Journal Pre-proof Volatility of order imbalance of institutional traders and expected asset returns: evidence from Taiwan Hong-Gia Huang, Wei-Che Tsai, Pei-Shih Weng, Ming-Hung Wu PII:
S1386-4181(20)30015-X
DOI:
https://doi.org/10.1016/j.finmar.2020.100546
Reference:
FINMAR 100546
To appear in:
Journal of Financial Markets
Received Date: 10 March 2019 Revised Date:
9 February 2020
Accepted Date: 11 February 2020
Please cite this article as: Huang, H.-G., Tsai, W.-C., Weng, P.-S., Wu, M.-H., Volatility of order imbalance of institutional traders and expected asset returns: evidence from Taiwan, Journal of Financial Markets, https://doi.org/10.1016/j.finmar.2020.100546. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2020 Published by Elsevier B.V.
Volatility of order imbalance of institutional traders and expected asset returns: evidence from Taiwan Hong-Gia Huang, Wei-Che Tsai, Pei-Shih Weng, Ming-Hung Wu* Abstract We use the newly-developed “volatility of order imbalance” of Chordia et al. (2019) to examine the relation between information asymmetry costs and expected returns across various types of institutional traders, taking advantage of a unique account-level transaction dataset on the Taiwan Futures Exchange. Our results show that the measure of foreign institutional traders is positively related to future market returns, regardless of whether weekly or monthly frequencies are examined. Turning to stock transaction data on institutional investors, the information risk of foreign institutional investors generates risk-adjusted monthly return differentials in the cross-section. Finally, vector autoregression analyses show that with an increase in the order imbalance volatility of foreign institutional investors in the index futures market, there is a corresponding increase in subsequent order imbalance volatility in the stock market, thereby providing evidence of a monthly lead-lag relation between the futures market and the spot market. Keywords:
Information asymmetry; Order imbalance volatility; Institutional investors.
JEL codes:
*
G12; G14; G23
Hong-Gia Huang is a Ph.D. candidate at National Sun Yat-sen University, Taiwan, e-mail: d054030002@ student.nsysu.edu.tw; Wei-Che Tsai is a Professor of Finance at National Sun Yat-sen University, Taiwan, e-mail:
[email protected]; Pei-Shih Weng is an Associate Professor of Finance at National Dong Hwa University, Taiwan, e-mail:
[email protected]; and Ming-Hung Wu (corresponding author) is an Assistant Professor at Beijing Normal University at Zhuhai, China. Address for correspondence: Beijing Normal University at Zhuhai, No 18 Jinfeng Road, Tangjiawan, Zhuhai City, Guangdong Province, China. E-mail:
[email protected].
Volatility of order imbalance of institutional traders and expected asset returns: evidence from Taiwan
Abstract We use the newly-developed “volatility of order imbalance” of Chordia et al. (2019) to examine the relation between information asymmetry costs and expected returns across various types of institutional traders, taking advantage of a unique account-level transaction dataset on the Taiwan Futures Exchange. Our results show that the measure of foreign institutional traders is positively related to future market returns, regardless of whether weekly or monthly frequencies are examined. Turning to stock transaction data on institutional investors, the information risk of foreign institutional investors generates risk-adjusted monthly return differentials in the cross-section. Finally, vector autoregression analyses show that with an increase in the order imbalance volatility of foreign institutional investors in the index futures market, there is a corresponding increase in subsequent order imbalance volatility in the stock market, thereby providing evidence of a monthly lead-lag relation between the futures market and the spot market. Keywords:
Information asymmetry; Order imbalance volatility; Institutional investors.
JEL codes:
G12; G14; G23
1
1. Introduction The costs of information asymmetry between investors directly affect the evolution of trading prices, which is also an essential issue within the asset pricing literature.1 Chordia et al. (2019) use the volatility of order imbalance (VOIB) as a means of measuring the costs of information asymmetry within the U.S. stock market, with an increase in VOIB representing a more severe adverse selection problem within the market; thus, uninformed traders may demand a premium as compensation for the risk they face when trading with informed investors. In this study, we provide further evidence of the relation between expected returns and the VOIB, which we construct from a unique account-level transaction dataset containing complete trading records of stock index futures and options across various types of institutional investors on the Taiwan Futures Exchange (TAIFEX). It is widely recognized that in terms of trading volume, the TAIFEX is among the leading derivatives exchanges in the world.2 Our dataset enables us to identify the trades undertaken by different types of institutional investors, which fall into one of three categories: foreign institutional investors, domestic institutional investors, and proprietary firms.
1
See, for example, Grossman and Stiglitz (1980), Diamond and Verrecchia (1991), Wang (1993), Brennan and Subrahmanyam (1996), Easley et al. (2002), Gârleanu and Pedersen (2004), Easley and O’Hara (2004), Chan et al. (2008), Duarte and Young (2009), Lambert and Verrecchia (2009), and Hwang and Qian (2010). 2 Established in 1998, the TAIFEX offers trading in futures and options across equities, indices, foreign exchanges, interest rates, and commodities. As reported in Euromoney TRADEDATA, in 2018 the TAIFEX was ranked, by volume, as the 15th largest derivatives exchange in the world.
2
There are at least three advantages to be gained from the study of derivatives trading by institutional investors in Taiwan. Firstly, such investors, particularly foreign institutional investors, are generally viewed as informed traders in emerging markets. Barber et al. (2009), for example, point out that from 1995 to 1999, foreign institutional investors in Taiwan were responsible for almost half of all institutional profits, with the aggregate portfolios of these institutional investors enjoying annual abnormal returns of 1.5 percentage points after commission and transaction taxes. This also reflects differences in the informativeness of trading across different types of institutional traders.3 Secondly, based on the seminal work of Easley et al. (1998), under certain conditions, derivatives markets can attract more informed traders.4 Since the VOIB was developed as a means of capturing the costs of information asymmetry in trading, testing the VOIB in a market within which informed traders are frequent participants and also carrying out a comparison between the different groups of institutional investors should provide greater insights and further evidence verifying the validity of this newly-developed measure of information risk.5
3
For emerging markets in particular, many studies have drawn comparisons with the information content of trading for different types of institutional investors; the findings are, however, somewhat inconclusive. The fact that domestic institutional investors are more informed has been demonstrated in numerous studies, including Hau (2001), Choe et al. (2005), Dvořák (2005), Chan et al. (2008), Agarwal et al. (2009), Ferreira et al. (2017), and Agudelo et al. (2019). Other studies have also shown that foreign institutional investors have superior information in trading; these include Grinblatt and Keloharju (2000), Froot et al. (2001), Seasholes (2004), Richards (2005), Chang et al. (2009), Weng et al. (2017), Bae and Dixon (2018), and Weng and Tsai (2018). 4 For example, as opposed to using stocks, informed traders exhibit a preference for the use of options when implicit leverage in options is high and the options are relatively liquid. 5 The empirical analysis of Chordia et al. (2019) is based on VOIBs estimated by the aggregated trades of each firm
3
Thirdly, the TAIFEX is a continuous trading market that has no officially designated market makers for futures trading. This characteristic helps to minimize the effect of inventory allocations by market-makers, and indeed, Chordia et al. (2019) also note that in addition to adverse selection costs, the variability of order flows may increase in line with an increase in inventory risk.6 The VOIB on the TAIFEX should, therefore, be mainly related to information risk. Our results demonstrate that of the three different types of institutional investors in the TAIFEX, the VOIB of foreign institutional investors is positively related to future market returns, a relation theoretically predicted by Chordia et al. (2019), whereas no other positive relations are discernible for the VOIB of domestic institutional investors and proprietary firms.7 The positive relation found between the VOIB of foreign institutional investors and future market returns is also found to be robust, regardless of the estimation frequency (weekly or monthly). In addition to the findings on the intertemporal relationship between VOIB and market returns in the futures market, we also carry out several additional analyses using the VOIB in an attempt to gain a better understanding of the information risk across different types of institutional investors. Our analyses are summarized as follows.
on the stock market in the U.S., whereas we focus on VOIBs in the derivatives markets. 6 Refer to Footnote 29 in Chordia et al. (2019) for more details. 7 We focus on the index futures market, which is largely in line with the theoretical framework of Wang (1993) in which the intertemporal asset-pricing model allowed traders to invest in a riskless asset and only one risky asset.
4
Firstly, we construct monthly VOIB measures for individual stocks and carry out Fama-MacBeth regression analyses to test the cross-sectional predictive power of the VOIB on the subsequent-month stock returns, and once again, the VOIB of foreign institutional investors is found to be positively associated with individual stock returns in the subsequent month. Interestingly, the return predictability can also be found in the VOIB of proprietary firms in the stock market. These results suggest that the trading activities of both foreign institutional investors and proprietary firms in the stock market are more closely related to information risk than those of domestic investment institutions, thereby implying that trading against foreign institutional investors or proprietary firms is likely to lead to greater adverse selection costs. The implications of our findings are in line with the finding of Huang and Shiu (2009), who note that foreign institutional traders in Taiwan tend to enjoy superior information in stock selection over local traders. Similarly, Fecht et al. (2018) demonstrate that German banks have a tendency to exploit their information advantage to sell stocks from their proprietary portfolios to their retail customers, with these retail investors subsequently incurring losses due to underperformance. Secondly, we use vector autoregression (VAR) to examine the monthly lead-lag relation between trading activity in the futures and stock markets. We find that the VOIB of the futures market primarily leads the VOIB in the stock market for foreign institutional investors, which
5
implies that the information risk of foreign institutional trading in the futures market is transmitted to its spot market. Return correlations or lead-lag relations between different markets have been identified; for example, Stoll and Whaley (1990) and Chan (1992) find that S&P 500 futures returns tended to lead the returns on the S&P 500 Index.8 Conversely, another line of related studies focuses on market-wide risk spillover between the markets.9 As compared to the findings within the extant literature, we show that information risk caused by a specific type of investor can also spillover into different financial markets and the VOIB works well as a risk measure capturing the impact. Thirdly, we make use of index option transactions data on the TAIFEX to re-calculate the VOIB for each type of institutional investor and provide a robustness test on the validity of the VOIB in a different derivatives market. Similar to our earlier findings, the VOIB of foreign institutional investors in the options market is again found to have a significantly positive impact on subsequent underlying index returns, whereas no such return predictability is found for the VOIB of other institutional investors. In short, the VOIB derived from options transactions reveals the information asymmetry costs exclusively in foreign institutional trading in the options market.
8
See also Erb et al. Viskanta (1994), Longin and Solnik (1995), and Karolyi and Stulz (1996). See, for example, Bekaert and Harvey (1997), ), Bekaert et al. (2005). Kim et al. (2005) and Asgharian and Nossman (2011). 9
6
While the Chordia et al.’s (2019) VOIB is designed as a new measure providing an intuitive explanation and friendly application to information risk, we aim to contribute to the development and application of the VOIB by providing empirical evidence on different types of institutional investors in an attempt to provide a better understanding of the information risk. We also contribute to the debate on the effect of asymmetric information on required returns by adding further evidence on foreign institutional trading. Although some studies provide support for the impact of information risk on asset pricing,10 Hughes et al. (2007) and Lambert et al. (2007) argue that information risk in larger economies is either fully diversifiable or subsumed by existing risk factors. Our overall findings indicate that the VOIB works well in representing the costs of information asymmetry when trading against informed institutional investors under various scenarios, with our results remaining robust under diverse sampling frequencies, alternative measures of trading activity, and different empirical approaches. As compared to the original work, we extend the application of VOIB to derivatives trading, thereby enriching the current research in the related fields. Furthermore, our analyses of different types of traders in Taiwan, including two domestic investment institutions and foreign investment institutions, provide new insights into trading behavior across different types of institutional investors and enhance our understanding of
10
See, for example, Easley et al. (2002), Easley and O’Hara (2004), and Gârleanu and Pedersen (2004).
7
their informational roles in the emerging markets. The remainder of the paper is organized as follows. Descriptions of the data used in this study and the variable construction for our examination of the VOIB are provided in Section 2, followed in Section 3 by the presentation of our results on the different types of institutional investors. In Section 4, we summarize the additional results. Finally, the conclusions drawn from this study are presented in Section 5.
