Interaction of Investor Trades and Market Volatility: Evidence from the Tokyo Stock Exchange

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1 Interaction of Investor Trades and Market Volatility: Evidence from the Tokyo Stock Exchange Kee-Hong Bae, Keiichi Ito, and Takeshi Yamada* Preliminary, comments welcome This version: 1 October 2002 * Bae is from Korea University, Yamada is from National University of Singapore, and Ito is from Nomura Securities Co. Ltd. We thank Kalok Chan, Lewis Chan, Wai Mun Fong, Chuan Yang Hwang, Junji Kawahara, Yasuhiko Tanigawa, participants at the NFA Annual Meeting, and seminar participants at the National University of Singapore for helpful comments. We appreciate the assistance of Masato Hirota and Hirotaka Kawai of the Tokyo Stock Exchange in answering our questions about institutional details. We thank Virginia Unkefer for excellent editorial assistance. The arguments expressed in the paper do not reflect the opinions of the institutions with which authors are affiliated.

2 Interaction of Investor Trades and Market Volatility: Evidence from the Tokyo Stock Exchange Abstract We examine how the interactions of trades among different investors affect market volatility. We find that market volatility increases when certain groups of investors interact more intensively in the market. Our result shows that market volatility increases more than 35% compared to the average level of volatility when there is greater market participation by investors whose trading patterns are less likely to provide liquidity to each other. The result is robust even after adjusting for the volatility-volume relation, which implies that investor interactions contain information that explain volatility in addition to explanation provided by total trading volume. We also find that individual investors buy stocks persistently as market becomes volatile, which in return might increase the persistence of market volatility over time. 2

3 Asset pricing theories such as the capital asset pricing model (CAPM) or the arbitrage pricing theory (APT) assume that if there is a mispricing of an asset, all or at least some investors trade instantaneously and in potentially unlimited amounts. In other words, these theories imply that trading schedules are flat in relation to returns and that asset returns adjust immediately to equilibrium. However, recent empirical studies that investigate the trading activities of various types of investors find that many investors appear to be either contrarian or momentum traders (Grinblatt and Keloharju, 2000, 2001; Brennan and Cao, 1997; Choe, Kho, and Stulz, 1999; Froot, O Connell, and Seasholes, 2001; Nofsinger and Sias, 1999; Cai, Kaul, and Zheng, 2000). Another group of studies that investigates the price impact of stocks that are added to stock indices suggests that demand curves for these stocks are downward sloping (Shleifer, 1986; Wurgler and Zhuravskaya, 1999). Together, these empirical findings imply that the trading schedules of most investors are not flat in relation to returns. Shleifer (2000) suggests that when investors trading schedules are not flat, the trading of participating investors affects asset prices. In other words, marginal investors rather than average, or representative, investors affect asset prices. If there are many different types of investors such as individual and institutional investors with differing trading schedules, the interactions of these investors (i.e., who trades with whom) will affect asset returns or volatility. This paper investigates the trading behavior of various investors, the interactions of these investors, and the impact of these interactions on market volatility in the Tokyo Stock Exchange (TSE). Since we have trading data categorized by individual investors, institutional investors, foreign investors, and proprietary traders of securities firms, we are able to investigate the interactions between these investors. 1

4 First, we investigate the buy and sell trading patterns of different investor types. We find that each investor type has different trading patterns for buys and sells. Some investors may follow momentum patterns for buy trades, but may not do so for sell trades. Based on this result, we ask if trades of different investors on either side of the market (i.e., the buy side or the sell side) have different impacts on volatility. Existing theory suggests that momentum or positive feedback traders destabilize the market (De Long, Shleifer, Summers, and Waldman, 1990). This result holds if momentum investors trade against noise traders whose trading patterns do not provide liquidity to the momentum traders. However, if momentum investors trade against contrarian investors, market volatility would be smoothed since contrarian investors would be providing liquidity to momentum investors. We examine the buy market participation ratio (the buy trading volume of each investor type divided by the total buy trading volume) and sell market participation ratio (the sell trading volume of each investor type divided by the total sell trading volume) of various investor types, and we examine their relation to market volatility. We find that market volatility is significantly higher when there is greater market participation by investors whose trading patterns are less likely to provide liquidity to other investors. In particular, we find that volatility is high when foreign investors sell more since their trades are not sensitive to returns (i.e., their trading schedule is vertical in relation to returns) and are less likely to provide liquidity to the market. On the other hand, domestic investors sell trades tend to smooth volatility since they sell more as market returns increase (i.e., the investors follow contrarian patterns) and provide liquidity to the buy trades. 2

