Institutional Sell-Order Illiquidity and Expected Stock Returns

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1 Institutional Sell-Order Illiquidity and Expected Stock Returns Qiuyang Chen a Huu Nhan Duong b,* Manapon Limkriangkrai c This version: 31 st December 2015 JEL classifications: G10, G20, G24 Keywords: Sell-order illiquidity, Institutional investors, Stock returns a, b, c Authors are from the Department of Banking and Finance, School of Business and Economics, Monash University, Melbourne, Australia * Corresponding author: Huu Nhan Duong, Department of Banking and Finance, Monash Business School, Monash University, 900 Dandenong Road, Caulfield East, 3145, Australia. Telephone: (+613) ; Acknowledgements: We are grateful to the Securities Industry Research Centre of Asia-Pacific (SIRCA) for providing the data used in our study. We are also grateful for helpful comments from Avanidhar Subrahmanyam, Henk Berkman, Petko Kalev, Phil Gray, Te-Feng Chen (the discussant), Eric Lam, Clark Liu, Talis Putnins, session participants at the 2015 Behavioural Finance and Capital Markets Conference and the 23 rd Conference on the Theories and Practices of Securities and Financial Markets. All remaining errors are our own.

2 Institutional Sell-Order Illiquidity and Expected Stock Returns Abstract Brennan, Chordia, Subrahmanyam and Tong (2012) document that sell-order illiquidity is priced more significantly than buy-order illiquidity in the cross-sectional stock returns. We decompose the buy- and sell-order illiquidity into institutional and individual components and show that institutional sell-order illiquidity is the most significantly priced illiquidity variable. We further show that the significant institutional orderilliquidity effect is driven by both the informational and liquidity-motivated mechanisms. Our findings highlights the importance of incorporating trader identities in the analysis of the illiquidity premium associated with buy- and sell-side trades. JEL classifications: G10, G20, G24 Keywords: Institutional investors, Sell-order illiquidity, Stock returns 1

3 1. Introduction The relation between liquidity and returns is one of the most researched areas in the finance literature. The seminal paper by Amihud and Mendelson (1986) shows that the bid-ask spread is positively associated with expected stock returns. Brennan and Subrahmanyam (1996) find that both the variable and fixed components of the price impact model have significant positive relations with stock returns. Amihud (2002) pioneers the illiquidity proxy based on the ratio of the absolute stock return to dollar volume, and shows that illiquidity has a significant positive effect on stock returns. Other studies examine the return-illiquidity relation through various alternative illiquidity proxies. 1 In spite of the vast literature on liquidity and asset pricing, extant research uses liquidity measures that assume a symmetric relation between order flow and price change (Brennan, Chordia, Subrahmanyam and Tong, 2012). In contrast to those conventional liquidity measures, Brennan et al. (2012) estimate buy and sell measures of illiquidity separately. They find that sell-order illiquidity has a greater effect on stock returns than buy-order illiquidity. We extend Brennan et al. s (2012) asymmetric illiquidity framework to further decompose buy- and sell-order illiquidity into institutional and individual components. The distinction between institutional investor and individual orders is important because these two classes of investors potentially differ in their possession of information (Barclay and Warner, 1993; Nofsinger and Sias, 1999 and Chakravarty, 2001). Moreover, the price pressure exerted by institutional trading might offer one of the explanations as to why sell-order illiquidity is more strongly associated with stock returns than buy-order illiquidity. Shleifer and Vishny (1997) and Gromb and Vayanos (2002) show that when 1 See, for example, Datar, Naik and Radcliffe (1998), Brennan, Chordia and Subrahmanyam (1998), Jacoby, Fowler and Gottesman (2000), Jones (2002) and Chordia, Huh and Subrahmanyam (2009). 2

4 financial constraints such as margin calls or investor redemption are imposed on institutional investors, institutions can be forced to liquidate their positions prematurely. The adverse price impact induced by institutional sell-offs can significantly reduce liquidity in the market and result in further price reduction. Therefore, institutional sellorder trades are particularly sensitive in illiquid stocks, and as such, institutional investors are willing to pay a higher illiquidity premium on their sell-side orders. In addition, Kaniel, Saar and Titman (2008) show that the short-term return reversal on winner and loser stocks are a form of illiquidity premium compensation to individual traders who provide liquidity to institutional investors. Furthermore, Campbell, Ramadorai and Schwartz (2009) demonstrate that institutional trades, particularly sell trades, appear to generate short-term losses. In fact, these losses reflect the institutional demand for liquidity. We estimate the individual and institutional order illiquidity based on Brennan et al. s (2012) price impact model by utilizing intraday transaction data from the Australian Securities Exchange (ASX) over the period from January 1996 to December Following the framework of Brennan, Chordia, and Subrahmanyam (1998), individual stock returns are adjusted for Fama and French (1993) three factors as well as Carhart (1997) momentum factor to account for the errors-in-variable problem. We first show that sell-order illiquidity is priced more significantly than buy-order illiquidity in the cross-sectional stock returns, which is consistent with Brennan et al. (2012). We further find that the institutional sell-order illiquidity is the most significantly priced illiquidity variable in the asset pricing test. For the largest stock size quintile, the long-short portfolio sorted on institutional sell-order illiquidity produces a significant risk-adjusted stock return of 1.65% per month. The prominence of institutional sell-order illiquidity is robust to various well-known firm characteristics and the correction of 3