2. Data and variable definitions 2.1 Data description The datasets are from two official exchanges in Taiwan (the Taiwan Futures Exchange (TAIFEX) and the Taiwan Stock Exchange (TWSE)). We construct the weekly VOIB in the index futures market based on a unique account-level transaction dataset obtained from the TAIFEX; we also use the daily-reported trading statistics on institutional investors in the TAIFEX to calculate the monthly VOIB. As for the spot market, we collect daily-reported trading volume with trade direction (buys or sells) for different types of institutional investors in the TWSE to construct the monthly VOIB for each stock. Detailed descriptions of each of the datasets are provided in the following subsections. 2.1.1 Account-level dataset and the TAIFEX With the one exception of the opening call auction for pre-market submissions, all transactions on 8
the TAIFEX are undertaken continuously from 8:45 a.m. to 1:45 p.m. The electronic trading system on the TAIFEX matches the submitted orders based on the price-time priority rule, where market orders have more precedence than limit orders. Our account-level dataset contains each transaction in Taiwan stock market index futures (TXF) over our January 1, 2003 to December 31, 2011 sample period.11 The TXF dataset contains complete trading information on each transaction, including position indicators (opening or closing), trading direction indicators (buy or sell), an identifier of institutional investors, trade date, time, quotes, price, and volume. One of the advantages of this dataset is that we can identify the exact trading direction without referring to any identification algorithms, such as the Lee and Ready (1991) algorithm, which would inevitably introduce some estimation errors. Furthermore, since we are able to identify the types of traders, we can also examine and compare different types of institutional investors, including foreign institutions, domestic institutions, and proprietary firms. For each type of institutional investor, we construct a time series of trading volume, at 60-minute intervals, for each trading day in our sample period. The summary statistics on trading volume by the different types of investors are presented in Panel A of Table 1. The results show that the trading volume of proprietary firms is the highest among the three groups of investors. The average hourly trading volume for proprietary firms 11
The TXF is the most actively traded index futures product on the TAIFEX. The TXF index usually represents the aggregated price level for the entire stock market in Taiwan.
9
was 4,348 contracts, the average for foreign institutional investors was 2,120 contracts, and the average for domestic institutional investors was 359 contracts.
2.1.2 TAIFEX daily transaction dataset In the TAIFEX reports, the trading statistics for each type of institutional investor are aggregated at daily frequency, which enables us to construct the monthly VOIB derived from the daily trading information. We collected the related data and constructed a dataset of daily TXF transactions from January 1, 2003 to December 31, 2018. The dataset contains daily trading statistics, an identifier of the type of trader, trade date, and daily trading volume. The summary statistics on daily trading volume for each type of institutional investor are reported in Panel B of Table 1. The results show that the average daily trading volume for foreign investment institutions was 39,523 contracts, the average for domestic institutional investors was 2,068 contracts, and the average for proprietary firms was 29,315 contracts. Over our 2003-2018 sample period, foreign institutional investors were the largest investor group in terms of their daily trading volume, which differs somewhat from the results reported in Panel A of Table 1. However, this difference merely reflects the fact that foreign institutional trading has seen the most rapid growth among the various types of institutional investors, with the most distinct increases occurring from 2009 to 2018.
10
2.1.3 TWSE daily transaction dataset The TWSE is a call auction stock market that has very frequent auction intervals throughout the entire regular trading session from 9:00 a.m. to 1:30 p.m.12 Similar to the TAIFEX, the TWSE reports aggregated trading statistics on each type of institutional investor at daily frequency, which enables us to construct the monthly VOIB for stock trading. We collect the related data and construct a dataset of daily transactions for each stock on the TWSE from January 1, 2003 to December 31, 2018. Our dataset contains daily trading statistics, an identifier of the type of trader, trading date, daily trading volume, daily price information (open, close, high, and low) and listing code for each stock. The summary statistics on the TWSE for each type of institutional investor are presented in Panel C of Table 1. Referring back to Panel B of Table 1, the stock trading volume for foreign institutional investors is found to be higher than that for domestic institutional investors and proprietary firms. From 2003 to 2018, the average daily trading volume in all stocks (in units of 1,000 shares) by foreign institutional investors was 941,693, while the average for domestic institutional traders was 108,841 and the average for proprietary firms was 134,464.
12
The TWSE has been promoting continuous trading under a gradual process. As the first stage of a master plan, warrants on the TWSE were traded continuously from June 2010. Between 2013 and 2014, the auction intervals for stock trading on the TWSE, which constitutes around 90% of total market trading, were shortened; on July 1, 2013, they went from the original 20 seconds to 15 seconds, to 10 seconds on February 24, 2014, and to the current five-second interval on December 29, 2014. The TWSE will implement a continuous trading system on March 23, 2020.
11
2.2 Order imbalance volatility In proposing the VOIB as a proxy for information asymmetry costs, Chordia et al. (2019) demonstrate that volatility in order flows varies positively with the measures of adverse selection costs, thereby implying that higher order flow volatility indicates that informed investors are trading more actively in the market, which ultimately causes higher information asymmetry in market trading. In line with Chordia et al. (2019), we use the VOIB as a primary variable in our analysis; however, the main analysis in our work differs from that of Chordia et al. (2019) in two important ways. First, Chordia et al. (2019) examine the VOIB in the U.S. stock market without any means of identifying the types of traders, whereas our analysis of the VOIB enables us to identify the various types of institutional investors in both the derivatives market and the stock market in Taiwan. Secondly, in addition to carrying out an analysis of the monthly VOIB in the stock market, as in Chordia et al. (2019), we also construct the weekly and monthly VOIB based on different datasets in order to test the validity of the VOIB in derivatives trading. In the following subsections, we explain the definition of the VOIB we use in our analysis.
2.2.1 Weekly VOIB for TXF trading For our construction of the weekly VOIB for futures trading, we divide each trading day into five consecutive 60-minute intervals and follow Chordia and Subrahmanyan (2002, 2004) to calculate
12
the order imbalance (hereafter, OIB) in each interval for each type of institutional investor. The OIB is defined as follows: = ( − ) / ( + ) ,
(1)
where OIBi is the order imbalance in interval i, and Bi (Si) is the buy (sell) volume in the same interval. We use all of the intraday OIBs in the same week to compute the weekly VOIB as the standard deviation of all of the OIBs in week t; for example, in a regular week of five trading days, the weekly VOIB for a specific type of institutional trader is defined as:
= ∑ (, − , ) .
(2)
2.2.2 Monthly VOIB for TXF trading To construct the monthly VOIB for futures trading, similar to equations (1) and (2), we use the daily OIBs of the TXF trading undertaken by each type of institutional trader to calculate the standard deviation of all daily OIBs in a given month. For a month t, which has N trading days, the monthly VOIB for a specific type of institutional trader is defined as:
= ∑ (, − , ) .
(3)
2.2.3 Monthly VOIB for stock trading For our calculation of the monthly VOIB for each individual stock on the TWSE, similar to
13
equation (3), we use all daily OIBs in a given month for a specific type of institutional investor, and only include those stocks with trading volume for at least 14 trading days in order to avoid any possible biases from illiquid stocks when estimating the VOIB. For a month t, which has N trading days, the monthly VOIB of stock j for a specific type of institutional trader is defined as:
, = ∑ (,, − ,, ) .
(4)
2.2.4 Descriptive statistics of VOIB and OIB The summary statistics on the different types of institutional investors are reported in Table 2 (in Panel A and Panel B with regard to futures trading, and in Panel C for stock trading). In the TAIFEX and the TWSE, both foreign institutions and domestic institutions are generally found to have higher VOIBs than proprietary firms, regardless of the different proxies used for trading volume. This finding implies that as compared to proprietary firms, foreign and domestic institutional investors will tend to adjust their trading direction more frequently. Furthermore, the OIBs of foreign institutional investors and domestic institutional investors are much higher than those of proprietary firms, which implies that on average, both foreign and domestic institutional investors are more likely to be net buyers in both the futures market and the stock market. Table 3 presents the correlation coefficients for the VOIB and OIB based on different
14
frequencies and markets for each type of institutional investor. The VOIBs of foreign institutions are positively correlated with the VOIBs of proprietary firms, and negatively correlated with the VOIBs of domestic institutions. Conversely, the OIBs of foreign institutions are negatively correlated with the OIBs of both proprietary firms and domestic institutions, which implies that foreign institutional investors and domestic institutional investors have different trading directions at the same time. Overall, with regard to institutional investors, we find that the VOIB is weakly correlated with the OIB. Within the extant literature, OIB is commonly viewed as a measure of information trading, but Chordia et al. (2019) develop VOIB as a proxy for the risk of information asymmetry.13 The weak correlation between VOIB and OIB may therefore simply reflect the essential difference between the two measures when they are used to represent the trading activities of investors.
3. Results 3.1 Weekly returns and information asymmetry costs on the TAIFEX In this subsection, we examine the relation between weekly returns and information asymmetry costs on the TAIFEX, starting with an examination of the predictability of the returns using the 13
Many prior studies use order imbalance to measure the information content of trading activity (e.g., Chordia et al., 2002, 2008; Easley et al., 2008; Subrahmanyam, 2008; Barber et al., 2009; O’Hara et al., 2012; Easley et al., 2016).
15
weekly VOIB of each trader type on the TAIFEX. We run the following regression model with Newey-West (1987) correction for the standard errors: 4
4
Rett = a0 + β1OIBtk–1 + β2VOIBtk–1 + Σi =1 β2+i Rett–i + Σi =1 β6+i ∆FutVolt–i +
(5)
Σi4 1 β10 i Illiqt i + Σi4 1 β14 i SDRett i + εt, =
+
–
=
+
–
where Rett refers to the return of the market index on the TAIFEX in week t;14 OIBtk–1 denotes the order imbalance for trader group k in week t-1; VOIBtk–1 is the volatility of order imbalance for trader group k in week t-1; and k is the indicator of foreign institutional investors (FI), domestic institutional investors (DI) or proprietary firms (PF). Our control variables include the following variables, each with their own four lags (of approximately one month): market returns, changes in total trading volume, a market illiquidity measure, and the volatility in market returns. Ret denotes the logarithm of index futures returns; ∆FutVol refers to the change in total trading volume between weeks; Illiq represents the Amihud’s (2002) illiquidity measure, which is the weekly average ratio of the daily absolute return divided by the total trading volume and multiplied by 104 in coefficient adjustment; SDRet denotes the standard deviation in the volatility of the daily returns in a given week. We calculate the weekly VOIB using the trading volume (Q) and the dollar volume (D) and then conduct a
14
We denote the market index on the TAIFEX as the TXF index, since the TXF is the leading stock market index futures contract. We then calculate the weekly market return as the logarithmic return of the TXF index for each week.
16
regression analysis based on equation (5).15 FI
Table 4 shows that the coefficients of VOIBt –1 are significantly positive in Models (1) and (2), which suggests that the VOIB of foreign institutional investors is positively correlated with subsequent-week market returns; however, similar relations with the VOIB are not discernible for FI
the other types of institutional traders.16 In addition, the coefficient on OIBt –1 has a significantly positive correlation with market returns in week t, a finding which is consistent with those of Chang et al. (2009) and Weng et al. (2017), both of whom suggest that foreign institutional trading tends to be more informed on price movements than domestic institutional trading on the TAIFEX. Overall, the above findings in Table 4 show that foreign institutional trading is informed with regard to future returns, and the aggressive participation of foreign institutional investors on the TAIFEX would, therefore, result in higher adverse selection costs. This is also consistent with of Chordia et al.’s (2019) theoretical prediction that active informed traders in the market lead to higher adverse selection costs for market makers or uninformed investors, thereby resulting in higher expected returns to the market as compensation for holding stocks with higher 15
The trading volume denotes the number of TXF contracts, with the dollar trading volume being calculated as the trading volume x NTD 200 x per index point for each TXF contract. 16 The results reported in Table 4 are robust to the weekly VOIB based on the number of transactions as an alternative trading activity measure. The results are also robust to the re-calculation of the weekly VOIB using 15and 30-minute intervals, as opposed to the 60-minute interval. For brevity, these results are not reported in the table.
17
information risk.