5 We also find that volatility tends to increase when there are more nonprofessional investors (i.e., individual investors and nonfinancial corporations) than professional investors in the market, probably because proprietary traders of securities companies, who are the major providers of liquidity in the TSE, do not provide liquidity to nonprofessional investors in the same way that they do to professional investors. Finally, we investigate how volatility affects the market participation of different investor types. We find that individual investors keep buying while some institutional investors trade less when market becomes volatile. 1 Our finding suggests that such trading pattern of individual investors increases the persistence of volatility. The remainder of this paper is organized as follows. Section 1 presents the background for our research and discusses relevant previous studies. Section 2 describes the data and provides descriptive statistics of trades by different investor types. Section 3 shows that buy and sell trades of different investors respond to market returns in an asymmetric manner. Following this result, Section 4 explores how the buy and sell trades of investors affect market volatility. Section 5 investigates how volatility affects the market participation of different types of investors and discusses how patterns of the market participations relate to volatility persistence. Section 6 concludes the paper. 1 Although we use aggregate data, our finding for individual investors is consistent with the trading patterns of individual investors reported by Barber and Odean (2002) who used individual trading account data. 3

6 I. Related Work Empirical studies that address the interactions of investors and their impact on market returns are scant. A notable exception is the study by Goetzmann and Massa (1999), which uses data from individual accounts in an equity index mutual fund to address the issue. Goetzmann and Massa identified contrarian and momentum investors and found that their interactions explain stock returns relatively well compared with other variables. A few recent studies have examined the interactions of investor trades, although these studies do not address the impact of the interactions on market returns. Cohen (1999) shows that individuals buy stock from institutions subsequent to market increases and sell to institutions subsequent to decreases. Cohen, Gompers, and Vuolteenaho (2001) find that institutions take advantage of the underreaction of stock prices by buying shares from individuals in response to good cash-flow news. Other studies have investigated the impact of trades of particular investor type(s) on returns or return volatility. Several studies have examined the impact of foreign investors trading, as most foreigners follow a momentum strategy that has the potential to destabilize the market. Some of these studies using emerging markets data have found that the trading of foreign investors affects returns either in the short or long term (Froot, O Connell, and Seasholes, 2001; Bekaert, Harvey, and Lumsdaine, 1999). Others, such as Choe, Kho, and Stulz (1999), studied the Korean equity market around the Asian currency crisis and found that foreign investors trading does not necessarily destabilize the market. In developed financial markets, Froot, O Connell, and Seasholes (2001), Karolyi (2001), Hamao and Mei (2001) have concluded that foreign investors trading does not generally destabilize the market. In addition, Chan and 4

7 Lakonishok (1993) and Warther (1995) reported that the trades of institutional investors affect contemporaneous security prices. Our paper also contributes to the literature by looking at the volatility-volume relation (see Karpoff (1987) for a survey). Few empirical studies have examined whether the trading volumes of different investors have different impacts on volatility. Daigler and Wiley (1999) addressed this issue using trading data from the futures market. They found that individual investors trading increases volatility the most. Our paper further not only shows that the investor type matters, but also that the interactions of buy and sell volumes among different investors adds information to the volatility-volume relation. Our paper is also related to the growing number of studies that have examined the trading behavior of various types of investors. Grinblatt and Keloharju (2000, 2001) are among the few researchers who described the trading behavior of all types of investors in a stock market. They found that foreigners tend to be momentum traders and that domestic investors, particularly individual investors, are contrarians in the Finnish market. Brennan and Cao (1997), Choe, Kho, and Stulz (1999), and Froot, O Connell, and Seasholes (2001) also found that foreign investors are momentum traders. Nofsinger and Sias (1999) and Cai, Kaul, and Zheng (2000) found that U.S. institutional investors follow momentum trading. Karolyi (2001) studied net buy trades of all investor types in the Japanese stock market and showed that Japanese institutions appear to be contrarian investors in contrast to U.S. institutions. Grinblatt and Keloharju (2001) found that individual investors are contrarian traders. 5

8 II. Data and Patterns of Trading A. Data We use weekly trading data on the First Section of the Tokyo Stock Exchange (TSE), which is categorized by investor types. The TSE publishes this data in their Monthly Securities Statistics. The data comprise the volume (i.e., number of shares traded) for both buy and sell trades for each investor type. The TSE reports trades that are aggregated across individual stocks for each investor type. The data cover all trades brokered by member securities companies of the TSE with a capitalization of at least 3 billion. The TSE also reports proprietary trades of these securities companies. The data account for approximately 90% of all trades in the First Section of the TSE. The TSE categorizes the brokered trades of member securities companies by classifying them into those by individuals, foreigners, and institutions. Institutions are further classified into nonfinancial corporations, mutual funds, insurance companies, and banks. Among the domestic institutions, the equity trading of financial institutions such as mutual funds, insurance companies, and banks arises from professional fund managers. Most trades by banks come from trust banks that manage equity funds such as corporate pensions. 2 On the other hand, the equity trading of nonfinancial corporations (and also a relatively small portion of trades 2 The bank category includes trading by commercial banks (city, long-term credit, and regional banks). The nature of equity trading by commercial banks differs from that of trust banks that are professional fund managers. The trading of commercial banks is around or less than 10% of the trades in the category. Data on the breakdown of bank trades into those of commercial and trust banks are available only for after September