5 market microstructure asset pricing biases. More importantly, after accounting for the effect of institutional sell-order illiquidity in asset pricing models, individual trading induced order illiquidity only plays a minor role in explaining stock returns. Having established a strong positive relation between institutional order illiquidity and stock returns, we next explore the underlying mechanisms behind this relation. We posit that the prominence of institutional order illiquidity can be attributed to both the information and liquidity effect of institutional price impact. The information and liquidity hypotheses are examined extensively in the literature. Holthausen et al., (1990) and Chan and Lakonishok (1993) find that most of the price impact induced by institutional block trading is permanent, and this permanent price effect is driven by the information content of institutional trades. On the other hand, Lakonishok et al., (1992), Nofsinger and Sias (1999) and Wermers (1999) document a temporary liquidity impact of institutional trading on contemporaneous stock returns, and that liquidity impact tend to dissipate quickly in the short-run. We use two types of events to test the information and liquidity hypotheses of institutional order illiquidity effect, namely, the corporate announcements and the S&P/ASX 200 Index quarterly rebalancing announcements. To test the information hypothesis, we investigate the relation between the pre-announcement abnormal levels of individual/institutional order illiquidity and the three-day announcement-period cumulative abnormal returns for the unscheduled announcements, such as share buyback, director appointment/resignation and takeover bid. We focus on the unscheduled announcements because there are no predetermined announcement dates. This makes the announcement returns more difficult to be inferred from the public information, which allow us to detect the information content of institutional order illiquidity. If the prominence of institutional order illiquidity is driven by the informational content of the 4

6 price impact, we would expect the pre-announcement institutional order illiquidity to have predictive power on the forthcoming announcement returns. Our analyses show that the pre-announcement institutional buy-order (sell-order) illiquidity has a positive (negative) relation with the three-day announcement returns. In contrast, the preannouncement individual buy and sell-order illiquidity have no effect on the announcement returns. These findings imply that the informational content of institutional order illiquidity can be one of the factors that is driving the significant institutional order illiquidity effect on the stock returns. The information effect of institutional order illiquidity does not rule out the possibility of the liquidity effect. The short-term illiquidity premium is often stemmed from the difficulty for institutional investors to locate the willing counterparties who can transact immediately. The efforts exerted by institutional investors to attract buyers and sellers are observed in the form of illiquidity premium. To test for this hypothesis, we focus on the institutional trading surrounding the announcement date of the S&P/ASX 200 Index deletion. The index deletion is an ideal setting to test the liquidity hypothesis, given that the announcement date of index deletion is publicly accessible and some institutional investors (i.e., index funds) are forced to rebalance their portfolios to minimize their tracking errors. This provides us a channel to gauge the behavior of the price pressure generated by institutional trading when changes to the index are announced. Moreover, if market is efficient, then the rebalance of the index itself is information-free. Hence, this can help us to disentangle the liquidity effect from the information effect in institutional order illiquidity. We observe a sharp decrease (increase) in institutional buy-order (sell-order) illiquidity upon the announcement of the S&P/ASX 200 Index deletion. The decrease in institutional buy-order illiquidity is temporary, the magnitude of institutional buy-order 5