3.2 Monthly returns and information asymmetry costs on the TAIFEX Similar to Subsection 3.1, in this section, we examine the relation between market returns and the VOIB at monthly horizons on the TAIFEX, carrying out a regression analysis based on the following equation with Newey-West (1987) correction for the standard errors: Rett = a0 + β1OIBtk–1 + β2VOIBtk–1 + β3Rett–1 + β4∆FutVolt–1 +
(6)
β5Illiqt–1 + β6SDRett–1 + εt,
where Rett denotes the market return on the TXF index in month t; OIBtk–1 is the order imbalance for trader group k in month t-1; VOIBtk–1 is the VOIB of trader group k in month t-1; k indicates the type of investor, comprising foreign institutional investors (FI), domestic institutional investors (DI), and proprietary firms (PF). The OIB and VOIB are calculated using alternative trading measures, which are the trading volume (Q) and the dollar trading volume (D), in order to ensure the robustness of our results. We also control for the following lagged variables in the model: Rett–1 is the market return on the TXF index in month t-1; ∆FutVolt–1 is the change in the total trading volume in month t-1; Illiqt–1 refers to the Amihud’s (2002) illiquidity measure, which is defined as the monthly average ratio of the daily absolute return divided by the total trading volume in month t-1 and multiplied by 104 in coefficient adjustment; and SDRett–1 denotes the standard deviation in the daily market
18
returns in month t-1. Similar to the results in Table 4, we find a positive relationship between the VOIB of foreign institutional investors and subsequent-month market returns in Models (1) and (2) of Table 5. For both domestic institutional investors and proprietary firms, we find no evidence of their VOIB having any significant impact on market returns.17 This result provides further support for the return predictability of the weekly VOIB of foreign institutional investors reported in Table 4. However, no significant relation is discernible in Table 5 between the OIB of foreign institutional traders and subsequent-month market returns, which indicates that the impact of the OIB of foreign institutions on market returns at the monthly horizon is weakened in significance. This finding is consistent with the argument of Chordia and Subrahmanyam (2004), and suggests that the order imbalance, as an information trading proxy, works better in predicting short-horizon returns.18 To summarize, we show that the positive relation between the VOIB of foreign institutional investors and future market returns survive weekly and monthly horizons in futures trading. This finding indicates that of the various types of institutional investors, foreign institutional trading
17
Again, our findings are robust when adopting the number of transactions as an alternative trading volume measure for the calculation of the VOIB. 18 Earlier related studies focus on short-horizon return movements, as can be seen from the tests of Lehmann (1990), Lo and MacKinlay (1990), and Conrad et al. (1994).
19
tends to be most closely related to the risk of information asymmetry in futures transactions on the TAIFEX.
3.3 Information asymmetry costs and subsequent-month returns on the TWSE In order to make our findings compatible with Chordia et al. (2019), we follow the Fama-MacBeth (1973) approach to examine the cross-sectional predictive power of VOIB on equity returns in the TWSE. Thus, we use standard Fama-MacBeth regressions involving a two-stage approach. In the first stage, we run the following cross-sectional regression for all individual stocks in each month: Reti,t = a0 + β1OIBki,t–1 + β2VOIBki,t–1 + β3∆StkVoli,t–1 + β4Turni,t–1 +
β5SDTurni,t–1 + β6Sizei,t–1 + β7SDReti,t–1 + β8Illiqi,t–1
+
β9Reti,t–1 + β10Reti,t–2,t–12 + β11SVOIBi,t–1 + β12STurni,t–1
+
β13SSDTurni,t–1 + β14SSDReti,t–1 + β15SIlliqi,t–1 + εi,t,
(7)
where Reti,t is the return of firm i in month t; OIBki,t–1 is the order imbalance of firm i for trader group k in month t-1; VOIBki,t–1 is the VOIB of firm i for trader group k in month t-1; k indicates the type of investor, comprised of foreign institutional investors (FI), domestic institutional investors (DI), and proprietary firms (PF); ∆StkVoli,t–1 denotes the monthly change in dollar trading volume of firm i; Turni,t–1 is the turnover ratio, which is the number of trades divided by shares outstanding; SDTurni,t–1 is the standard deviation in the turnover ratio (Turn) over the
20
previous three-year (36-month) period; Sizei,t–1 is defined as market capitalization (in millions); SDReti,t–1 denotes the standard deviation in the daily individual stock returns in a given month; Illiqi,t–1 refers to the Amihud’s (2002) illiquidity measure, which is the monthly average ratio of the daily absolute return divided by the dollar trading volume in month t-1; Reti,t–2,t–12 is the cumulative stock returns for firm i over the previous 12-month period; SVOIBi,t–1 is the shock to the order imbalance volatility, which is the difference between the VOIB in month t-1 and the six-month moving average of the VOIB from month t-7 to month t-2; STurni,t–1, SSDTurni,t–1, SSDReti,t–1, and SIlliqi,t–1 are the respective shocks on Turni,t–1, SDTurni,t–1 , SDReti,t–1, and Illiqi,t–1, which are defined as SVOIBi,t–1. In the second stage, after collecting a time series of slope coefficients (α0,t, β1,t, β2,t,…, β15,t), we make the inference based on the time series regression of the coefficients to assume that the coefficients are independently and identically distributed (i.i.d.) over time. We also use robust Newey-West correction for the standard errors in the coefficients to examine the results when we allow the autocorrelation structures to exist in the time series of the coefficients. The Fama-MacBeth regression not only provides us with a straightforward way of verifying the significance of the firm-level return predictability of the VOIB for each investor type, but also enables us to simultaneously control for abundant firm characteristics. The results of the Fama-MacBeth analysis on both raw returns and open-to-close returns are
21
reported in Table 6. The results show the time series average of the coefficient estimates from the cross-sectional regressions for each month, with the standard errors being adjusted using FI
Newey-West correction. The coefficients on VOIBt – 1 are 1.3071 (t-statistic = 2.67) in Model (1) and 1.0662 (t-statistic = 2.31) in Model (2). For economic significance, an increase of one standard deviation in the VOIB of foreign institutions in the current month leads to subsequent-month increases of 26 bps for Model (1) and 21 bps for Model (2). These results indicate that the VOIB of foreign institutional investors can explain the individual stock returns in the subsequent month on the TWSE. In addition to foreign institutional investors, the return predictability can also be found in the VOIB of proprietary firms, where the coefficients on PF
VOIBt – 1 are 2.1197 (t-statistic = 3.92) in Model (5) and 2.0208 (t-statistic = 3.82) in Model (6). We also adopt the framework of Chordia et al. (2019) to examine the relations between future returns and the shocks to VOIB. Our results in Table 6 show that the time series averages of the coefficient estimates on the shocks to VOIB are negative in the monthly cross-sectional regressions, which is consistent with the findings in Table 5 of Chordia et al. (2019). We can therefore confidently rely on the Chordia et al. (2019) explanations from the viewpoint of the limits to arbitrage or delayed responses of investors to the shock.19
19
We would like to express our sincere gratitude to the anonymous reviewer for this constructive suggestion, which
22
In summary, the trading activities of foreign institutional investors and proprietary firms within the stock market are more closely related to information risk than those of domestic institutions, which indicates that trading against foreign institutional investors or proprietary firms is likely to lead to greater adverse selection costs. We also conduct additional tests to examine the cross-sectional predictive power of VOIB on equity returns. Firstly, the financial crisis period partially initiated by the bankruptcy of Lehman Brothers is excluded from our original sample, as the accentuated stock market volatility is likely to generate outliers for the estimation of VOIB during this period, leading to potentially spurious results from the full-period analysis. Kahle and Stulz (2013) define the Lehman Brothers bankruptcy period as running from 2008:Q4 to 2009:Q1; we follow this definition to exclude all observations between October 2008 and March 2009 and then re-run the Fama-MacBeth analysis. Secondly, we follow Chordia et al. (2019) to recalculate the shock variable as the difference between the current month and the 12-month moving average in the previous month as a control variable. The results of these two tests are reported in Table 7. Similar to the findings reported in Table 6, the VOIBs of foreign institutional investors and proprietary firms have significant impacts on individual stock returns in the subsequent month, while the VOIBs of domestic institutional investors demonstrate no significant impact on subsequent-month stock returns.
significantly enhanced our understanding of shocks to the VOIB.
23
3.4 Lead-lag relation between trading activity in the futures and stock markets The extant literature is concerned with return correlations or lead-lag relations between different markets. In one line of research, the returns on the futures market tend to lead the returns on the spot market (e.g., Stoll and Whaley, 1990; Chan, 1992; Erb et al., 1994; Longin and Solnik, 1995; Karolyi and Stulz, 1996)., while another line of research points to the risk spillover between markets (e.g., Bekaert and Harvey, 1997; Bekaert et al., 2005; Kim et al., 2005; Asgharian and Nossman, 2011). In Taiwan, foreign institutional investors are aggressive participants in both the futures market and the stock market. Since our findings show that VOIB works well in measuring the risk of information asymmetry in trading activity on the TAIFEX and TWSE, particularly for foreign institutional investors, we use a vector autoregression (VAR) to examine the lead-lag relation of information risk between the futures and stock markets to enrich our understanding of the validity of VOIB, and to contribute to the literature on spillover effects between markets. We consider an eight-equation VAR system incorporating eight variables at monthly horizons, which is expressed as: I
X t = α0 + ∑ βik X t −i + ε t ,
(8)
i=1
where Xt is a vector representing VOIBStk,k, VOIBFut,k, OIBStk,k, OIBFut,k, Ret, ∆FutVol, Illiq, and
24
SDRet; the monthly volatility in the order imbalance of trader group k in the whole stock market (futures market) is denoted by VOIBStk,k (VOIBFut,k); k indicates the type of investor, comprised of foreign institutional investors (FI), domestic institutional investors (DI), and proprietary firms (PF); OIBStk,k (OIBFut,k) denotes the monthly order imbalance of trader group k in the whole stock market (futures market); Ret; ∆FutVol; Illiq and SDRet are the same as in equation (6). We choose the lag length (I = 3) based on the Akaike information criterion (AIC). Table 8 reports the VAR results. Similar to the previous tables, we report the VOIB for each type of trader by alternative trading volume measures, the trading volume (Q), and the dollar trading volume (D). For brevity, we only report the coefficients of VOIBk and OIBk and particular on the coefficients of VOIB
Fu t,k t –1
Stk,k
on VOIBt
S t k,k
Fut,k
and those of VOIB t– 1 on VOIBt
for each type of
institutional trader, which respectively indicate the effects of futures-led-stocks and stock-led-futures through the information risk measured by VOIB. The results in columns (1) and (3) in Panel A of Table 8 reveal that the VOIB of the futures market leads the VOIB of the stock market for foreign institutional investors at the 5% and 1% significance levels, respectively, thereby suggesting that the information risk of foreign institutional trading in the futures market is transmitted to its spot market. However, our results show no similar effects in the VOIB of domestic institutional investors and proprietary firms as the coefficients of
25
Fu t,DI
Stk,DI
VOIB t –1 on VOIBt
and those of VOIB
Fu t,PF t –1
Stk,PF
on VOIBt
are insignificant.
We also find no evidence of the information risk in stock market trading leading the information risk in futures market trading for any specific type of institutional investor. Our findings provide support for the related studies that report that the futures markets tend to lead their spot markets in different ways, and show that the risk of information asymmetry in trading caused by specific types of investors can have spillover effects from the futures market to the spot market. We also plot the impulse responses of our VAR results in Figures 1 and 2. In the figures, the VOIB of each type of institutional investor is based on the trading volume (Figure 1) and the dollar trading volume (Figure 2). In each plot, the horizon of the impulse response is up to 10 months. Figure 1 shows that the impulse response of VOIBStk,FI to VOIBFut,FI is consistently positive throughout each horizon, implying that the shock of the order flow volatility in futures trading by foreign institutional investors increases the order flow volatility in stock trading and has a long-run positive impact. As in the results reported in Table 8, Figures 1 and 2 show that no other types of investors having the same impact on VOIB, as the impulse responses have similar patterns in the other plots and are either negative or not distinctly above zero.
4.
Constructing the VOIB from index option trading
26
In addition to the stock market, our findings show that the VOIB of futures trading works well in capturing the information risk; however, index options on the TWSE index (traded as TXOs) are also a rapidly-growing trading contract of derivatives on the TAIFEX and attracting increasing numbers of institutional investors. Numerous studies suggest that informed traders also actively participate in the options markets given that options trading has higher leverage than futures trading and can be more profitable for informed traders (e.g., Easley et al., 1998; Roll et al., 2010; Johnson and So, 2012; An et al., 2014; Hu, 2014). Such aggressive participation by informed traders should result in a higher risk of information asymmetry in the options market, and if this is indeed the case, then the question naturally arises as to how the VOIB performs in the options market. We therefore provide additional analysis to test the validity of the VOIB in TXO trading. The TAIFEX launched TXO trading in 2003, so a unique account-level transaction dataset of index options is available for us to extend our primary analysis to the options market, with the overall aim of identifying whether the VOIB can also effectively measure the information risk arising from the trading activities of institutional investors in a different derivatives market. Similar to our dataset of TXF contracts, the account-level dataset of options trading consists of all TXO transactions, providing the type of options (call/put), strike price, time to expiration, trading direction, transaction price, trading volume, and an identifier of types of investors. The dataset covers all trading days from January 1, 2003 to December 31, 2008.