9 by commercial banks) arises not only through corporate asset management but also from adjustment of cross-shareholdings in Japanese corporate groups (keiretsu). The foreign investors category includes both institutional and individual investors, but most trades are believed to be from institutions. As there are no designated dealers or market makers in the TSE, liquidity must be supplied by orders from investors. However, we cannot rule out the possibility that the proprietary trading divisions of the member securities firms act as dealers, despite the internal rules of the TSE intended to deter proprietary traders from engaging in such activities (Hamao and Hasbrouk, 1995). It is understood among member firms that one of the functions of proprietary trading is to facilitate the execution of customer orders. Therefore, proprietary trading includes both liquidity-providing activity and the autonomous trading of the member securities firms. Our sample period begins in the first week of January 1991 and ends in the last week of April We use 1991 as the beginning of our observation period because the period immediately before 1990 was the so-called stock market bubble period in Japan. Unless otherwise noted, we obtained all of the data used in this study from the Nomura Research Institute. B. Descriptive statistics of trades by different investor types In Table 1, Panel A shows the weekly buy and sell trading volume of different investor types and the proportion of that volume in the total buy and sell volume between January 1991 and April Major traders include proprietary traders of securities companies, individuals, foreigners, and banks. These investors make up between 75% and 80% of all trades. Other 7

10 investor types such as mutual funds, insurance companies, and nonfinancial corporations, account for a relatively small percentage of trades. Table 1, Panel B shows the average turnover ratios for each investor type. 3 Because they have relatively longer investment horizons compared with other institutional investors, insurance companies have one of the lowest turnover ratios of 0.09 per year. Nonfinancial corporations also have a low turnover ratio (0.09), because a large part of their shares are likely cross-held among group companies and are not traded frequently. Banks have a relatively low turnover ratio (0.25) because the very large shareholdings of commercial banks are part of the crossholdings of the keiretsu firms and are not traded frequently. However, trust banks, which are also included in the Banks category, have higher turnover since they are professional fund managers. For example, after 1997, when a breakdown of the data is available, the average turnover ratio of trust banks is 0.60 per year compared with 0.14 per year for commercial banks. Foreigners and mutual funds have higher turnover than the market average (1.13 for foreigners, 0.84 for mutual funds, and 0.45 for the market average). The turnover ratio of individual investors (0.37) is lower than the market average and that of proprietary traders (12.44) is the highest among all investor types, which is not surprising given the liquidity-providing role of proprietary traders. Panel C of Table 1 shows the contemporaneous correlations between market returns and the net buys of different investors. By observing their net buy trades, all domestic investors (except proprietary traders) appear to be contrarian traders. Japanese institutional investors are 8

11 contrarian traders in contrast to U.S. institutions, which are momentum traders (Nofsinger and Sias, 1999; Cai, Kaul, and Zheng, 2000). Similar to findings from other markets (Grinblatt and Keloharju, 2000, 2001; Brennan and Cao, 1997; Choe, Kho, and Stulz, 1999; Froot, O Connell, and Seasholes, 2001; Karolyi, 2001), our result indicates that foreign investors are momentum traders. The net buys of proprietary traders have a positive correlation with market returns. However, we cannot determine from this fact that proprietary traders are momentum traders, because their net buy trades can mirror the trades of the contrarian traders to whom they provide liquidity. In Panel D, we present correlation coefficients between buy and sell market participation ratios of different investor types. 4 We find several interesting correlations among different investor types. (1) We find that the market participations of professional investors are either negatively correlated or nonsignificantly correlated with those of nonprofessional investors for most cases, which implies that professional investors tend to trade less with nonprofessional investors. (2) Our results also indicate that buy trades of foreign investors are positively and significantly correlated with sell trades of domestic institutional investors, particularly with banks and mutual funds. This result implies that foreign investors tend to buy from domestic banks and mutual funds. However, we do not find any strong correlation between sell trades of foreign investors and domestic institutions. (3) We find evidence that proprietary traders provide more liquidity to banks, as correlation coefficient between buy (sell) market participation ratio of 3 To calculate turnover ratios, we use the ownership data of all listed shares in Japan, as these data are unavailable for the TSE. The trading volume that we use in the numerator of the turnover ratio is that of the three major stock exchanges (i.e., Tokyo, Osaka and Nagoya). 4 We detrend market participation ratios using 26 week backward moving averages. 9