7 illiquidity quickly bounce back to the pre-announcement level within one day following the announcement of index deletion. In contrast, the sharp increase has a more prolonged impact on the overall magnitude of institutional sell-order illiquidity. The responses of individual buy- and sell-order illiquidity only emerge four to five trading days after the index deletion is announced. Therefore, the contemporaneous relation between the announcement of index deletion and changes in institutional order illiquidity provides indirect evidence for the liquidity effect. The results of this study contributes to a recent stream of market microstructure and asset pricing studies by decomposing the aggregate buy- and sell-order illiquidity by investor types into institutional and individual. As highlighted by Brennan et al. (2012), the market microstructure literature traditionally focuses on the illiquidity proxies in a symmetric framework. However, recent studies find that the impact of illiquidity on asset returns is clearly asymmetric (Brennan et al., 2012; Brennan et al., 2013; and Brennan et al., 2014). This is the first study to show that institutional sell-order illiquidity is the most prominent illiquidity proxy in predicting future stock returns. Our findings of information and liquidity effects on the institutional price impact induced illiquidity premium also complements the literature on the influence of institutional trading on stock prices (Holthausen et al., 1990; Lakonishok et al., 1992; Chan and Lakonishok, 1993, 1995). The remainder of the paper is structured as follows: Section 2 outlines the methodology. Section 3 describes the sample and data. Section 4 reports the empirical results on individual and institutional buy- and sell-order illiquidity. Section 5 details the empirical results on the underlying mechanisms of institutional order illiquidity. Section 6 presents the robustness check of the findings. Section 7 concludes the paper. 6

8 2. Methodology 2.1 Buy- and sell-order illiquidity and cross sectional of stock returns Following Brennan et al. s (2012) approach, this paper utilizes the ASX intraday transaction data to measure buy- and sell-order illiquidity (henceforth, buy and sell lambdas) separately. The estimation of lambdas is based on the modified Glosten and Harris (1988) model: ΔP t = α + λ buy (q t q t > 0) + λ sell (q t q t < 0) + ψ(d t D t 1 ) + y t, (1) where ΔP t represents changes in price at time t between transactions, λ buy and λ sell refer to buy and sell lambdas, respectively. qt is the size of a transaction as measured by the dollar value of shares traded. Dt represents the direction of the incoming order at time t (+1 indicates a buyer-initiated order and -1 indicates a seller-initiated order). ψ is the coefficient on (D t D t 1 ) which captures the changes in trade direction. The parameters in Eq (1) are estimated monthly for each stock using ordinary least squares (OLS) and yt is treated as an error term. 2.2 Institutional and individual buy/sell lambdas To test whether institutional buy/sell lambdas have greater significant effect on stock returns than individual buy/sell lambdas, Eq (1) is modified to specifically incorporate investor identity. Following Fong, Gallagher and Lee (2014), this study classifies brokers into retail and institutional brokers using information from brokers websites and Factiva searches. Institutional trades are identified as those executed through institutional brokers; whereas individual trades are identified as those executed through retail brokers. Such approach of classifying institutional and individual traders using broker identities is supported by Linnainmaa and Saar (2012) who suggest that broker identity is an effective tool to make inference about identities of investors. Hence, 7

9 the institutional and individual buy/sell lambdas are estimated through the following regression: ΔP t = α + λ Inst_buy (q t q t > 0&Inst) + λ Inst_sell (q t q t < 0&Inst) (2) + λ Ind_buy (q t q t > 0&Indv) + λ Ind_sell (q t q t < 0&Indv) + λ Unc_buy (q t q t > 0&Unc) + λ Unc_sell (q t q t < 0&Unc) + ψ(d t D t 1 ) + y t, where λ Inst_buy and λ Ind_buy represent institutional buy lambda and individual buy lambda, respectively; λ Inst_sell and λ Ind_sell represent institutional sell lambda and individual sell lambda, respectively. λ Unc_buy and λ Unc_sell represent the unclassified buy and sell lambdas, given that there may be insufficient information on brokers websites and Factiva to classify them as either institutional or retail broker. 2.3 Stock-level cross-sectional regression for lambdas For asset pricing test, we apply the Brennan, Chordia and Subrahmanyam (1998) approach. In this section, we test whether the lambdas as illiquidity proxies has any incremental explanatory power on stock returns relative to the four-factor benchmark after controlling for other well-known stock characteristics. To alleviate the error-invariable issue, we use risk-adjusted returns for the cross-sectional regression test (Huh, 2014). More specifically, the individual stock returns are adjusted for Fama-French- Carhart (FFC) factors i.e. market (MKT t ), size (SIZE t ), book-to-market (HML t ), and momentum ( UMD t ) factors (Fama and French, 1993; Carhart, 1997) based on two methods. In the first method, the individual stock raw returns are regressed on FFC factors to generate the intercepts and residuals each month, subsequently, the sum of the intercepts and residuals are computed as FFC risk-adjusted return R jt 1, i.e. 8