27
We follow Bae and Dixon (2018) to calculate the option OIB (OOIB) for index options.20 For our construction of the weekly VOIB for the TXO, we again divide each trading day into five consecutive 60-minute intervals, then define the OOIB as the difference between the net positions for call and put options:
OOIBi = (Long Calli - Short Calli ) - (Long Puti - Short Puti ) ,
(9)
where OOIBi is the option OIB in interval i. We then standardize them by dividing the total option (call and put) trading volume by the corresponding investors in interval i. We also estimate the volatility of OOIB (VOOIB) at weekly horizons, as in equation (2), to test the return predictability of VOOIB for each type of institutional investor. We then run the following adjusted model based on equation (5): 4
4
Rett = a0 + β1OOIBkt–1 + β2VOOIBkt–1 + Σi =1 β2+i Rett–i + Σi =1 β6+i ∆OptVolt–i +
(10)
Σi4 1 β10 i Illiqt i + Σi4 1 β14 i PSPRt i + εt, =
+
–
=
+
–
where Rett denotes the market return in week t; OOIBkt–1 is the options order imbalance of trader group k in week t-1; VOOIBkt–1 is the options VOIB of trader group k in week t-1; and k indicates the type of investor, comprised of foreign institutional investors (FI), domestic institutional investors (DI), and proprietary firms (PF). The control variables include variables with their own four lags (of approximately one month), 20
Similar to the analysis on the TXF, we consider only current-month TXO contracts to calculate options VOIB.
28
comprised of the market return, the change in total options trading volume (∆OptVol), Amihud’s (2002) illiquidity measure (Illiq), and the average of all daily bid-ask spreads within a given week (PSPR, %). The estimations of the standard errors in the regression coefficients are carried out using Newey-West (1987) correction. The results of equation (10) are reported in Table 9. Similar to our earlier reported findings in Table 4, we find that the VOOIB of foreign institutional investors has a coefficient estimate of 2.4286 and significantly positive impact on subsequent-week market returns at 5% level, whereas the returns for the VOOIB for the other investment institutions have no discernible predictive ability. These findings indicate that the VOOIB derived from options transactions also represents the adverse selection costs for foreign institutional trading in TXOs on the TAIFEX.21 Similar to our analysis for Table 5, in equation (10) we examine the relation between VOOIB FI
and market returns at monthly horizons. The results are in Table 10, we find that VOOIBt – 1 is still significantly and positively correlated with subsequent-month market returns, although the VOOIB for domestic institutions and proprietary firms have no discernable predictive ability on the
21
When calculating the weekly VOOIB based on the number of transactions in the index options market, we find that foreign institutional investors’ VOOIB still has a significantly positive impact on subsequent-week market returns. The results are also robust to the re-calculation of the weekly VOOIB using 15- and 30-minute intervals, as opposed to the 60-minute interval. For brevity, these results are not reported, but are available upon request.
29
returns.22 Overall, the results in Tables 9 and 10 provide further support for our findings reported in Table 4 and Table 5. The results suggest that the order flow volatility is also suitable for capturing the risk of information asymmetry in the options market. The validity of the VOIB is not, therefore, distinctly affected by different estimation frequencies or different types of derivatives contracts.
5. Conclusions The volatility of order imbalance (VOIB), contributed by Chordia et al. (2019), is a newly-developed proxy measure of the costs of information asymmetry. The higher the VOIB, the more severe the adverse selection problem for trading in the market, leading to uninformed investors requiring higher expected returns as compensation for the risk of trading with informed investors. Given Chordia et al.’s (2019) findings on the U.S. stock market, we provide further evidence of the relation between expected returns and the VOIB in Taiwan. We employ a unique account-level transaction dataset containing complete trading records of index futures and options across various types of institutional investors on the TAIFEX. Our findings are beneficial to the further development of the literature; however, our analysis differs from the work of Chordia et al. (2019) in many respects. Firstly, we extend the 22
Our results remain robust when adopting the number of transactions as alternative trading activity measures for the construction of the monthly VOOIB.
30
application of the VOIB to the derivatives markets, including trading in futures and options markets. Secondly, we construct the VOIB of various types of institutional traders, including two domestic investment institutions and foreign investment institutions in Taiwan, and then examine the informational roles for different types of institutional investors in an emerging market. Thirdly, we provide separate analyses for different data frequencies. Finally, using the VOIB of various types of institutional investors, we identify a spillover effect between the futures market and the spot market based upon our VAR analysis. Overall, our findings show that of the three types of institutional investors in Taiwan, foreign institutional trading exhibits the closest association with the risk of information asymmetry, regardless of whether we examine derivatives trading or stock trading. The validity of the VOIB for foreign institutional traders on the TAIFEX is also robust to weekly and monthly horizons. Furthermore, in futures trading by the different types of traders, only the VOIB of foreign institutional traders leads stock trading, which suggests a spillover effect of the information risk embedded in foreign institutional trading between the futures market and the stock market. We conclude that the VOIB works well in representing the costs of information asymmetry when trading against informed institutional investors under various scenarios, with our results remaining robust under diverse sampling frequencies, alternative measures of trading activity,
31
and various empirical approaches. Echoing the work of Chordia et al. (2019), we believe that the simple computation and flexibility of the VOIB make it a valid measure for studying the various trading activities and events in the financial markets. We leave other applications of the VOIB to future research.
32
ACKNOWLEDGEMENT We would like thank Tarun Chordia for inviting us to submit our manuscript. We also acknowledge and greatly appreciate the valuable support provided by the Ministry of Science and Technology of Taiwan, the Taiwan Stock Exchange Corporation, and the Taiwan Futures Exchange. The views expressed in this paper are those of the authors alone and should not be attributed to the Taiwan Stock Exchange Corporation or the Taiwan Futures Exchange. Our work has also benefited from suggestions provided by the session participants at the 26th Conference on the Theories and Practices of Securities and Financial Markets and the 10th Financial Markets and Corporate Governance Conference. Special thanks go to Te-Feng Chen, Tao Chen, Shuai Qiao, Thu Phuong Truong, Yaw-Huei Jeffrey Wang, John Wei, and Jimmy Yang.
33
References Agarwal, S., S. Faircloth, C. Liu and S.G. Rhee (2009), ‘Why do Foreign Investors Underperform Domestic Investors in Trading Activities? Evidence from Indonesia’, Journal of Financial Markets, 12(1): 32-53. Agudelo, D.A., J. Byder and P. Yepes-Henao (2019), ‘Performance and Informed Trading; Comparing Foreigners, Institutions and Individuals in an Emerging Stock Market’, Journal of International Money and Finance, 90: 187-203. Amihud, Y. (2002), ‘Illiquidity and Stock Returns: Cross-section and Time Series Effects’, Journal of Financial Markets, 5(1): 31-56. An, B.J., A. Ang, T.G. Bali and N. Cakici (2014), ‘The Joint Cross-section of Stocks and Options’, Journal of Finance, 69(5): 2279-337. Asgharian, H. and M. Nossman (2011), ‘Risk Contagion among International Stock Markets’, Journal of International Money and Finance, 30(1): 22-38. Bae, K.H. and P. Dixon (2018), ‘Do Investors use Options and Futures to Trade on Different Types of Information? Evidence from an Aggregate Stock Index’, Journal of Futures Markets, 38(2): 175-98. Barber, B.M., Y.T. Lee, Y.J. Liu and T. Odean (2009), ‘How Much Do Individual Investors Lose by Trading?’, Review of Financial Studies, 22(2): 609-32. Barber, B.M., T. Odean and N. Zhu (2009), ‘Do Retail Trades Move Markets?’, Review of Financial Studies, 22(1): 151-86.
34
Bekaert, G., and Harvey, C. R. (1997), ‘Emerging Equity Market Volatility, Journal of Financial Economics, 43(1): 29-77. Bekaert, G., C.R. Harvey and A. Ng (2005), ‘Market Integration and Contagion’, Journal of Business, 78(1): 39-69. Brennan, M.J. and A. Subrahmanyam (1996), ‘Market Microstructure and Asset Pricing: On the Compensation for Illiquidity in Stock Returns’, Journal of Financial Economics, 41(3): 441-64. Chan, K. (1992), ‘A Further Analysis of the Lead-lag Relationship between the Cash Market and Stock Index Futures Market’, Review of Financial Studies, 5(1): 123-52. Chan, K., A.J. Menkveld and Z. Yang (2008), ‘Information Asymmetry and Asset Prices: Evidence from the China Foreign Share Discount’, Journal of Finance, 63(1): 159-96. Chang, C.C., P.F. Hsieh and H.N. Lai (2009), ‘Do Informed Option Investors Predict Stock Returns? Evidence from the Taiwan Stock Exchange’, Journal of Banking and Finance, 33(4): 757-64. Choe, H., B.C. Kho and R.M. Stulz (2005), ‘Do Domestic Investors Have an Edge? The Trading Experience of Foreign Investors in Korea’, Review of Financial Studies, 18(3): 795-829. Chordia, T., J.F. Hu, A. Subrahmanyam and Q. Tong (2019), ‘Order Flow Volatility and Equity Costs of Capital’, Management Science, 65(4): 1520-51. Chordia, T., R. Roll and A. Subrahmanyam (2002), ‘Order Imbalance, Liquidity and Market Returns’, Journal of Financial Economics, 65(1): 111-30. Chordia, T., R. Roll and A. Subrahmanyam (2008), ‘Liquidity and Market Efficiency’, Journal of
35
Financial Economics, 87(2): 249-68. Chordia, T. and A. Subrahmanyam (2004), ‘Order Imbalance and Individual Stock Returns: Theory and Evidence’, Journal of Financial Economics, 72(3): 485-518. Conrad, J., A. Hameed and C. Niden (1994), ‘Volume and Autocovariances in Short-horizon Individual Security Returns’, Journal of Finance, 49(4): 1305-29. Diamond, D.W. and R.E. Verrecchia (1991), ‘Disclosure, Liquidity and the Cost of Capital’, Journal of Finance, 46(4): 1325-59. Duarte, J. and L. Young (2009), ‘Why is PIN Priced?’, Journal of Financial Economics, 91(2): 119-38. Dvořák, T. (2005), ‘Do Domestic Investors Have an Information Advantage? Evidence from Indonesia’, Journal of Finance, 60(2): 817-39. Easley, D., R.F. Engle M. O’Hara and L. Wu (2008), ‘Time-varying Arrival Rates of Informed and Uninformed Trades’, Journal of Financial Econometrics, 6(2): 171-207. Easley, D., M.L. de Prado and M. O’Hara (2016), ‘Discerning Information from Trade Data’, Journal of Financial Economics, 120(2): 269-85. Easley, D., S. Hvidkjaer and M. O’Hara (2002), ‘Is Information Risk a Determinant of Asset Returns?’, Journal of Finance, 57(5): 2185-221. Easley, D. and M. O’Hara (2004), ‘Information and the Cost of Capital’, Journal of Finance, 59(4): 1553-83.
36
Easley, D., M. O’Hara and P.S. Srinivas (1998), ‘Option Volume and Stock Prices: Evidence on Where Informed Traders Trade’, Journal of Finance, 53(2): 431-65. Erb, C., C.R. Harvey and T. Viskanta (1994), ‘Forecasting International Equity Correlations’, Financial Analysts Journal, 50(6): 32-45. Fecht, F., A. Hackethal and Y. Karabulut (2018), ‘Is Proprietary Trading Detrimental to Retail Investors?’, Journal of Finance, 73(3): 1323-61. Ferreira, M.A., P. Matos, J.P. Pereira and P. Pires (2017), ‘Do Locals Know Better? A Comparison of the Performance of Local and Foreign Institutional Investors’, Journal of Banking and Finance, 82: 151-64. Froot, K.A., P.G.J. O’Connell and M. Seasholes (2001), ‘The Portfolio Flows of International Investors’, Journal of Financial Economics, 59(2): 151-93. Gârleanu, N. and L.H. Pedersen (2004), ‘Adverse Selection and the Required Return, Review of Financial Studies, 17(3): 643-65. Grinblatt, M. and M. Keloharju (2000), ‘The Investment Behavior and Performance of Various Investor Types: A Study of Finland’s Unique Dataset’, Journal of Financial Economics, 55(1): 43-67. Grossman, S.J. and J.E. Stiglitz (1980), ‘On the Impossibility of Informationally Efficient Markets’, American Economic Review, 70(3): 393-408. Hau, H. (2001), ‘Location Matters: An Examination of Trading Profits’, Journal of Finance, 56(5): 1951-83.