12 banks and sell (buy) ratio of proprietary traders is more positive and significant compared to those correlations between other investors and proprietary traders. (4) Finally, we find that buy and sell trades of individual investors tend to cluster much more compared to other investors (compare correlation coefficients in the diagonal cells in Panel D). In the following sections, we aggregate mutual funds, insurance companies, and banks into a single category (i.e., financial institutions) for the following reasons: First, these investors are primarily involved in professional fund management. Second, the net buys of the three types of financial institutions all trade in a contrarian manner in relation to current market returns. (Thus, although not reported here, their net buys are all positively correlated.) We consequently have five investor types: individuals, nonfinancial institutions, financial institutions, foreign investors, and proprietary traders. III. Patterns of Buy and Sell Trades A. Asymmetric Behavior of Buy and Sell Trades of Various Investor Types In this section, we investigate whether the relation between net buy trades and market returns that we observed in Table 1, Panel C, arises from buy trades or from sell trades of each investor type. We regress the buy volume and sell volume of each investor type on current market returns (R t ) and a lagged dependent variable. As trading volumes might have trends in series, we detrend the data by differencing. Although there are many ways of detrending the data, we use the differencing method because we wish to maintain the same metric for all of the trade variables and to estimate all equations simultaneously in a system that we explain later in this section. Accordingly, all explanatory variables in the regression are differenced to maintain the original 10

13 relation among the variables. The regression also includes a constant and monthly dummy variables (D Month ) from February to December that take the value of 1 for the corresponding month and 0 otherwise. For each investor type, i, (other than proprietary traders), we estimate the following pair of equations ( is the difference operator): Buy Sell volume volume i, t i, t = const = const B i S i 12 + a j= a j= 2 B i, j S i, j D D Month j Month j + b R + c Buy volume B i S i t t B i + b R + c Sell volume S i i, t 1 i, t 1 + u + u B i, t S i, t (1) where u B and u S are error terms. We are interested in the coefficient on the market return, b B and b S. In Table 2, Panel A shows that the buy trades of domestic investors are either momentum traders (individual investors) or generally not sensitive to current returns (nonfinancial corporations and financial institutions). On the other hand, the sell volume shows a significant positive correlation to current returns for all domestic investors. As they sell more as returns increase, their trading can be interpreted as profit-taking behavior. Therefore, the negative correlation between the net buy trades and market returns of domestic investors appears to be caused by the trading pattern of their sell trades. (We do not report the intercepts and the coefficients on the dummy variables.) In contrast to domestic investors, the buy volume of foreign investors shows significant momentum behavior, but the sell volume is insensitive to current returns. Therefore, the net buy trades of foreigners indicate momentum patterns because their buy trades are positively correlated with returns. Grinblatt and Keloharju (2001) also find asymmetric patterns of buy and sell trades for different investors. They find similar results that sell trades are more sensitive 11

14 than buy trades to high past returns for domestic investors, while the opposite is true for foreign investors. 5 B. Supply of Liquidity by Proprietary Traders to Different Investor Types In Table 2, Panel B, we examine the buy and sell volume of proprietary traders in relation to the trades of other investors. Although proprietary traders are the major suppliers of liquidity, they also have the discretion to determine the liquidity supply to different investors or different stocks. We regress the buy (sell) volume of proprietary traders against the current sell (buy) volume of other investor types. The regression coefficient on the trade variable of investor type i (see coefficients c B pp,i and c S pp,i below) is expected to capture how proprietary traders respond to the current trades of each investor type. For this purpose, we detrend the volume data by differencing, as mentioned above. In this way, the metric of all the variables is maintained. We include market returns in the regression, since proprietary traders also trade on their own account in addition to providing liquidity to other investors. We have a pair of equations for proprietary traders: Buy Sell volume volume pp, t pp, t = const = const B pp S pp + b + b B pp S pp R + c Sell t t i i B pp, i R + c Buy S pp, i volume volume i, t i, t + u + u B pp, t S pp, t (2) where u B and u S are error terms. 5 For all investor types, a significant coefficient on the lagged trade variable suggests that trades have lagged response to past trading. 12

15 The results in Panel B of Table 2 show that proprietary traders are less likely to provide liquidity to individual investors. The coefficients on individual investors buy and sell volumes are close to zero, which indicate that proprietary traders are less likely to trade directly with individual investors. We find a similar result for proprietary traders buy volumes in response to the sell volume of nonfinancial corporations since the coefficient on the sell volume is nonsignificantly different from zero. On the other hand, we find that the estimated coefficients on the buy and sell volumes of financial institutions are much larger. These results are not surprising, as it is known that proprietary traders actively supply liquidity services to most of their domestic institutional clients. For example, proprietary traders trade portfolios of stocks to facilitate the execution of orders for their institutional clients (this is called the package or basket type transaction). They accumulate portfolios of shares in their position before selling to their clients, or they buy the portfolios of shares from their clients and liquidate them for their clients to avoid price impacts. We find that the supply of liquidity by proprietary traders to foreign investors is less accommodating than to domestic financial institutions as the size of the coefficients on the trades of foreign investors is smaller than that of financial institutions. C. Estimation We estimate the equations in both Panels A and B of Table 2 simultaneously because the trades of various investors as well as market returns are interrelated (i.e., they are possibly endogenous variables). Since the error term in each equation is likely to be correlated with these variables when they are used as explanatory variables, such correlation would introduce bias in the parameter estimates (i.e., a simultaneous equation bias) if we use the OLS method. In 13