10 R it 1 = (R it R ft ) (β i1 MKT t + β i2 SMB t + β i3 HML t + β i4 UMD t ), (3) where the four factor loadings are estimated by using the time-series range of the sample (from January 1996 to December 2012). Hence, one set of factor loading, β ik is estimated 1 for each stock by using the entire time series of data. The risk-adjusted return, R it is denoted as EXSRET1. In the second method, the rolling estimates of the factor loadings, β ik are generated for all stocks each month over the entire sample period using the time series over the past 36 months (with at least 24 months of past returns). The factor loadings, β ik are then substituted back to Eq (4) for sample fitting process to obtain the sum of intercepts and residuals as the second risk-adjusted return, R it 2, i.e. R it 2 = (R it R ft ) (β i1 MKT t + β i2 SMB t + β i3 HML t + β i4 UMD t ). (4) 2 Hence, this risk-adjusted return, R it is denoted as EXSRET The risk-adjusted return, R it and R it are then used as dependent variables for the Fama-MacBeth (1973) cross-sectional regression. The t-statistics of average slope coefficients are computed with Newey-West (1987) standard errors. The regression specification closely follows Chordia, Subrahmanyam and Anshuman (2001): R it m j = c 0 + λ i,t 1 N + c n X ni,t 1 + ε i,t, n=1 A (5) j = λ buy, λ sell, λ Ind_buy, λ Ind_sell, λ Inst_buy or λ Inst_sell j where m = 1 or 2, λ i,t 1 is one of the six lambdas for stock i in month t 1 estimated in Eq (1) and Eq (2). X i,t 1 is a vector of control variables for stock i in month t 1. All of the explanatory variables are lagged by one month in Eq (5), as well as in subsequent cross-sectional regressions. Based on well-known stock return determinants, in spirit of Fama and French (1993), Jegadeesh and Titman (1993), Ang, Hodrick, Xing and Zhang (2006) and Amihud (2002) the control variables are defines as follows: 9

11 SIZE: measured as the natural logarithm of the market capitalization. BM: measured as the natural logarithm of the book value of the firm s equity to its market value of equity. SH_TURN: measured as the natural logarithm of the company s share turnover rate which is computed as the trading volume divided by the total number of shares outstanding. RET1-3: measured as buy- and-hold return on stock from month t 1 to t 3. RET4-6: measured as buy- and-hold return on stock from month t 4 to t 6. RET7-9: measured as buy- and-hold return on stock from month t 7 to t 9. RET10-12: measured as buy- and-hold return on stock from month t 10 to t 12. IVOL: idiosyncratic volatility is measured as the volatility of the idiosyncratic return (ε i,d ). The idiosyncratic return (i.e. the residual term) is computed by regressing daily stock return on a value-weighted market index and daily Fama-French factors over a maximum of 250 days ending on December 31 of year t. Amihud: measured as the average daily ratio of the absolute stock return to the dollar trading volume within the month t. 3. Data 3.1 Sample collection This paper examines the positive return-lambda relations for ordinary shares listed on the ASX over the period January 1996 to December We use intraday transaction data from the Australian Equities Tick History Service (AETHS), supplied by the Securities Industry Research Centre of Asia-Pacific (SIRCA). The AETHS database provides details on every order submitted to the central limit order book which comprise of the stock code, the order type (order submission, order revision, order cancellation and execution), the order price, the order direction (buy or sell order), date and time. 10

12 Therefore, the study is able to classify trades into buyer-initiated and seller-initiated trades based on the order directions (Dt in Eq (1)) without relying on Lee and Ready (1991) algorithm. In addition, this dataset provides broker IDs for every order submission and execution. We then match the broker IDs provided by SIRCA with the broker list from IRESS to identify the broker firm used for every order. We classify brokers with the same approach as Fong et al. (2014) by using information on brokers websites and Factiva searches. The dollar volume of each transaction is used to measure the variable qt in Eq (1). To avoid extreme illiquid stocks, this study only includes stocks with at least 300 trades per month. 2 The monthly stock returns, market capitalization, number of share outstanding are obtained from Share Price and Price Relative (SPPR) database. The accounting variable data such as book value of total shareholder equity is obtained from Aspect Huntley. The SPPR group ticker code is used to cross-match the intraday transaction data from AETHS to monthly stock data from SPPR as well as accounting data from Aspect Huntley. The overall characteristics of sample stocks are close to top 500 largest stocks based on the market capitalization listed on the ASX. 3.2 Sample characteristics Panel A of Table 1 presents the summary statistics for the buy and sell lambda variables scaled by previous month-end stock prices. The mean of sell lambda is greater than the mean of buy lambda by approximately 9.5% and the difference is significant with t-statistic in excess of 9. 3 The magnitude of sell lambda exceeds buy lambda at the 25 th 2 We also used other trade filters, for example, 100 trades and 200 trades per month, the results are quantitatively similar. The results of alternative trade filter samples are available upon request. 3 Provided the mean of buy lambda is and mean of sell lambda is , therefore, the difference is ( / ) - 1 = 9.53%. 11