37
Hu, J. (2014), ‘Does Option Trading Convey Stock Price Information?’, Journal of Financial Economics, 111(3): 625-45. Huang, R.D. and C.Y. Shiu (2009), ‘Local Effects of Foreign Ownership in an Emerging Financial Market: Evidence from Qualified Foreign Institutional Investors in Taiwan’, Financial Management, 38(3): 567-602. Hughes, J.S., J. Liu and J. Liu (2007), ‘Information Asymmetry, Diversification and Cost of Capital’, Accounting Review, 82(3): 705-29. Hwang, C.Y. and X. Qian, (2010), ‘Information Risk and Momentum Anomalies’, Working Paper, Nanyang Technological University. Johnson, T.L. and E.C. So (2012), ‘The Option to Stock Volume Ratio and Future Returns’, Journal of Financial Economics, 106(2): 262-86. Kahle, K.M. and R.M. Stulz (2013), ‘Access to Capital, Investment and the Financial Crisis’, Journal of Financial Economics, 110(2): 280-99. Karolyi, G.A. and R.M. Stulz (1996), ‘Why Do Markets Move Together? An Investigation of US-Japan Stock Return Co-movements’, Journal of Finance, 51(3): 951-86. Kim, S.J., F. Moshirian and E. Wu (2005), ‘Dynamic Stock Market Integration Driven by the European Monetary Union: An Empirical Analysis’, Journal of Banking and Finance, 29(10): 2475-502. Lambert, R., C. Leuz and R.E. Verrecchia (2007), ‘Accounting Information, Disclosure and the Cost of Capital’, Journal of Accounting Research, 45(2): 385-420.
38
Lambert, R. and R.E. Verrecchia (2009), ‘The Cost of Capital in Imperfect Competition Settings’, Working Paper, The Wharton School of the University of Pennsylvania. Lee, C. and M.J. Ready (1991), ‘Inferring Trade Direction from Intraday Data’, Journal of Finance, 46(2): 733-46. Lehmann, B.N. (1990), ‘Fads, Martingales and Market Efficiency’, Quarterly Journal of Economics, 105(1): 1–28. Lo, A. and C. MacKinlay (1990), ‘When are Contrarian Profits Due to Stock Market Overreaction?’, Review of Financial Studies, 3(2): 175-205. Longin, F. and B. Solnik (1995), ‘Is Correlation in International Equity Returns Constant: 1960–1990’, Journal of International Money and Finance, 14(1): 3-26. O’Hara, M., C. Yao and M. Ye (2012), ‘What’s Not There: The Odd-lot Bias in TAQ Data’, Johnson School Research Paper Series, No.16-2012. Richards, A. (2005), ‘Big Fish in Small Ponds: The Trading Behavior and Price Impact of Foreign Investors in Asian Emerging Equity Markets’, Journal of Financial and Quantitative Analysis, 40(1): 1-27. Roll, R., E. Schwartz and A. Subrahmanyam (2010), ‘O/S: The Relative Trading Activity in Options and Stock’, Journal of Financial Economics, 96(1): 1-17. Seasholes, M.S. (2004), ‘Reexamining Information Asymmetries in Emerging Stock Markets’, unpublished working paper, University of California, Berkeley. Stoll, H.R. and R.E. Whaley (1990), ‘The Dynamics of Stock Index and Stock Index Futures
39
Returns’, Journal of Financial and Quantitative Analysis, 25(4): 441-68. Subrahmanyam, A. (2008), ‘Lagged Order Flows and Returns: A Longer-term Perspective’, Quarterly Review of Economics and Finance, 48(3): 623-40. Wang, J. (1993), ‘A Model of Intertemporal Asset Prices under Asymmetric Information’, Review of Economic Studies, 60(2): 249-82. Weng, P.S. and W.C. Tsai (2018), ‘Do Foreign Institutional Traders have Private Information for the Market Index? The Aspect of Market Microstructure’, International Review of Economics and Finance, 55: 308-23. Weng, P.S., M.H. Wu, M.L. Chen and W.C. Tsai (2017), ‘An Empirical Analysis of the Dynamic Probability of Institutional Informed Trading: Evidence from the Taiwan Futures Exchange’, Journal of Futures Markets, 37(9): 865-91.
40
Table 1 Summary statistics of trading volume in the index futures and stock markets Variables
Mean
S.D.
P1
Median
P99
Panel A: Index futures market 60-minute trading volume (2003-2011) FI
2,120
2,616
0
1,204
12,016
DI
359
467
0
211
2,122
PF
4,348
3,853
311
3,169
17,680
Panel B: Index futures market daily trading volume (2003-2018) FI
39,523
42,416
225
27,389
174,490
DI
2,068
4,064
27
972
21,306
PF
29,315
18,023
3,332
28,469
76,692
Panel C: Stock market daily trading volume (2003-2018) FI
941,693
368,451
241,048
895,949
2,244,374
DI
108,841
67,948
22,362
98,003
312,538
PF
134,464
56,999
42,055
125,598
326,566
Note: This table reports the summary statistics on trading volume across three types of institutional investors: foreign institutional (FI) investors, domestic institutional (DI) investors, and proprietary firms (PF). Panel A reports the statistics on trading volume by the three investor types over the 60-minute interval in the index futures market from 2003 to 2011; Panel B reports the statistics on daily trading volume by the three investor types in the index futures market from 2003 to 2018; and Panel C reports the statistics on daily trading volume by the three investor types in the stock market from 2003 to 2018. P1 (P99) denotes the 1st (99th) percentile.
41
Table 2 Summary statistics of weekly and monthly trading activity measures Variables
Mean
S.D.
P1
Median
P99
Panel A: Weekly OIB and VOIB in the index futures market Trading Volume (Q) OIBFI
0.0313
0.2418
-0.5948
0.0077
0.8044
0.3854
0.2104
0.0913
0.3297
0.8770
OIB
0.0471
0.2308
-0.5293
0.0351
0.6698
VOIBDI
0.5216
0.1900
0.1958
0.4683
0.9215
0.0044
0.0533
-0.1677
0.0017
0.1574
0.1762
0.0692
0.0677
0.1676
0.3408
0.0310
0.2420
-0.5946
0.0074
0.8049
0.3854
0.2104
0.0912
0.3289
0.8770
0.0479
0.2308
-0.5255
0.0366
0.6706
VOIB
0.5216
0.1900
0.1958
0.4682
0.9215
OIBPF
0.0045
0.0532
-0.1684
0.0018
0.1570
0.1762
0.0692
0.0677
0.1676
0.3408
VOIB
FI
DI
OIB
PF
VOIB
PF
Dollar Trading Volume (D) OIBFI VOIB
FI
DI
OIB
DI
VOIB
PF
Panel B: Monthly OIB and VOIB in the index futures market Trading Volume (Q) OIBFI
0.0211
0.1190
-0.2475
0.0012
0.5001
0.1875
0.1863
0.0257
0.0888
0.7104
0.0272
0.0865
-0.1897
0.0218
0.2411
VOIB
0.3986
0.1338
0.1685
0.3811
0.7689
OIBPF
0.0037
0.0209
-0.0673
0.0027
0.0729
0.0703
0.0477
0.0202
0.0502
0.2322
0.0202
0.1189
-0.2489
0.0011
0.5019
0.1866
0.1866
0.0257
0.0882
0.7105
0.0278
0.0862
-0.1904
0.0229
0.2412
0.3986
0.1338
0.1686
0.3811
0.7689
0.0038
0.0209
-0.0673
0.0027
0.0730
0.0703
0.0477
0.0202
0.0502
0.2322
-0.8564
0.0191
0.8545
VOIB
FI
DI
OIB
DI
VOIB
PF
Dollar Trading Volume (D) OIBFI VOIB
FI
DI
OIB
DI
VOIB OIB
PF
VOIBPF
Panel C: Monthly OIB and VOIB in the stock market Trading Volume (Q) OIBFI VOIB
FI
0.0196
0.3571
0.6368
0.1980
0.1939
0.6528
0.9908
OIBDI
-0.0574
0.5620
-1.0000
-0.0596
0.9891
VOIBDI
0.7490
0.2128
0.0000
0.7988
1.0210
42
OIBPF VOIB Note:
PF
-0.0316
0.3070
-0.9155
-0.0081
0.7969
0.7077
0.1721
0.3094
0.7206
1.0005
This table reports the summary statistics on OIBs and VOIBs across the three types of institutional investors. Panel A reports the descriptive statistics on the weekly OIBs and VOIBs in the index futures market for a sample period running from 1 January 2003 to 31 December 2011. OIB is defined as (B-S)/(B+S), where B (S) is the buyer (seller). Q (D) refers to trading volume (dollar trading volume) in the index futures market, where dollar trading volume is calculated as the trading volume x NTD 200 x per index point for each futures contract. VOIB is defined as the standard deviation in the 60-minute order imbalances in a given week. Panel B reports the descriptive statistics on monthly OIBs and VOIBs in the index futures market from January 2003 to December 2018. OIB denotes the monthly order imbalance in the index futures market, where the monthly VOIB is defined as the standard deviation in the daily order imbalance in a given month. Panel C reports the descriptive statistics on monthly OIBs and VOIBs in the stock market from January 2003 to December 2018. OIB denotes the monthly order imbalance in the individual stock market. Q refers to the trading volume (in units of 1,000 shares) in each stock, where VOIB is defined as the standard deviation in the daily order imbalance in a given month. FI, DI and PF respectively denote foreign institutional investors, domestic institutional investors and proprietary firms, and P1 and P99 refer to the 1st and 99th percentiles.
43
Table 3 Correlation matrix on order flow volatility OIBFI
VOIBFI
OIBDI
VOIBDI
OIBPF
VOIBPF
Panel A: Weekly OIBs and VOIBs in the index futures market Trading Volume (Q) OIBFI VOIB
FI
DI
OIB
DI
VOIB OIB
PF
VOIB
PF
1.0000 0.0486
1.0000
-0.0995
-0.0357
1.0000
-0.0142
-0.4187
-0.0990
1.0000
-0.3653
0.0815
-0.0236
-0.0715
1.0000
0.1060
0.7140
0.0535
-0.3555
0.0077
1.0000
Dollar Trading Volume (D) OIBFI
1.0000
VOIBFI
0.0486
1.0000
-0.1003
-0.0377
1.0000
-0.0145
-0.4187
-0.0968
1.0000
-0.3655
0.0811
-0.0232
-0.0726
1.0000
0.1063
0.7140
0.0519
-0.3554
0.0078
DI
OIB
DI
VOIB OIB
PF
VOIB
PF
1.0000
Panel B: Monthly OIBs and VOIBs in the index futures market Trading Volume (Q) OIBFI VOIB
FI
DI
1.0000 0.3514
1.0000
OIB
-0.0854
0.2398
1.0000
VOIBDI
-0.0980
-0.4066
-0.1971
1.0000
-0.6153
0.0050
0.0478
-0.0942
1.0000
0.2764
0.8832
0.3124
-0.3561
0.0454
OIB
PF
VOIB
PF
1.0000
Dollar Trading Volume (D) OIBFI VOIB
FI
DI
OIB
DI
VOIB OIB
PF
VOIB
PF
1.0000 0.3454
1.0000
-0.0848
0.2305
1.0000
-0.0950
-0.3994
-0.1961
1.0000
-0.6174
0.0061
0.0478
-0.0966
1.0000
0.2699
0.8772
0.3074
-0.3561
0.0479
Panel C: Monthly OIBs and VOIBs in the stock market Trading Volume (Q) OIBFI VOIB
FI
DI
OIB
1.0000 -0.0094
1.0000
0.0785
-0.0027
1.0000
44
1.0000
VOIBDI OIB
PF
VOIB Note:
PF
0.0010
0.0185
0.0999
1.0000
0.0140
-0.0221
0.1479
0.0245
1.0000
-0.0042
0.3588
-0.0372
0.0546
-0.0274
1.0000
This table reports the correlation coefficients on order flow volatility across the three types of institutional investors. Panel A reports the correlation coefficients on the weekly OIBs and VOIBs in the index futures market for a sample period running from 1 January 2003 to 31 December 2011.Panel B reports the correlation coefficients on monthly OIBs and VOIBs in the index futures market from January 2003 to December 2018. Panel C reports the correlation coefficients on monthly OIBs and VOIBs in the stock market from January 2003 to December 2018. The definitions of variables are the same as in Table 2.