16 addition, the error term in each equation is most likely to be correlated across equations because trades of various investors tend to be correlated with each other. To cope with these issues as well as to correct the standard errors for heteroskedasticity and autocorrelation, we estimate all of the equations (10 equations) simultaneously in Panels A and B in Table 2 using the Generalized Method of Moments (GMM). 6 We use Hansen s J-statistic to test jointly if the model and the instruments are valid. 7 The statistic, which has a χ 2 distribution, takes the value of 135.8, and has a p-value of IV. Impact of Investor Interactions on Volatility A. Interactions of destabilizing investors In the previous section, we documented there are no contrarian traders on the buy side, since the buy trades of foreign investors and individual investors follow momentum patterns and those of other domestic investors are not sensitive to returns. We aggregate all noncontrarian buy trades as these trades could potentially destabilize the market. On the sell side, the trades of domestic investors follow a contrarian pattern (i.e., domestic investors sell more as the returns increase) and foreign investors are not sensitive to current returns. Thus, we conjecture that the sell trades of domestic investors act as a stabilizing factor, while the sell trades of foreign investors could be destabilizing as they are less likely to provide liquidity for the buy trades. 6 We have also conducted equation-by-equation estimation using OLS as well as the Seemingly Unrelated Regression (SUR) that takes into account of cross-equation correlations of error terms but does not account for endogeneity. Although different estimation methods give different estimated values for the coefficients, the qualitative results are similar among the different methods. 7 We employ as instruments all lagged right-hand side variables, differenced returns lagged one to four weeks, monthly dummies, and a constant. 14

17 To test our conjecture, we divide the entire sample according to the market participation of those investors that are likely to destabilize the market. We calculate the market participation ratios (MRPs) for buy and sell trades as below: MPR(b) = (buy volume b / total buy volume) for investor type b MPR(s) = (sell volume s / total sell volume) for investor type s. We split the sample into four subsamples according to the high and low participation ratios of these investors. We test if the volatility is higher than that of the overall sample when the destabilizing investors have greater participation in trading on both the buy side and the sell side. The weekly volatility is measured using the daily return volatility (r 2 i ) accumulated over the number of business days during each week ( Σr 2 i ). Table 3 shows the volatility for subsamples that are sorted by different market participation ratios. In Panel A of Table 3, the entire sample is split into four subsamples according to the median buy and sell participation ratios of investor types whose trading behavior could destabilize the market. On the buy side, MRP(b) is the market participation ratio of all noncontrarian investors. Thus, 1-MRP(b) represents the MPR of proprietary traders on the buy side that could stabilize the market. On the sell side, we single out foreigners as the potentially destabilizing group of investors, since the rest of the investors, including proprietary traders and domestic investors, are possibly stabilizing types. Each cell in the Table 3 contains the mean and median volatility and the number of observations for each subsample. The numbers in parentheses below the mean and the median 15

18 figures are p-values that indicate the differences from the overall sample. We use a t-test for comparisons of the means and a signed rank test for comparisons of the medians. Consistent with our expectations, Panel A shows that volatility is significantly higher than the market mean and median when there is greater market participation from potentially destabilizing traders on both the buy side and the sell side. The mean volatility, for example, increases about 25% compared with the rest of the sample. In the remaining cells, we see that the volatility is either lower than, or at the same level as, that of the overall sample. This result shows that volatility significantly increases when groups of investors that are likely to destabilize the market interact more intensively. 8 In Panel B, we create subsamples that are split at the median buy participation ratio of nonprofessional investors and at the median sell participation ratio of foreigners. Nonprofessional investors include individual investors and nonfinancial corporations. The result is more emphatic than that in Panel A, which suggests that the buy trades of nonprofessional investors contribute more to volatility. The mean volatility increases more than 35% for the high participation subsample compared with the rest of the sample. 8 In a related study, Chan and Lakonishok (1993) showed that the price impacts of the buy and sell trades of institutional investors are different. Their results might reflect the fact that institutional investors buy and sell trades face different sell and buy schedules than those of other investors. 16