13 percentile, median and the 75 th percentile which indicates that sell lambda s greater magnitude is not driven by a particular lambda sub-sample. In addition, the standard deviation of sell lambda is also greater than buy lambda s standard deviation, indicating that sell lambda is more volatile. Panel A also reports that there are 70% of buy lambdas and 72% of sell lambdas are estimated with t-statistics exceeding The time-series average of the cross-sectional buy lambda has a t-statistic of 4.84, while sell lambda has a slightly higher t-statistic of The greater statistical significance and magnitude of sell lambda suggest that the sell order flow has a greater impact on price changes, compared with the buy order flow. The higher sell lambda indicates that on average, investors are willing to pay a higher illiquidity cost for a sell order transaction compared with a buy order transaction. The above findings are consistent with Brennan et al. (2012) who document that sell lambda exceeds buy lambda both in terms of the magnitude and the level of statistical significance. Panel B of Table 1 presents the summary statistics for individual and institutional buy and sell lambdas. The prominence of sell-order illiquidity remains significant at both individual and institutional levels. The mean of individual sell is greater than that of individual buy and the difference is statistically significant with a t-statistic of The mean of institutional sell also exceeds that of institutional buy with a mean difference test, t-statistic of It is worth noting that the magnitude of institutional lambda is far greater than the average lambda level. For example, the institutional sell lambda is 25% higher than average sell lambda, whereas the individual sell lambda is slightly less than the average sell lambda at around 3%. This evidence suggests that on average, institutional investors trading induced price impact is higher than that of individual investors. The higher magnitude of institutional lambda over individual lambda is expected, given that the institutional trades are more likely to generate a larger price 12

14 impact on stock return than that of individual trades. Holthausen et al., (1990) and Chan and Lakonishok (1993, 1995) argue that the large price impact of institutional trading are often stemmed from either the short-run liquidity costs or the information effects of their transactions. The mean difference test on null hypotheses that the average of individual buy lambda equals to the average of institutional buy lambda and the average of individual sell lambda equals to the average of institutional sell lambda are rejected with t-statistics of and 16.27, respectively. The statistical significant difference between individual lambda and institutional lambda indicates the importance of differentiating between institutional and individual trade-order illiquidity. Panel C of Table 1 reports the mean and standard deviation of lambda when the market monthly excess value-weighted returns are positive and negative. During the up-day market, both buy and sell lambdas are slightly lower compared with average buy and sell lambdas in Panel A. In contrast, when the current market is down, the magnitudes of buy and sell lambdas both increase significantly approximately 3.12% higher. 4 A similar pattern is also found at individual and institutional levels. Lastly, the mean difference tests in Panel C are all rejected at the 1% level suggesting that the prominence of sell lambda over buy lambda is robust for different investor types and across different market conditions. The results are consistent with the empirical findings in Chordia, Roll and Subrahmanyam (2000, 2001). [Insert Table 1] 4. Empirical Results of Asymmetric Order illiquidity 4.1 Portfolio analyses 4 The average lambda level when market is down: = ( )/2; whereas the overall average lambda level in Panel A: = ( )/2. Hence, the incremental amount when market is down: 3.12% = ( / )

15 Following Chordia, Huh and Subrahmanyam (2009), we first sort stocks into five lambda quintiles, and subsequently each lambda quintile is then sorted into five size quintiles. This sorting procedure results in total of 25 portfolios. We then compute valueweighted portfolio returns based on the market capitalization from the end of previous month. The size and lambda groups are denoted as SIZE i and Illq i (where i = 1~5). For consistency, stocks used for the portfolio analyses are limited to those used for our main regression analyses in the subsequent section. We report the intercepts from the timeseries regression of 25 value-weighted portfolio returns (in excess of 13-week Treasury notes rate) regressed on the Fama-French-Carhart four factors (FFC) in Table 2. 5 [Insert Table 2] Table 2 Panel A shows that the intercepts for the top three most liquid buy lambda portfolios are generally insignificant, suggesting that they can be explained by the FFC factors. However, the highest buy lambda quintile (i.e. the most illiquid portfolio) yields statistically significant FFC alphas for all size quintiles, except for size quintile five. The table also reports the intercepts from the time-series regressions of return differences (i.e. the highest lambda quintile return minus the lowest lambda quintile return). These risk-adjusted hedge returns are positive and statistically significant except for the biggest size quintile. This result suggests that the buy lambda effect concentrates in small stocks. Panel A further shows that the result of 25 sell lambda and size sorted portfolios are generally similar to those of buy lambda. It is worth noting that the hedge returns for sell lambda are positive and significant in all five size quintiles. This indicates that the sell lambda effect is more pervasive than the buy lambda effect on stock returns, which is consistent with Brennan et al. (2012). The economic significance of sell lambda effect can 5 We also compute the equal-weighted mean returns for 25 portfolios formed by sorting stocks into lambdas and firm size which leads to similar results. 14