45
Table 4
Relations between order imbalance volatility and the subsequent-week market returns in the index futures market FI
Variables
Intercept FI OIBt –1 FI VOIBt –1 DI OIBt –1 DI VOIBt –1
DI
Q
D
Q
D
Coeff.
t-stat.
Coeff.
t-stat.
Coeff.
t-stat.
Coeff.
t-stat.
-0.3380
-0.89
-0.3387
-0.90
-0.2762
-0.32
-0.2766
-0.32
-0.4977
-0.45
-0.4964
-0.44
1.1393
0.88
1.1403
0.88
1.4824**
2.45
1.4753**
2.45
1.8135***
2.63
1.8132***
2.63
Rett–1
-0.0368
-1.09
-0.0368
-1.10
-0.0241
-0.58
-0.0240
-0.57
Rett–2
0.0201
0.30
0.0202
0.30
0.0076
0.08
0.0077
0.08
Rett–3
0.0248
0.25
0.0248
0.25
0.0248
0.17
0.0247
0.17
Rett–4
0.0136
0.38
0.0136
0.38
0.0116
0.46
0.0115
0.46
∆FutVolt–1
1.0059
0.92
1.0071
0.93
0.7062
0.58
0.7060
0.58
∆FutVolt–2
-0.1083
-0.11
-0.1054
-0.10
-0.1557
-0.13
-0.1560
-0.13
∆FutVolt–3
-0.9635
-0.82
-0.9614
-0.82
-1.0119
-0.64
-1.0126
-0.63
∆FutVolt–4
0.9270
1.00
0.9259
1.00
0.8013
1.09
0.8005
1.08
Illiqt–1
-1.6341
-1.43
-1.6317
-1.43
-1.0572
-0.81
-1.0569
-0.81
Illiqt–2
2.2043
1.34
2.2042
1.34
2.7180
1.21
2.7171
1.21
Illiqt–3
-1.5909*
-1.89
-1.5905*
-1.89
-1.0167
-1.01
-1.0176
-1.01
Illiqt–4
-0.7607
-1.22
-0.7609
-1.22
-0.2390
-0.44
-0.2391
-0.44
SDRett–1
-0.6397***
-2.77
-0.6389***
-2.77
-0.6821***
-2.88
-0.6819***
-2.88
SDRett–2
0.1467
0.42
0.1466
0.42
0.0272
0.09
0.0275
0.09
SDRett–3
0.4884***
2.98
0.4885***
2.99
0.3721**
1.99
0.3722**
1.99
SDRett–4
0.1309
0.54
0.1307
0.54
0.0400
0.15
0.0399
0.15
2
R (%)
8.43
8.42
7.54
7.54
No. of Obs.
445
445
445
445
PF Intercept FI OIBt –1 FI VOIBt –1 DI OIBt –1 DI VOIBt –1 PF OIBt – 1 PF VOIBt – 1
-0.2987
-0.37
FI, DI and PF -0.2987
-0.37
-2.5232
-1.58
-2.5280
-1.60
1.8368**
2.45
1.8291**
2.46
2.1174***
3.22
2.1215***
3.23
-0.1831
-0.28
-0.1832
-0.28
2.1810*
1.76
2.1858*
1.78
1.7178
0.56
1.6990
0.55
5.1826***
5.67
5.1653***
5.72
3.5173
0.98
3.5171
0.98
3.4337
1.22
3.4273
1.22
Rett–1
-0.0432
-1.16
-0.0431
-1.15
-0.0674**
-1.99
-0.0672**
-1.98
Rett–2
0.0006
0.01
0.0006
0.01
0.0215
0.55
0.0216
0.56
46
Table 4 (Contd.) Rett–3
0.0196
0.15
0.0196
0.15
0.0337
0.34
0.0336
0.34
Rett–4
0.0076
0.20
0.0076
0.21
0.0188
0.41
0.0188
0.41
∆FutVolt–1
0.8350
0.43
0.8363
0.43
1.1629
1.15
1.1666
1.16
∆FutVolt–2
-0.1071
-0.08
-0.1068
-0.08
0.2728
0.30
0.2777
0.30
∆FutVolt–3
-1.0848
-0.57
-1.0849
-0.57
-0.6395
-0.54
-0.6366
-0.54
∆FutVolt–4
0.7074
0.72
0.7073
0.72
Illiqt–1
-1.3331
-0.79
-1.3326
-0.79
Illiqt–2
2.3885
1.29
2.3880
1.29
2.1082*
1.70
2.1063*
1.70
Illiqt–3
-1.2613
-0.92
-1.2616
-0.93
-1.5480*
-1.92
-1.5487*
-1.92
Illiqt–4
-0.5418
-0.65
-0.5415
-0.65
-0.8442
-1.27
-0.8437
-1.27
SDRett–1
-0.6989**
-2.56
-0.6993**
-2.55
-0.6075**
-2.54
-0.6071**
-2.56
SDRett–2
0.1280
0.38
0.1280
0.38
0.2651
0.86
0.2655
0.87
SDRett–3
0.4463**
2.49
0.4464**
2.50
0.5696***
2.92
0.5701***
2.92
SDRett–4
0.0999
0.31
0.1000
0.31
0.2225
0.60
0.2225
0.61
2
1.1274* -1.8793
1.96 -1.63
1.1266* -1.8755
R (%)
7.36
7.36
9.93
9.93
No. of Obs.
445
445
445
445
1.96 -1.63
Note: This table reports the regression results on the subsequent-week market return on order imbalance volatility for each type of institutional investor for a sample period running from January 1, 2003 to December 31, 2011. The dependent variable, Ret is the logarithm of index futures returns; OIB denotes the weekly order imbalance and VOIB is the weekly volatility of order imbalance as defined in equation (2). ∆FutVol denotes the weekly change in the total futures trading volume; Illiq denotes the Amihud’s (2002) illiquidity measure, which is the weekly average ratio of the absolute daily return divided by total trading volume and multiplied by 104 in coefficient adjustment; SDRet is the standard deviation in the daily index futures return in a given week; and VOIB is the measure of Chordia et al. (2019). The t-statistics are estimated using Newey-West correction. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.
47
Table 5 Relations between order imbalance volatility and the subsequent-month market returns in the index futures market FI Variables
Intercept FI OIBt –1 FI VOIBt –1 DI OIBt –1 DI VOIBt –1
DI
Q
D
Q
D
Coeff.
t-stat.
Coeff.
t-stat.
Coeff.
t-stat.
Coeff.
t-stat.
-0.4395
-0.48
-0.5580
-0.58
1.2068
0.79
1.2015
0.79
2.2326
0.88
2.1874
0.88
0.6039***
2.64
0.6799***
2.73 -6.2963
-1.54
-6.1393
-1.50
-0.1679
-0.63
-0.1661
-0.62
Rett–1
0.0990
0.90
0.0970
0.88
0.1444
1.28
0.1439
1.28
∆FutVolt–1
0.3447
0.22
0.3351
0.21
0.2376
0.16
0.2419
0.16
-2.98
-0.0977
-0.40
-0.1000
-0.41
0.82
0.1410
0.13
0.1418
0.13
Illiqt–1 SDRett–1
-0.7305*** 0.7693
2
-3.04
-0.7958***
0.76
0.8539
R (%)
3.70
4.20
2.70
2.66
No. of Obs.
192
192
192
192
PF Intercept FI OIBt –1 FI VOIBt –1 DI OIBt –1 DI VOIBt –1 PF OIBt – 1 PF VOIBt – 1
-0.3593
-0.36
-3.1157
-0.19
FI, DI and PF -0.3618
-3.2049
-0.37
-0.7693
-0.49
-0.7709
-0.51
1.0822
0.48
1.0570
0.47
0.7869***
3.03
0.9465***
3.03
-7.5892*
-1.96
-7.2487*
-1.93
0.0362
0.21
0.0474
0.28
-0.19
0.1583
0.01
0.0364
0.00
1.2554*
1.78
1.2590*
1.78
0.0103
-0.38
0.0023
-0.85
Rett–1
0.1103
0.98
0.1104
0.98
0.1265
1.26
0.1255
1.25
∆FutVolt–1
0.3469
0.22
0.3467
0.22
-0.0690
-0.04
-0.0488
-0.03
-0.3821
-1.40
-0.3827
-1.40
-0.7441***
-4.05
-0.7987***
-4.13
0.4417
0.43
0.4431
0.43
Illiqt–1 SDRett–1 2
1.0185
1.23
1.0624
R (%)
2.38
2.39
4.97
5.52
No. of Obs.
192
192
192
192
1.29
Note: This table reports the regression results on the subsequent-month market return on order imbalance volatility for each type of institutional investor for a sample period running from 2003 to 2018, where the dependent variable is index futures returns (Ret). OIB denotes the monthly order imbalance and VOIB refers to monthly volatility of order imbalance as defined in equation (3). We also control the following variables: Ret; ∆FutVol; Illiq; SDRet as defined in equation (6). The t-statistics are estimated using Newey-West correction. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.
48
Table 6 Fama-MacBeth stock return regressions on monthly order imbalance volatility FI Variables
Model (1) Raw Ret Coeff.
Intercept k OIBt–1 k VOIBt–1 ∆StkVolt–1 Turnt–1 SDTurnt–1 Sizet–1 SDRett–1 Illiqt–1 Rett–1 Rett–2, t–12 k SVOIBt–1 STurnt–1 SSDTurnt–1 SSDRett–1 SIlliqt–1 R2 (%) Avg. number of stocks
t-stat.
DI Model (2) O-C Ret Coeff. t-stat.
0.1617
0.32
0.2687
0.58
-0.1856
-1.49
-0.1873
-1.51
1.3071*** 0.0405
2.67 0.37
1.0662** 0.0090
2.31 0.09
Model (3) Raw Ret
PF Model (4) O-C Ret
Model (5) Raw Ret
Coeff.
t-stat.
Coeff.
t-stat.
Coeff.
1.7217**
2.36
1.7522**
2.51
-0.5063
0.7026*** -0.0769
4.31 -0.10
0.1555
0.6793*** -0.0418
t-stat. -0.82
Model (6) O-C Ret Coeff.
t-stat.
