⎪ ⎩ j=1
j=1
4 According to the same line of argument, the fraction of old people (65 years and over) in the population could be another potential instrument. However, Goyal (2004) finds that although the fraction of old people seems to be positively correlated with net outflows from the stock market, the relation is not statistically significant (see Table 4 in Goyal, 2004, p. 127). We also observe that the correlation between the fraction of old people and net outflows is much weaker than that between the fraction of middle-aged people and net outflows. For these reasons, we believe that the fraction of middle-aged people is a stronger instrument and therefore we prefer to use it in our analysis. 5 The 2SLS technique is widely used in dealing with the omitted variable bias. See, for example, Ginsburgh and Ours (2003).
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Table 2 The effects of changes in aggregate demand for stocks on excess market returns Constant (Panel A) Percentage net outflows as the explanatory variable Estimation of Eq. (6) OLS 0.0513** (2.03) IV estimation of Eqs. (6) and (7) 2SLS
0.0333 (0.41)
(Panel B) Year-to-year changes in percentage outflows as the explanatory variable Estimation of Eq. (6) OLS 0.0839*** (3.41) IV estimation of Eqs. (6) and (7) 2SLS
0.0951* (1.96)
Aggregate demand
2.5262*** (3.36) 3.7553 (0.71)
0.5367 (0.60) −10.5173 (−0.53)
The table reports the results of regressing excess market returns on changes in the aggregate demand for stocks. As proxies for changes in the aggregate demand for stocks, we use either the percentage net outflows from the stock market (in Panel A) or year-to-year changes in these percentage outflows (in Panel B). In all regressions, excess market returns lead aggregate demand by one year. The fraction of middle-aged (45–64 years) people serves as an instrumental variable in the simultaneous equations. t-Statistics are given between parentheses under coefficient estimates. * Significance at the 10% level. ** Significance at the 5% level. *** Significance at the 1% level.
where RT is excess market return and DT represents changes in aggregate demand for stocks in year T. A problem with the VAR model is that it cannot disentangle the effects of demand curves from the effects of information, because changes in aggregate demand are likely to be related to market fundamentals. To filter out the information contents of changes in aggregate demand, we first regress these changes on the lagged fraction of middle-aged people. We then use the predicted values in this regression to replace changes in aggregate demand, DT , in the VAR model. After estimating the model, we can use standard F-tests to assess the predictive power of lagged changes in aggregate demand for excess market returns and vice versa. If the F-statistic for the predictive power of lagged values of DT (RT ) on RT (DT ) is significant, then we can say that DT (RT ) Granger-causes RT (DT ). 5. Empirical results 5.1. IV estimation results Table 2 reports results from the estimations of Eqs. (6) and (7). In all regressions, the dependent variable, excess stock returns, leads the explanatory variables by one year. We focus on predictive regressions, because contemporaneously it is not clear whether stock returns affect demand or demand affects stock returns, but the causality is stronger in predictive regressions.6 Indeed, Goyal (2004) finds that the percentage outflows from the market can predict next year’s stock market returns. In Panel A of Table 2, the explanatory variable is the percentage net outflows from the market as expressed in Eq. (4). The OLS regression shows that there is a statistically significant relation 6
We thank the referee for this point.
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between lagged percentage outflows from the market and excess market returns. This finding is consistent with that in Goyal (2004). However, as we discuss in Section 4, this result has nothing to say about the aggregate demand curve for stocks because unadjusted net outflows from the market may partially reflect information about market fundamentals. 2SLS estimations paint a very different picture on the predictive power of net outflows from the market for excess market returns. As we show in Panel A of Table 2, when we use the fraction of middle-aged people as an instrumental variable, we find no relation between the percentage net outflows from the market and next year’s excess market returns. In Panel B of Table 2, the explanatory variable is the year-to-year changes in the percentage net outflows from the market. Both OLS and 2SLS estimations indicate that the year-to-year changes in the percentage outflows are not related to next year’s excess market returns in any significant way. Overall, the results in Table 2 illustrate the potential difficulty of making causal inferences when one attempts to use the OLS approach to examine the relation between changes in aggregate demand and excess market returns. The results also show that the use of the fraction of middleaged people as an instrumental variable appears to mitigate this difficulty. Further, the findings in Table 2 suggest that the aggregate demand curve for stocks is horizontal, which is consistent with the results of Cha and Lee (2001). 5.2. VAR estimation results Table 3 presents our VAR estimation results. The results in Panel A of Table 3 show that when we use raw (unadjusted) percentage outflows from the market as a measure of aggregate demand for stocks, the null hypothesis that net outflows do not Granger-cause excess market returns is rejected at the 1% level. This result is robust to different numbers of lags. The finding is consistent with the OLS predictive regression result in Table 2 in that raw percentage outflows, which are highly likely to partially reflect information, have predictive power for future market returns. The result also appears to be consistent with that of Goyal (2004) who uses a nontraditional VAR model and finds that net outflows from the market help predict excess returns on the S&P 500 index. In addition, the results in Panel A of Table 3 show that annual excess market returns do not Granger-cause either themselves or net outflows from the market, but lagged net outflows are related to future outflows. However, Panel B of Table 3 tells a different story. In Panel B, we use the predicted values of regressing raw percentage outflows on the fraction of middle-aged people and find that changes in aggregate demand for stocks induced by changes in an information-free variable do not Grangercause excess market returns. This result holds when we use different numbers of lags. Thus, our VAR results further confirm our findings from estimating simultaneous equations, which show that information-free changes in aggregate demand for stocks do not lead to changes in market returns, thereby suggesting a horizontal aggregate demand curve for stocks. 6. Summary and conclusions In this paper we examine whether demand curves for stocks are downward sloping. We focus on the aggregate demand curve for stocks because a horizontal aggregate curve is sufficient for horizontal individual curves. As a measure of aggregated demand for stocks, we follow Goyal (2004) and use net outflows (dividends plus repurchases less net issues) from the stock market scaled by the previous year’s market capitalization.