16 be interpreted based on the FFC alpha from high minus low return difference for the biggest size group (i.e. the size quintile five) which is 0.99% per month. The Panel B reports the FFC alphas of individual buy and sell lambda portfolios. The return patterns of individual buy and sell portfolios are similar to those reported in buy and sell lambdas. However, the lambda high-low difference alphas show that the magnitude of hedge returns produced by individual buy and sell portfolios are generally smaller compared to the hedge returns of buy and sell lambda portfolios reported in Panel A. This indicates that the effects of individual buy and sell lambdas on stock returns are relatively weaker compared to the effects of buy and sell lambdas. Moreover, the hedge returns for the biggest size quintiles are both insignificant for individual buy and sell portfolios, indicating that the individual buy- and sell-lambda effects are predominately concentrated in small stocks. The Panel C reports the FFC alphas of institutional buy and sell lambda portfolios. The FFC alphas for hedge returns are only significant for the first four size quintiles for institutional buy lambda. However, the hedge returns are all positive and statistically significant for institutional sell lambda sorted portfolios. The high-low difference for the size quintile five yields a risk-adjusted return of 1.65% per month and it is significant at the 1% level. In the biggest size quintile, the effect of institutional sell lambda on stock returns is much more pervasive compared to the sell lambda both in magnitude and statistical significance. The portfolio sorting is only a preliminary examination of the return-lambda relation, given that the sorting analyses do not account for other firm characteristics at the individual stock level. We address this issue by conducting crosssectional regressions in the following section. 4.2 Cross-sectional regressions 15

17 In this section, we examine whether the six lambda measures have any explanatory power on stock returns at the individual stock level. As in Eq (5), the Fama- Macbeth (1973) cross-sectional regression is performed each month. Table 3 reports the time-series averages of the coefficients of six lambdas and other control variables, the associated t-statistics are calculated with Newey-West (1987) standard errors. Table 3 Panel A shows the average coefficient of buy lambda with the unadjusted excess returns (i.e. EXSRET0) is positive and statistically significant at the 1% level. Similarly, the positive and significant average coefficient of sell lambda indicates that sell-order illiquidity is positively associated with stock returns after controlling for other stock characteristics. These results confirm that both buy- and sell-order illiquidity have a positive relationship with stock returns. The second column reports the first method adjusted return (i.e. EXSRET1). The buy and sell lambdas remain positive and significant after accounting for the FFC risk factors together with firm characteristics. Similar results can be observed in the third column, when we replace the dependent variables with EXSRET2, the buy and sell lambdas remain robust. [Insert Table 3] The signs of the coefficients on the control variables are consistent with prior literature. For instance, the negative coefficient on the firm size and positive slope on book-to-market ratio are consistent with Fama and French (1992, 1993). The average coefficients on momentum variables are positive and significant. This is consistent with stock price momentum in Jegadeesh and Titman (1993). It is worth noting that the other two alternative liquidity proxies share turnover and Amihud (2002) measure are less significant compared to buy and sell lambdas. This might indicate that the low frequency based liquidity proxies do not provide much adverse selection related information to the 16

18 asset pricing model after controlling for high frequency data estimated liquidity variable lambda. This result is consistent with the findings in Huh (2014). Table 3 Panel B reports the regression results for individual buy and sell lambdas. All three regression specifications suggest that both individual buy and sell lambdas have a positive and significant effect on stocks returns. However, the magnitude of average coefficients of individual buy and sell lambdas are smaller in comparison with the coefficients of buy and sell lambda in Panel A. This indicates that individual order illiquidity has less impact on stock returns compared with buy- and sell-order illiquidity. Table 3 Panel C shows that institutional buy and sell lambdas both have a positive and significant relationship with stock returns. It is clear that institutional sell lambda has the highest level of statistical significance among six lambda variables tested in this section. This result is consistent with notion that the institutional investor is willing to offer illiquidity premium for their requirement of liquidity immediacy. This is particularly true for their sell-side orders (Campbell, Ramadorai and Schwartz, 2009; Kaniel, Saar and Titman, 2008). Similarly to Brennan et al. (2012), we control the effect of buy-order illiquidity for sell-order illiquidity at both individual and institutional levels to show that the prominence of sell lambda is not driven by the buy lambda. For brevity, we only report the regression results on the second type of FFC-adjusted return (i.e. EXSRET2) in Table 4. 6 Panel A of Table 4 shows the result when both buy and sell lambdas are included in the regression, the statistical significance of sell lambda remains robust. On the other hand, the t-value of buy lambda is reduced from 5.19 to 2.84 after the inclusion of sell lambda, indicating that the sell-order illiquidity is priced more significantly in stock 6 The results are quantitatively similar when EXSRET0 and EXSRET1 are used as the dependent variables. 17