19 One might argue that the trading of nonprofessional investors increases volatility because their trading is excessive. However, the turnover ratios of these investors are not necessarily high (see Table 1). A possible explanation for the high volatility is that proprietary traders do not provide liquidity to nonprofessional investors, as we have shown in the previous section. B. Volatility-volume relation In Table 4, we examine whether investor interaction provides any additional information about the volatility-volume relation. As it is known that volatility is positively associated with trading volume, we employ a volatility model that uses the volume of the entire market and the lagged volatility as independent variables. The model is widely used to describe the volatilityvolume relation (see Karpoff (1987) for a survey). We add various market participation ratios (MPRs) as the independent variables of this model and examine its statistical significance as well as its additional explanatory power. 9 Model 1 in Table 4 is a benchmark model of the volatilityvolume relation, which includes total volume and lagged volatility as the only explanatory variables. Models 2, 3, and 4 add various MPRs to the volatility-volume relation: σ t = const. + λ b MPR(b) t + λ s MPR(s) t + c ln(total volume) t + d σ t-1 + e t (3) where λ b, λ s, c, and d are coefficients and e t is the error term. The parameter estimates are corrected for both heteroskedasticity and autocorrelation using Parzen kernel to estimate the covariance matrix (Andrews, 1991). 9 Chan and Fong (2000) consider the roles of trade size and order imbalance on volatility. These variables could also reflect the participation of investors on both the buy and sell sides of trades. 17

20 The results confirm the findings in Table 3. Models 2 and 3 show that the higher market participation of destabilizing traders on the buy side and the participation of foreigners on the sell side increase volatility, even after taking into account the effects of the volatility-volume relation. In Model 4, when we use the market participation ratio of nonprofessional investors on the buy side, the coefficient becomes more significant and the explanatory power of the equation increases. In sum, we find that investor interactions contain information that explains volatility in addition to explanations provided by the traditional volatility-volume relation and lagged volatility. 10 C. Interactions of different investor types and volatility To examine which investor participants have more influence on the results in the previous tables, Table 5 provides a detailed account of investor interactions and their relation to volatility. The table presents mean volatilities when a combination of the investor types interact more in buy and sell trades and compares these with the mean volatilities when they interact less. We take every combination of investor type on both the buy and sell side. The columns represent market participation on the buy side, and the rows represent that on the sell side. The first line in each cell is the mean volatility of a subsample that has high (i.e., above median) market participation ratios of investor type b on the buy side and type s on the sell side; i.e., [MPR(b) > median] and [MPR(s) > median]. The second line in the cell is the difference in 10 Also using data from the TSE, Hamao and Mei (2001) examined the impact of various investor trades on volatility. They examined the impact of buy, sell, and absolute value of net buy trades on volatility for various investor types separately. Although their observation period ( ) was different from ours, they also found that the sell trades of foreign investors slightly increase volatility. 18

21 the mean volatility of the subsample from the rest of the sample. The numbers in the brackets present the p-value for the difference in the two samples from a nonparametric Wilcoxon-Mann- Whitney rank test. The last line in each cell is the difference in the mean volatility of the two subsamples after adjusting for the volatility-volume relation and lagged volatility. In the volatility-volume regression with lagged volatility, we add a dummy variable that takes the value of 1 when [MPR(b) > median] and [MPR(s) > median], and 0 otherwise. The coefficient on the dummy variable measures the shift in volatility when investor type b and type s interact intensively. The standard errors of the regression estimates are adjusted for both autocorrelation and heteroskedasticity in the same manner as in Table 4 above. The entire cell is highlighted when the high-participation subsample has higher volatility than the rest of the sample and the cell is not highlighted when the high-participation subsample has lower volatility. The results indicate the following: (i) The high participation of individuals or nonfinancial corporations tends to show higher volatility than with other participants. When foreigners sell against these investors, volatility is higher, which confirms the previous results. Particularly, when nonfinancial corporations buy more and foreign investors sell more at the same time, volatility increases by almost 40% (=2.929/2.103) compared with the rest of the sample. 19

22 (ii) The high participation of financial institutions is generally associated with lower volatility relative to the rest of the sample, or it does not affect the volatility after adjusting for the volatility-volume relation and lagged volatility. Since proprietary traders are more likely to provide liquidity to financial institutions directly (see Panel D in Table 1 and Panel B in Table 2), this fact might explain why the market participation of financial institutions are generally associated with lower volatility levels. (iii) The high participation of foreign investors tends to increase volatility for most cases, which is consistent with our previous findings that buy trades and sell trades of foreign investors are potentially destabilizing to the market. Also, proprietary traders are not as accommodative to foreign investors in supplying liquidity as to domestic financial institutions (see Panel B in Table 2), which might also explain the higher volatility levels. (iv) Greater participation from proprietary traders is generally associated with lower volatility, which is consistent with the traders role as the major provider of market liquidity. There are a few exceptions. In these cases, volatility is not necessarily lower when nonprofessional investors are participating more in buy trades despite the fact that proprietary traders are participating more in sell trades (see the first two cells in the last column). In fact, volatility could increase since greater participation from proprietary traders does not necessarily imply that they are providing direct liquidity to nonprofessional investors. V. Reaction of Investors Market Participation to Volatility In this section, we investigate how market volatility affects the market participation of different investor types and we relate this finding to determine which investor participations 20