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Table 3 VAR tests for Granger-causality between changes in aggregate stock demand and excess market returns Lags
Dependent variable: market returns
Dependent variable: aggregate demand
Aggregate demand
Market return
Aggregate demand
Market return
F-statistics
F-statistics
p-Value
F-statistics
p-Value
F-statistics
p-Value
0.8479 0.3813 0.4742
0.4330 0.7668 0.7545
8.7015 9.6755 1.5601
0.0004 0 0.1968
1.8954 0.8164 0.0087
0.1585 0.4897 0.4950
70 69 68
(Panel B) Using predicted outflows 2 0.9172 0.4047 1.0243 3 1.0986 0.3565 0.4227 4 0.8633 0.4913 0.4528
0.3648 0.7374 0.7700
471.9542 289.8647 206.8602
0 0 0
0.0023 0.6224 1.4208
0.9977 0.6032 0.2383
70 69 68
p-Value
(Panel A) Using raw outflows 2 10.6601 0.0001 3 6.3611 0.0008 4 3.7517 0.0087
Observations
The table reports the estimation results of the following bivariate VAR model:
⎧ n n ⎪ ⎪ ⎪ + βj DT −j + εT , R = ω + α R j T −j ⎪ ⎨ T j=1 n
j=1 n
⎪ ⎪ ⎪ αj DT −j + βj RT −j + εT , DT = ω + ⎪ ⎩ j=1
j=1
where RT denotes excess market return and DT represents aggregate demand for stocks in year T. Panel A uses raw percentage outflows from the market (as a proxy for aggregate stock demand), which could reflect information. Panel B uses predicted percentage outflows that we obtain from regressing percentage outflows on the lagged fraction of middleaged (45–64 years) people. Reported in this table are F-statistics for the joint significance of separate, lagged right-hand variables and their corresponding p-values.
Since outflows from the market are likely to be correlated with market fundamentals, we use the fraction of middle-aged people as an instrumental variable to filter out the information contents of net outflows from the market. This instrument is valid because it is correlated with net outflows from the market, but not with market fundamentals (Goyal, 2004). To avoid the influence of temporary price pressures, we use annual data. Our IV and VAR estimation results indicate that information-free changes in aggregate demand for stocks do not lead to changes in excess market returns. This finding provides empirical support for the idea of horizontal demand curves for stocks, which is one of the most important assumptions in finance. References Bakshi, G., & Chen, Z. (1994). Baby boom, population aging, and capital markets. Journal of Business, 67, 165–202. Beneish, M., & Whaley, R. (1996). An anatomy of the ‘S&P game’: The effects of changing the rules. Journal of Finance, 51, 1909–1930. Brennan, M., & Cao, H. (1997). International portfolio investment flows. Journal of Finance, 52, 1851–1880. Cha, H., & Lee, B. (2001). The market demand curve for common stocks: Evidence from equity mutual fund flows. Journal of Financial and Quantitative Analysis, 36, 195–220. Chakrabarti, R., Huang, Jayaraman, W., & Lee, J. (2002). Do international investors’ demand curves for stocks slope down too? Working paper. Georgia Institute of Technology. Chen, H., Noronha, G., & Singal, V. (2004). The price response to S&P 500 index additions and deletions: Evidence of asymmetry and a new explanation. Journal of Finance, 59, 1901–1929.
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Denis, D., McConnell, J., Ovtchinnikov, A., & Yu, Y. (2003). S&P 500 index additions and earnings expectations. Journal of Finance, 58, 1821–1840. Dhillon, U., & Johnson, H. (1991). Changes in the Standard and Poor’s 500 list. Journal of Business, 64, 75–85. Froot, K., & T. Ramadorai (2005). The information content of international portfolio flows. Working Paper. Harvard University/University of Oxford. Ginsburgh, V., & Ours, J. (2003). Expert opinion and compensation: Evidence from a musical competition. American Economic Review, 93, 289–298. Goyal, A. (2004). Demographics, stock market flows, and stock returns. Journal of Financial and Quantitative Analysis, 39, 115–142. Griffin, J., Nardari, F., & Stulz, R. (2004). Are daily cross-border equity flows pushed or pulled? The Review of Economics and Statistics, 86, 641–657. Harris, L., & Gurel, E. (1986). Price and volume effects associated with changes in the S&P list: New evidence for the existence of price pressures. Journal of Finance, 41, 815–829. Jain, P. (1987). The effect on stock price of inclusion in or exclusion from the S&P 500. Financial Analysts Journal, 43, 58–65. Kaul, A., Mehrotra, V., & Morck, R. (2000). Demand curves for stocks do slope down: New evidence from an index weights adjustment. Journal of Finance, 55, 893–912. Lynch, A., & Mendenhall, R. (1997). New evidence on stock price effects associated with changes in the S&P 500 index. Journal of Business, 70, 351–383. Mikkelson, W., & Partch, M. (1985). Stock price effects and costs of secondary distributions. Journal of Financial Economics, 14, 165–194. Scholes, M. (1972). The market for securities: Substitution versus price pressure and the effects of information on share prices. Journal of Business, 45, 179–211. Shleifer, A. (1986). Do demand curves for stocks slope down? Journal of Finance, 41, 579–590. Wurgler, J., & Zhuravskaya, K. (2002). Does arbitrage flatten demand curves for stocks? Journal of Business, 75, 583–608.