19 returns than the buy-order illiquidity. This result is consistent with the findings in Brennan et al. (2012). The prominence of sell-order illiquidity over buy-order illiquidity is robust at both individual and institutional levels. The result in Panels B and C show that the magnitude of coefficients of individual and institutional sell lambda is considerably greater than those of buy lambdas. Our results confirm that the sell-order illiquidity is the predominant driving force for the overall stock illiquidity in the market and this effect holds at both individual and institutional levels. [Insert Table 4] To investigate whether the positive return-illiquidity relation is predominantly driven by institutional order illiquidity, we perform a horse race between individual buy and institutional buy as well as individual sell and institutional sell in Table 5. Again, for brevity, we only report regression results on EXSRET0 and EXSRET2. Panel A of Table 5 shows that when individual and institutional buy lambdas are both included in the regression, institutional buy lambda has a higher coefficient as well as greater statistical significance (for EXSRET0, t-statistics are 4.26 versus 2.59). This result confirms that the magnitude of the positive return-illiquidity relationship is greater for institutional buyorder illiquidity than for individual buy-order illiquidity. Similarly, such asymmetric relationship is more pronounced in their sell-side pair comparisons (for EXSRET0, t- statistics are 7.72 versus 2.96). [Insert Table 5] To demonstrate that the institutional lambda effect is stronger for stocks that have greater institutional order pressure, we create parsimonious measure for relative institutional trading intensity institutional to individual order flow ratio (i.e., II_Ratio). In each month from 1996 to 2012, we aggregate institutional and individual dollar order flow across each stock. The ratio of monthly institutional order flow to monthly individual 18

20 order flow is computed to gauge the relative institutional trading intensity for each stock. We then interact individual lambda and institutional lambda with II_Ratio, respectively. If institutional lambda effect is indeed stronger for stocks that experience higher institutional trading pressure, we would expect the interaction term between institutional lambda and II_Ratio is priced stronger than the interaction between individual lambda and II_Ratio. Table 6 reports the regression results for II_Ratio and its corresponding interaction terms. First, all regression models suggest that II_Ratio has a strong positive effect on stock returns after controlling for other firm characteristics. This indicates that the relative institutional over individual trading pressure is highly informative about future stock returns. In addition, the prominence of institutional lambda over individual lambda remains robust for all model specifications. Panel A of Table 6 shows that Inst_buy II_Ratio subsumes the significance of Ind_buy II_Ratio. Similarly, Panel B shows that after controlling for Inst_sell II_Ratio, Ind_sell II_Ratio does not have an effect on stock returns. The results supports our hypothesis that institutional order illiquidity is priced stronger in stocks that have higher institutional trading pressure, whereas individual order illiquidity plays no role in explaining the returns for stocks that has higher II_Ratio. [Insert Table 6] In summary, we present three important findings in this section. First, sell-order illiquidity is priced more strongly than buy-order illiquidity, and that the prominence of sell-order illiquidity in the asset pricing context holds at both individual and institutional levels. Second, after accounting for institutional investors price impact in the asset pricing test, individual buy- and sell-order illiquidity only play a minor role in explaining the stock returns. The positive return-illiquidity relation is mainly driven by institutional 19