23 could explain the persistence in volatility. In Table 6, we regress market participation ratio (MPR) of different investor types on either contemporaneous or lagged market volatility and lagged market participation ratio. 11 In separate regressions, we use ln(total trading volume) in place of the volatility since some investors might respond to market-wide trading activity as well. Since we know that MPR affects volatility contemporaneously (see Tables 4 and 5), there would be a simultaneous equation bias in the estimated coefficient when we use the contemporaneous volatility in the regression. Therefore, we use instrumental variable approach where we use lagged MPR, one-week and two-week lagged volatility as instruments for contemporaneous volatility. Our result shows that Buy MPR of individual investors is positively and significantly sensitive to both contemporaneous and lagged volatility, while their Sell MPR is not. The Buy MPR of individual investors is also positively sensitive to total trading volume of the market. This result supports the findings by Barber and Odean (2002) that used detailed transaction data of individual investors accounts in the US and found that individual investors tend to buy on days that follow extreme price movements and when trading volume is abnormally high. On the other hand, we find that the buy participation ratio of financial institutions and sell participation ratio of foreign investors are negatively sensitive to volatility and market trading volume. 11 As volatility and total market volume is positively correlated (see Table 4) and trades among different investors are also positively correlated, there are positive correlations between volatility and buy or sell trades of different investors. Since the sensitivity of trades to volatility differs between different investors, a positive (negative) sensitivity of MPR to volatility implies that those trades are more (less) sensitive to volatility than the sensitivity of aggregate volume to volatility. 21

24 Our result implies that buy trades of individual investors increases the persistence of market volatility. Following an increase in market volatility, the buy trades of individual investors tend to increase more than the total market trade (i.e., MPR increases) in consecutive periods. As a result, volatility spills over to consecutive periods. There is an opposite implication for the buy participation of financial institutions. Following a decrease in market volatility, the buy trades of financial institutions tend to increase more than the total market trade (i.e., MPR increases) in consecutive periods. Since the trades of financial institutions are generally associated with lower volatility, lower volatility spills over to the following periods. The implication for the sell trades of foreign investors is ambiguous since they participate less as market becomes volatile while their sell trades often increase current volatility. VI. Concluding Remarks We investigate the interaction of different investor types and their impact on market volatility by using data that describe trading of all investor types on the Tokyo Stock Exchange (TSE). We find that buy trades of most investors either follow momentum trading patterns. These trades could potentially destabilize the market since they do not provide more liquidity as market returns increase, and vice versa. On the sell side, we find that domestic investors sell more as the returns increase. Their trading pattern is expected to stabilize the market. Compared with those of domestic investors, the sell trades of foreign investors are not sensitive to returns and are less likely to provide liquidity to the momentum buy trades. Based on these conjectures, we examine the relation between market volatility and the market participation of various investor types for both buy and sell trades. Consistent with the intuition above, we find that market volatility is higher when more buy trades by noncontrarian 22

25 investors (i.e., momentum investors and those investors whose trades are not sensitive to market returns) face more sell trades by foreign investors. We infer from the result that volatility increases since neither investor s trading pattern supplies liquidity to each other. We also find that volatility is even higher (by more than 35%) when buy trades by nonprofessional investors such as individuals and nonfinancial corporations face more sell trades by foreigners. This result is most likely due to the fact that proprietary traders of brokerage firms do not provide liquidity to nonprofessional investors. We find that these results are robust even after adjusting for the volatility-volume relation and lagged volatility effects. We also find that individual investors keep trading even when the market becomes volatile, which in return makes volatility more persistent. On the other hand, foreign investors are quick to withdraw from trades following a volatile market in which their trades could have increased the volatility. 23

26 REFERENCES Andrews, Donald, 1991, Heteroskedasticity and autocorrelation consistent covariance matrix estimation, Econometrica, 59, Barber, Brad M., and Terrance Odean, 2002, All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors, Working paper, University of California at Berkeley. Bekaert, Geert, Campbell R. Harvey, and Robin L. Lumsdaine, 1999, The dynamics of emerging market equity flows, Working paper. Brennan, Michael, and Henry Cao, 1997, International portfolio investment flows, Journal of Finance, 52, Cai, Fang, Gautam Kaul, and Lu Zheng, 2000, Institutional trading and stock returns, Working paper, University of Michigan Business School. Chan, Kalok and Wai-Ming Fong, 2000, Trade size, order imbalance, and the volatility-volume relation, Journal of Financial Economics, 57, Chan, Louis K.C. and Joseph Lakonishok, 1993, Institutional trades and intraday stock price behavior, Journal of Financial Economics, 33, Choe, Hyuk, Bong-Chan Kho, and René Stulz, 1999, Do foreign investors destabilize stock markets? The Korean experience in 1997, Journal of Financial Economics, 54, Cohen, Randolph B., 1999, Asset allocation decisions of individuals and institutions, working paper, Harvard Business School, Cambridge, MA. Cohen, Randolph B., Paul A. Gompers, and Tuomo Vuolteenaho, Who underreacts to cash-flow news? Evidence from trading between individuals and institutions, working paper, Harvard University, Cambridge, MA. Daigler, Robert T. and Marilyn K. Wiley, 1999, The impact of trader type on the futures volatility-volume relation, Journal of Finance, 54, De Long, J. Bradford, Andrei Shleifer, Lawrence H. Summers, and Robert J. Waldman, 1990, Positive feedback investment strategies and destabilizing rational speculation, Journal of Finance, 45, Froot, Kenneth A., Paul G. J. O Connell, and Mark S. Seasholes, 2001, The portfolio flows of international investors, Journal of Financial Economics, 59, Goetzmann, William N. and M. Massa, 1999, Daily momentum and contrarian behavior of index fund investors, Working paper, Yale International Center for Finance. 24