-0.3948
-0.67
4.31
0.4391***
2.73
0.4102***
2.79
-0.06
2.1197***
3.92
2.0208***
3.82
0.86
0.1393
0.74
0.0739
0.62
0.0176
0.15
-0.0194***
-2.79
-0.0189***
-2.74
-0.0262**
-2.36
-0.0285**
-2.59
-0.0187**
-2.28
-0.0176**
-2.29
-0.0096
-0.99
-0.0095
-0.99
0.0465**
2.32
0.0446**
2.31
0.0029
0.24
0.0004
0.04
0.0332
0.13
0.1158
0.50
-0.3997
-1.62
-0.4626
-1.59
0.0571
0.21
0.1858
0.71
-0.1191
-0.58
-0.2193
-1.01
-0.4538*
-1.76
-0.6056**
-2.10
-0.1986
-0.90
-0.3273
-1.44
0.8754
0.77
1.9208
1.37
2.0565
0.27
1.9319
0.31
-1.3392
-1.22
0.3932
0.27
-0.0252*
-1.80
-0.0264*
-1.89
0.0117
1.14
0.0082
0.84
2.70
0.0066*
1.81
0.0073*
1.91
-2.5038***
-4.46
-2.4351***
-4.35
0.0111
1.12
0.0078
0.84
0.0068**
2.13
0.0066**
1.99
-1.1580**
-2.37
-0.9210*
-1.95
0.0088**
2.51
0.0093***
-0.3964
-0.55
-0.5920
-0.84
0.0001
0.01
0.0009
0.12
0.0054
0.45
0.0066
0.53
0.0004
0.04
-0.0005
-0.05
-0.0552
-1.48
-0.0560
-1.53
0.0121
0.19
0.0162
0.26
0.0012
0.03
0.0026
0.07
-0.1186
-0.64
-0.0479
-0.24
0.3291
1.17
0.4143
1.32
-0.0706
-0.31
0.0404
0.17
-3.5564**
-2.09
-2.6568***
-2.81
-15.9061*
-1.88
-13.8071**
-2.00
-1.1608
-0.54
-1.1406
-0.66
13.72
13.62
24.93
24.90
15.65
15.69
751
751
179
179
442
442
Note: This table reports the results of the Fama-MacBeth regressions of stock returns on monthly order imbalance volatility for each type of institutional investor from July 2003 to December 2018. We follow Chordia et al. (2019) to require the monthly observations to have at least 14 daily trading records in a month for each investor type. Two types of stock returns are examined: Raw Ret is the monthly raw return and O-C Ret is the monthly open to close return. ∆StkVol denotes the monthly change in the individual stock dollar trading volume; Turn is the turnover ratio, defined as the trading volume divided by shares outstanding. SDTurn is the standard deviation in the turnover ratio (Turn) over the previous 36 months. Size is defined as market capitalization (in millions). SDRet is the standard deviation in the daily individual stock return in a given month. Illiq denotes the Amihud (2002) illiquidity measure, which is defined as the monthly average ratio of the absolute daily return divided by the dollar trading volume; Rett-1 is the one-month lagged individual stock return; and Rett-2, t-12 are the cumulative returns over the twelve month period ending at the start of the previous two months. SVOIB is the shock to order imbalance volatility, which is defined as the difference between the current month VOIB and the six-month moving average of VOIB in the previous month; the definitions of STurn, SSDTurn, SSDRet and SIlliq are similar to SVOIB. The t-statistics are estimated using Newey-West correction. Avg. number of stocks refers to the average number of stocks per month. *,**, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively. 49
Table 7
Alternative Fama-MacBeth regression tests of stock returns on monthly order imbalance volatility FI
Variables
Intercept k OIBt–1 k VOIBt–1 ∆StkVolt–1 Turnt–1 SDTurnt–1 Sizet–1 SDRett–1 Illiqt–1 Rett–1 Rett–2, t–12 k SVOIBt–1 STurnt–1 SSDTurnt–1 SSDRett–1 SIlliqt–1 R2 (%) Avg. number of stocks
Model (1) Ex-Crisis Coeff. t-stat. 0.3610 0.79 -0.1276 -1.19 1.2705** 2.58 0.051 0.47 -0.0182** -2.54 -0.0093 -0.93 0.1848 0.66 -0.2290 -1.23 0.8811 0.75 0.0126 1.23 0.0080*** 3.27 -1.1865** -2.38 0.0035 0.52 -0.0457 -1.20 -0.0391 -0.22 -3.5807** -2.04
DI Model (2) MA = 12 Coeff. t-stat. 0.0225 0.04 -0.0904 -0.68 1.2668** 2.31 -0.0431 -0.41 -0.0305*** -4.42 0.0078 0.91 0.1768 0.69 -0.1281 -0.52 0.4536 0.45 0.0081 0.84 0.0046 1.18 -1.1863** -2.19 0.0204*** 2.72 -0.0445** -2.21 -0.1435 -0.66 -4.1958 -1.52
Model (3) Ex-Crisis Coeff. t-stat. 1.8416*** 2.86 0.6552*** 3.99 -0.0460 -0.06 0.1340 0.72 -0.0269** -2.36 0.0478** 2.31 -0.3081 -1.28 -0.5723** -2.26 2.6802 0.35 -0.0208 -1.49 0.0096*** 2.89 -0.4254 -0.60 0.0066 0.57 0.0102 0.15 0.4334 1.57 -1.6097* -1.84
PF Model (4) MA = 12 Coeff. t-stat. 1.3470 1.30 0.6493*** 3.87 -0.3550 -0.29 0.2096 0.98 -0.0470*** -3.81 0.0816*** 3.81 -0.2179 -1.00 -0.2951 -1.05 8.1301 0.90 -0.0240 -1.65 0.0079** 2.28 -0.0296 -0.03 0.0243* 1.94 -0.0034 -0.08 0.0743 0.26 -27.2145*** -3.02
Model (5) Ex-Crisis Coeff. t-stat. -0.2709 -0.48 0.4390*** 2.62 2.1238*** 3.80 0.0325 0.28 -0.0154* -1.88 -0.0004 -0.04 0.2288 0.83 -0.3178 -1.61 -1.3911 -1.23 0.0135 1.29 0.0082*** 2.91 -2.4286*** -4.34 0.0033 0.35 0.0097 0.24 0.0345 0.16 -1.1911 -0.54
Model (6) MA = 12 Coeff. t-stat. -0.6455 -0.86 0.4180** 2.39 2.1927*** 3.48 0.0188 0.14 -0.0292*** -3.16 0.0221 1.54 0.0912 0.40 -0.2043 -0.80 -2.1788 -1.50 0.0066 0.68 0.0058 1.46 -2.6385*** -3.90 0.0180* 1.94 -0.0130 -0.48 0.0170 0.07 0.3804 0.19
13.61
14.08
24.98
26.28
15.60
15.83
759
768
179
179
442
449
Note: This table reports the results of two alternative Fama-MacBeth regression tests of stock returns on monthly order imbalance volatility for each type of institutional investor from July 2003 to December 2018; Firstly, Ex-Crisis excludes the period of the bankruptcy of Lehman Brothers, with the inclusion of shock control variables, which are defined as the difference between the current month and the six-month moving average in the previous month. Secondly, following Chordia et al. (2019), MA=12 indicates the re-calculation of the shock variables by the difference between the current month and the 12-month moving average in the previous month. We follow Chordia et al. (2019) to require the monthly observations to have at least 14 daily trading records in a month for each investor type. Raw Ret is the dependent variable, the independent variables OIB, VOIB, ∆StkVol, Turn, SDTurn, Size, SDRet, Illiq, Rett-1, Rett-2, t-12, SVOIB, STurn, SSDTurn, SSDRet and SIlliq are the same as in equation (7). The t-statistics are estimated using Newey-West correction. Avg. number of stocks refers to the average number of stocks per month. *,**, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.
50
Table 8 A VAR analysis of the VOIB between the futures market and the stock market Trading Volume (Q)
Dollar Trading Volume (D)
Variables Coeff.
Panel A: FI
t-stat.
Stk,FI VOIBt
Intercept
0.0389***
2.88
Fut,k VOIB t –1
0.1402**
2.30
Coeff.
t-stat.
Fut,FI VOIBt
-0.0003
Coeff.
t-stat.
Stk,FI VOIBt
Coeff.
t-stat.
Fut,FI VOIBt
-0.02
0.0323***
2.86
0.0013
0.07
0.5042***
6.05
0.1402***
2.66
0.4871***
5.85
0.0885
1.00
-0.0835
-1.48
0.1018
1.15
0.2607***
3.36
0.0532
1.07
0.2483***
3.15
Fut,k VOIB t –2
-0.0976
-1.51
Fut,k VOIB t –3
0.0585
1.03
Stk,k VOIB t–1
0.1393*
1.72
-0.0139
-0.13
0.0587
0.72
0.0259
0.20
Stk,k VOIB t–2
0.1396*
1.68
-0.2226*
-1.96
0.1523*
1.85
-0.2465*
-1.90
Stk,k VOIB t–3
0.1483*
1.85
0.2338**
2.13
0.1932**
2.42
0.2218*
1.76
Fut,k OIB t –1
0.0198
0.69
0.1192***
3.02
0.0110
0.43
0.1294***
3.20
0.1276***
3.11
-0.0619**
-2.32
0.1063**
2.52
Fut,k OIB t –2
-0.0752**
-2.51
Fut,k OIB t –3
0.0335
1.12
0.0256
0.62
0.0062
0.24
0.0245
0.59
Stk,k OIB t–1
0.0260
0.60
0.1852***
3.15
0.0059
0.14
0.1981***
2.91
Stk,k OIB t–2
0.0652
1.48
0.0748
1.24
0.0788*
1.80
0.1558**
2.26
Stk,k OIB t–3
-0.0174
-0.39
0.0549
0.91
0.0330
0.47
R2 (%)
-0.0224
-0.50
42.41
93.33
49.93
92.93
No. of Obs.
192
192
192
192
Panel B: DI
VOIBt
Stk,DI
Fut,DI
VOIBt
Stk,DI
VOIBt
Intercept
0.1261***
4.96
0.1017**
2.07
Fut,k VOIB t –1
0.0011
0.03
0.6148***
7.88
-0.0049
-0.16
Fut,k VOIB t –2
0.0272
0.58
0.1634*
1.79
0.0185
Fut,k VOIB t –3
-0.0359
-0.88
0.0626
0.79
2.55
-0.1639
-0.38
Stk,k VOIB t–1 Stk,k VOIB t–2
0.2025** -0.0307
0.0710***
3.98
Fut,DI
VOIBt 0.0488
1.06
0.6106***
7.82
0.53
0.1571*
1.74
-0.0161
-0.53
0.0547
0.70
-1.07
0.0835
1.05
0.0474
0.23
-0.1662
-1.06
0.1158
1.48
-0.2682
-1.33
0.3459***
4.36
0.5341**
2.61
Stk,k VOIB t–3
0.1959**
2.43
0.2522
1.62
Fut,k OIB t –1
0.0180
0.41
-0.2146**
-2.56
0.0065
0.20
-0.2053**
-2.47
Fut,k OIB t –2
-0.0622
-1.36
0.0794
0.90
-0.0125
-0.37
0.0888
1.01
Fut,k OIB t –3
-0.0192
-0.45
-0.0590
-0.72
-0.0069
-0.22
-0.0294
-0.36
Stk,k OIB t–1
-0.0023
-0.07
0.0750
1.19
0.0811*
1.93
-0.0412
-0.38
Stk,k OIB t–2
0.0163
0.48
0.0145
0.22
0.0034
0.07
0.0350
0.30
Stk,k OIB t–3
0.0253
0.76
-0.0419
-0.65
-0.0171
-0.40
-0.0908
-0.82
R2 (%) No. of Obs.
17.96
70.91
27.13
71.48
192
192
192
192
51
Table 8
(Contd.) Trading Volume (Q)
Dollar Trading Volume (D)
Variables Coeff.
Panel C: PF
t-stat.
Stk,PF VOIBt
Coeff.
t-stat.
Fut,PF VOIBt
Coeff.
t-stat.
Stk,PF VOIBt
Coeff.
t-stat.
Fut,PF VOIBt
Intercept
0.0939***
4.63
0.0166
1.61
Fut,k VOIB t –1
0.0382
0.26
0.7152***
9.68
-0.0140
-0.13
Fut,k VOIB t –2
0.0317
0.18
-2.61
0.2337*
1.76
Fut,k VOIB t –3
0.0532
0.40
6.30
0.0025
0.02
0.4351***
Stk,k VOIB t–1
0.2416***
2.96
-0.0373
-0.90
0.3417***
4.18
-0.0114
-0.21
Stk,k VOIB t–2
0.0928
1.05
0.0344
0.77
0.1729*
1.96
0.0477
0.80
Stk,k VOIB t–3
-0.0391
-0.95
0.0931
1.17
-0.0210
-0.39
2.87
0.0273
0.23
-0.1666**
-2.13
-0.0546
-0.47
-0.1565**
-1.99
-0.2317** 0.4316***
0.0240**
2.18
0.0090
1.21
0.7009***
9.35
-0.2265**
-2.53 6.23
0.1263
1.56
Fut,k OIB t –1
-0.1001
-0.65
Fut,k OIB t –2
0.0382
0.25
Fut,k OIB t –3
-0.1076
-0.71
-0.1099
-1.42
-0.2355**
-2.04
-0.1177
-1.51
Stk,k OIB t–1
0.0137
0.24
-0.0232
-0.79
-0.0571
-0.93
0.0072
0.17
Stk,k OIB t–2
0.0221
0.39
2.26
-0.0150
-0.24
0.0628
1.48
Stk,k OIB t–3
0.1190**
2.22
-1.31
0.0669
1.11
-0.0352
-0.86
R2 (%) No. of Obs.
0.2253***
0.0646** -0.0356
0.2174***
32.47
86.21
58.15
85.88
192
192
192
192
2.76
Note: This table presents the vector autoregression analysis of the order imbalance volatility in the futures market vs. the order imbalance volatility in the stock market for each type of institutional investor from January 2003 to December 2018. We choose a lag length of 3 based on the Akaike information criterion during the empirical estimation, and for the sake of brevity, we only report the VAR regressions results of of VOIBStk,k and VOIBFut,k . VOIBStk,k (VOIBFut,k ) is the order imbalance volatility in the whole cash (futures) market. Again, for the sake of brevity, the following control variables are not reported in this table: Ret, ∆FutVol, Illiq, and SDRet, which are defined as in equation (8). *,**, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.
52
Table 9
Weekly order imbalance volatility in the index options market FI Coeff.
Intercept FI OOIBt –1 FI VOOIBt –1 DI OOIBt –1 DI VOOIBt –1
DI
Q
Variables
D t-stat.
Coeff.
Q t-stat.
-1.6566
-1.44
-1.6167
-1.38
-0.2375
-0.36
-0.9203
-1.38
2.4286**
2.33
2.2362**
Coeff.
D t-stat.
Coeff.
t-stat.
-0.2359
-0.26
-0.1967
-0.21
-1.1761
-0.83
-1.0023
-0.86
1.4114
0.86
1.1128
0.62
2.20
Rett–1
-0.0557
-0.99
-0.0719
-1.27
-0.0481
-0.85
-0.0376
-0.65
Rett–2
0.0389
0.44
0.0235
0.25
0.0384
0.41
0.0445
0.48
Rett–3
0.0850
1.03
0.0758
0.90
0.0879
1.01
0.0887
1.04
Rett–4
-0.0119
-0.21
-0.0251
-0.43
-0.0140
-0.24
-0.0124
-0.22
∆OptVolt–1
0.3435
0.35
0.2690
0.26
0.4553
0.44
0.4767
0.45
∆OptVolt–2
-0.1401
-0.17
-0.3418
-0.41
-0.1436
-0.17
-0.0875
-0.10
∆OptVolt–3
-0.3684
-0.35
-0.4568
-0.44
-0.2877
-0.27
-0.2137
-0.20
∆OptVolt–4
0.9534
1.21
0.8526
1.10
0.8157
1.12
0.9060
1.22
Illiqt–1
-1.8947**
-2.08
-1.8288*
-1.85
-1.8163*
-1.72
-1.8842*
-1.78
Illiqt–2
2.6942**
2.20
2.6497**
2.26
2.7295**
2.26
2.7555**
2.24
Illiqt–3
-0.7390
-0.95
-0.7556
-1.01
-0.8335
-1.02
-0.8910
-1.05
Illiqt–4
-0.6532
-0.90
-0.7015
-0.96
-0.4775
-0.67
-0.4973
-0.66
PSPRt–1
-0.7710
-0.98
-0.6711
-0.85
-0.9386
-1.21
-0.9522
-1.21
1.74
1.3584
1.64
1.3203
1.58
PSPRt–2
1.4802*
1.78
1.4379*
PSPRt–3
-0.2569
-0.38
-0.3987
-0.63
-0.3152
-0.47
-0.2791
-0.43
PSPRt–4
-0.2314
-0.26
-0.1280
-0.14
-0.1864
-0.21
-0.1259
-0.14
R2 (%)
11.89
12.17
10.59
10.54
297
297
297
297
No. of Obs.
PF Intercept FI OOIBt –1 FI VOOIBt –1 DI OOIBt –1 DI VOOIBt –1 PF OOIBt – 1 PF VOOIBt – 1
-0.2855
-0.30
FI, DI and PF -0.7982
-0.73
-1.8078
-1.40
-2.0361
-1.51
-0.4007
-0.55
-0.9509
-1.42
2.4331**
2.09
2.3100**
2.09
-1.4769
-1.00
-1.1548
-0.96
-0.4576
-0.21
-1.0227
-0.49
3.6362*
1.76
1.7815
1.45
3.3704
1.55
1.4882
1.15
0.9188
0.42
2.2437
0.98
1.0796
0.41
2.1348
0.89
Rett–1
-0.0711
-1.18
-0.0777
-1.24
-0.0857
-1.39
-0.0953
-1.43
Rett–2
0.0562
0.61
0.0333
0.37
0.0355
0.39
0.0061
0.07
Rett–3
0.1004
1.14
0.0913
1.07
0.0922
1.06
0.0749
0.89
Rett–4
-0.0039
-0.07
-0.0040
-0.07
-0.0194
-0.34
-0.0311
-0.55
53
Table 9
(Contd.) PF
FI, DI and PF
Q
Variables
D
Q
D
Coeff.
t-stat.
Coeff.
t-stat.
Coeff.
t-stat.
Coeff.
t-stat.
∆OptVolt–1
0.2292
0.23
0.3616
0.36
0.3149
0.30
0.4364
0.40
∆OptVolt–2
-0.2148
-0.26
-0.2155
-0.26
-0.1487
-0.17
-0.2431
-0.27
∆OptVolt–3
-0.2169
-0.21
-0.2031
-0.20
-0.2416
-0.22
-0.2824
-0.26
∆OptVolt–4
0.9939
1.28
0.9068
1.19
0.9592
1.20
0.9139
1.18
-1.9777*
-1.89
-2.0179*
-1.88
-2.0371**
-2.00
-2.0557*
-1.87
2.43
2.8107**
2.40
Illiqt–1 Illiqt–2
2.9943**
2.52
2.8540**
2.6668**
2.32
Illiqt–3
-0.7662
-1.07
-0.7437
-1.02
-0.8168
-1.12
-0.8634
-1.14
Illiqt–4
-0.6895
-0.91
-0.6630
-0.88
-0.6968
-1.04
-0.7096
-0.99
PSPRt–1
-0.9076
-1.22
-0.9648
-1.26
-0.8210
-1.11
-0.7738
-1.00
PSPRt–2
1.4693*
1.77
1.4751*
1.76
1.4683*
1.74
1.4027*
1.65
PSPRt–3
-0.3683
-0.57
-0.3406
-0.52
-0.2782
-0.43
-0.3347
-0.54
PSPRt–4
-0.1217
-0.13
-0.0775
-0.09
-0.1301
-0.14
0.0357
0.04
R2 (%) No. of Obs.
11.12
11.07
13.06
13.29
297
297
297
297
Note: In this table, we examine the relation between the subsequent-week market returns and the order imbalance volatility of the different types of institutional investors in the index options market from January 2003 to December 2008. The dependent variable is the logarithm of index futures returns (Ret). We refer to Bae and Dixon (2018) to construct the OIB of the options. OOIB represents the OIB of the options in a given week, which is defined as (long call – short call) – (long put – short put) and then standardized by dividing by the total trading volume of the corresponding investors. VOOIB is the VOIB of the options, which is defined as the standard deviation in the order imbalance of a 60-minute option in a given week. Q refers to the trading volume and dollar trading volume (D) is defined as the trading volume x NTD 50 x per index point for each options contract. ∆OptVol denotes the weekly change in the total options trading volume. Illiq refers to the Amihud (2002) measure of illiquidity, which is defined the same as in equation (5). PSPR is the average daily percentage quoted spread (%) in a given week. The t-statistics are estimated using Newey-West correction. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.
54
Table 10
Monthly order imbalance volatility in the index options market FI
Variables Intercept FI OOIBt –1 FI VOOIBt –1 DI OOIBt –1 DI VOOIBt –1
DI
Q
D
Q
-7.2748
-1.63
-5.9276
-1.32
-1.6015
-0.37
-1.4257
-0.52
1.5429**
2.34
1.1063**
D
-1.2793
-0.40
-1.3553
-0.36
-3.8753
-0.52
1.7719
0.40
0.9224
0.82
0.2848
0.30
1.97
Rett–1
0.1083
1.08
0.1288
1.17
0.1964
1.57
0.2121
1.52
OptVolt–1
1.3080
0.63
1.0609
0.52
1.3135
0.60
1.6216
0.76
-6.0818*
-1.88
-5.7353*
-1.78
-2.8474
-0.84
-2.2648
-0.75
1.0276
0.69
1.2218
0.81
-0.0880
-0.05
0.4860
0.30
Illiqt–1 PSPRt–1 2
R (%) No. of Obs.
16.16
12.45
6.84
6.07
72
72
72
72
PF Intercept FI OOIBt –1 FI VOOIBt –1 DI OOIBt –1 DI VOOIBt –1 PF OOIBt – 1 PF VOOIBt – 1
-2.5092
-0.85
FI, DI and PF -2.4734
-0.80
-8.0483**
-2.02
-6.2097
-1.54
-1.9154
-0.41
-0.4290
-0.17
1.5771**
1.0516**
1.99
-1.1870
-0.14
4.1089
0.87
-0.6472
-0.50
-0.7171
-0.96
-0.46
-25.5404*
-1.79
-1.9119
-0.28
-26.0871*
-1.74
1.6818*
1.93
1.1790*
1.87
-1.7947
0.73
-0.2755
1.10
Rett–1
0.2017*
1.70
0.1719
1.44
0.1323
1.43
0.1010
1.06
OptVolt–1
1.1865
0.60
1.6368
0.84
0.8785
0.44
1.3928
0.93
-5.3200
-1.60
-4.2221
-1.48
-7.3941**
-2.08
0.4375
0.29
0.4245
0.28
1.4923
0.78
Illiqt–1 PSPRt–1 R2 (%) No. of Obs. Note:
-2.9747
2.07
-6.3124*** -2.74 1.5249
10.30
8.27
19.29
13.75
72
72
72
72
0.92
This table examines the relationship between the subsequent-month market returns and order imbalance volatility of the different types of institutional investors in the index options market from January 2003 to December 2008. The dependent variable, market return (Ret), is the logarithm of index futures returns. OOIB represents the OIB of the options in a given month. VOOIB refers to the VOIB of the options, which is defined as the standard deviation in the order imbalance of daily option in a given month. We also control the following variables with one-month lags: Ret; ∆OptVol; Illiq; PSPR. The t-statistics are estimated using Newey-West correction. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.
55
VOIBSTK,k
VOIBFUT,k
Response of VOIBStk,FI to VOIBFut,FI
Response of VOIBFut,FI to VOIBStk,FI
Response of VOIBStk,DI to VOIBFut,DI
Response of VOIBFut,DI to VOIBStk,DI
Response of VOIBStk,PF to VOIBFut,PF
Response of VOIBFut,PF to VOIBStk,PF
Figure 1 Note:
VAR impulse response functions for trading volume (Q)
This figure reports the impulse response to one standard deviation Cholesky innovations ±2 S.E. For brevity, we show only the results of the responses of VOIBStk,k to VOIBFut,k and the responses of VOIBFut,k to VOIBStk,k, with VOIBStk,k and VOIBFut,k respectively denoting the VOIB in the whole stock and futures markets, which are defined as in equation (8). (Q) refers to trading volume in the whole stock (futures) market. The superscripts FI, DI and PF respectively represent foreign institutional investors, domestic institutional investors and proprietary firms.
56
VOIBStk,k
VOIBFut,k
Response of VOIBStk,FI to VOIBFut,FI
Response of VOIBFut,FI to VOIBStk,FI
Response of VOIBStk,DI to VOIBFut,DI
Response of VOIBFut,DI to VOIBStk,DI
Response of VOIBStk,PF to VOIBFut,PF
Response of VOIBFut,PF to VOIBStk,PF
Figure 2 Note:
VAR impulse response functions for trading volume (D)
This figure reports the impulse response to one standard deviation Cholesky innovations ±2 S.E. For the sake of brevity, we show only the results of the responses of VOIBStk,k to VOIBFut,k and the responses of VOIBFut,k to VOIBStk,k, with VOIBStk,k and VOIBFut,k respectively denoting the VOIB in the whole stock and futures markets, which are defined the same as in Table 8. (D) refers to dollar trading volume in the whole stock (futures) market and defined as the trading volume multiplied by the traded price for each share traded (or futures contract). The superscripts FI, DI and PF respectively represent foreign institutional investors, domestic institutional investors and proprietary firms.
57
Highlights
We study the relationship between the VOIB and expected returns across various types of institutional traders.
The VOIB measure of foreign institutional traders is positively related to future returns in the index futures market.
The positive relation still holds on the Taiwan Stock Exchange.
The VOIB of foreign institutional investors in the index futures market leads the VOIB in the whole stock market.