27 Grinblatt, Mark and Matti Keloharju, 2000, The investment behavior and performance of various investor-types: A study of Finland s unique data set, Journal of Financial Economics, 55, Grinblatt, Mark and Matti Keloharju, 2001, What makes investors trade? Journal of Finance, 56, Hamao, Yasushi and Joel Hasbrouck, 1995, Securities trading in the absence of dealers: Trades and quotes on the Tokyo Stock Exchange, Review of Financial Studies, 8, Hamao, Yasushi and Jianping Mei, 2001, Living with the enemy : An analysis of foreign investment in the Japanese equity market, Journal of International Money and Finance, 20, Karolyi, G. Andrew, 2001, Did the Asian financial crisis scare foreign investors out of Japan? Working paper, Ohio State University. Karpoff, Jonathan, 1987, The relationship between price changes and trading volume: A survey, Journal of Financial and Quantitative Analysis, 22, Nofsinger, John R. and Richard W. Sias, 1999, Herding and feedback trading by institutional and individual investors, Journal of Finance, 54, Shleifer, Andrei, 1986, Do demand curves for stocks slope down? Journal of Finance, 41, Shleifer, Andrei, 2000, Inefficient Markets: An Introduction to Behavioral Finance, Oxford University Press, Oxford. Warther, Vincent A., 1995, Aggregate mutual fund flows and security returns, Journal of Financial Economics, 39, Wurgler, J., and K. Zhuravskaya, 1999, Does arbitrage flatten demand curves for stocks? Journal of Business, forthcoming. 25

28 Table 1 Descriptive Statistics of Trades of Different Investor Types This table presents descriptive statistics of the Japanese stock market by investor type. We calculate Panels A, C and D from the First Section of the Tokyo Stock Exchange (TSE). These panels cover trading brokered by member securities companies of the TSE with at least 3 billion yen of capital and proprietary trading of the member securities companies. Panel A presents average buy and sell volume during the observation period for each investor type and its proportion in total trading volume. We present the standard deviation of the proportions as well as the maximum and minimum values of the proportions. Panel B shows the turnover ratio that we calculate by dividing the annual trading volume (for major exchanges) by the ownership of shares outstanding at the beginning of the year of all stock exchanges in Japan. (Similar data for the TSE are not available.) In Panel C, we present correlations between net buy volume and market index return for various investor types. We use the value-weighted TOPIX (First Section) for the market index. Panel D presents rank correlation coefficients between buy and sell market participation ratios for different investor types. The buy (sell) market participation ratio is buy (sell) trading volume of each investor type divided by the total buy (sell) trading volume. We detrend the ratios using 26 week moving average. We show p-values in the brackets. The data were obtained from the Nomura Research Institute. The observation period is from the first week of January 1991 to the last week of April 1999, unless otherwise noted. All investors Individuals Nonfin. cos. Mutual funds Insurance cos. A. Average weekly trading volume, in million shares and % of total trading volume Buy volume Banks Foreigners Proprietary traders % (Std. dev.) - (5.3) (1.4) (4.1) (1.0) (4.6) (5.1) (5.3) Max Min Sell volume % (Std. dev.) - (5.7) (2.0) (3.9) (1.6) (3.6) (6.3) (6.2) Max Min B. Average turnover ratio, as annualized rate C. Correlations between market return and net buy volume N/A D. Correlations between buy and sell market participation ratios Sell participation ratio Buy participation ratio Individuals Nonfin. Mutual Insurance Individuals (<.0001) Nonfinancial cos (0.073) Mutual funds (0.292) Insurance cos (<.0001) Banks (0.196) Foreigners (<.0001) Proprietary traders (<.0001) cos (<.0001) (<.0001) (0.377) (0.733) (0.378) (0.013) (<.0001) funds (0.007) (<.0001) (0.638) (0.012) (<.0001) (<.0001) (0.003) cos (<.0001) (0.000) (0.101) (<.0001) (0.100) (0.004) (0.004) Banks Foreigners Proprietary traders (<.0001) (0.097) (<.0001) (0.002) (0.896) (0.373) (0.913) (0.259) (0.384) (0.044) (0.005) (0.001) (0.649) (0.181) (<.0001) (<.0001) (0.029) (0.007) (<.0001) (0.158) (0.157) 26

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