21 buy- and sell-order illiquidity. Third, institutional order illiquidity effect is more pervasive on stocks that has higher relative institutional trading intensity. 5. Underlying Mechanisms of Institutional Lambda Effect Our finding of the positive institutional lambda effect on the cross-sectional stock returns raises the question of what might be the underlying mechanisms behind the dominance of institutional lambda effect on returns over individual lambda. Following both the theoretical and empirical institutional trading literature, we hypothesize that the significant effect of institutional order illiquidity on stock returns is caused by both the information and liquidity effect of institutional price impact (Holthausen et al., 1990; Lakonishok et al., 1992; Chan and Lakonishok, 1993, 1995; Saar, 2001 and Sias et al., 2006). Chan and Lakonishok (1993, 1995) argue that the large institutional trades will cause permanent price changes if the trades themselves reveal private information that is not yet incorporated into the stock price. As uninformed traders who observe such large incoming orders, a risk premium is required to transact with the informed parties. Therefore, the information effect of institutional trading will lead to a positive and significant institutional order illiquidity premium. On the other hand, the short-term illiquidity premium is often caused by the difficulty of institutional investors to search for the willing buyers and sellers who can transact immediately. The efforts exerted by institutional investors to attract counterparties is then translated in the form of positive and significant institutional order illiquidity (Kaniel et al., 2008, and Campbell et al., 2009). We use two types of events to investigate the information and liquidity hypotheses of institutional order illiquidity effect: the corporate announcements and the S&P/ASX 200 Index quarterly rebalancing announcements. 20

22 5.1 Informational role of institutional lambda To test the information hypothesis, we investigate the relation between the preannouncement abnormal level individual/institutional order illiquidity and the three-day cumulative abnormal returns of announcements. If the significance of institutional order illiquidity is driven by the private information content of their trades, we would expect the pre-announcement institutional order illiquidity to have predictive power on the forthcoming announcement returns. We collect all corporate announcements data over the period of 1996 to 2012 from the Australian Corporate Announcement (ACA) database via SIRCA. 7 We focus on the unscheduled announcements only, such as the open-market share buy-back, director appointment/resignation, and the intention to make takeover bid. Given that these announcements do not have a predetermined announcement date, which makes information leakages less likely and thus provide us a cleaner setting to study the information effect of institutional lambda. Following Khan and Lu (2013) and Hao (2015), we first compute the benchmark period lambda level over the window [-60, -11]. We then subtract the benchmark lambda level from the daily lambda during pre-announcement window [-10, -2] and we denote the difference as abnormal level lambda (i.e., AL lambdas). The dependent variable is the three-day [-1, +1] cumulative abnormal returns around the announcement date t (i.e., CAR_3). The SPPR value-weighted market return is used as the benchmark to calculate the CAR_3. To ensure that the result is not driven by other illiquidity proxies, we compute the average share turnover in natural logarithm over the window [-30, -2] and the average daily Amihud ratio over the window [-30, -2] prior to the announcement date t. To control for the potential price run-up leading up to the announcements, we compute 7 We require 60 trading days prior to the announcement to compute the benchmark level lambda, therefore, the starting point of the sample is April

23 the buy-and-hold market adjusted return over the window [-60, -2] as well as the standard deviation of daily stock return over the window [-60, -2] prior to the announcements. Finally, we control for the the firm characteristics, namely the firm size, book-to-market ratio and the stock price. Table 7 reports the panel regression results of the three-day announcement returns on abnormal level lambdas and control variables together with year and industry fixed effects. Following Petersen (2009) and Thomson (2011), all model specifications apply two-way cluster-robust standard errors that cluster by firm and event. The result shows that AL_Inst_buy (AL_Inst_sell) has a positive (negative) relationship with the unscheduled announcements returns and both variables are significant at the 1% level. In contrast, none of the abnormal level individual lambdas have predictive power on CAR_3 which indicates individual pre-announcement trades do not contain any private information. Our finding is consistent with the finding that institutional investors are the more informed groups of investors as documented in prior studies (see, for examples, Cho, 2007; Baik, Kang and Kim, 2010 and Park, Lee and Song, 2014). The asymmetric predictive power between the pre-announcement institutional and individual lambda on the unscheduled announcement returns provide support for the information hypothesis. The information effect of institutional price impact imply that the prominence of institutional lambda can be a form of risk premium required by uninformed investors to transact with informed investors. Hence, this will lead to a positive and significant institutional lambda effect on stock returns. 5.2 Liquidity motivation of institutional lambda effect The information effect of institutional lambda does not rule out the possibility of liquidity hypothesis. To directly examine the liquidity effect of institutional lambda is 22

24 difficult, given that the detailed institutional portfolio holding data is generally unavailable. Therefore, we utilize a unique event where the institutional investors with indexing approach are forced to rebalance their portfolios without fundamental reasons upon the announcement of the S&P/ASX 200 Index deletion. The announcement of index deletion is unique for testing the liquidity-motived institutional lambda effect, because the rebalance of index itself is information-free under the efficient market hypothesis. Hence, this unique characteristic of index deletion allow us to disentangle the liquidity hypothesis from the information hypothesis. The list of stock deletion is manually collected from S&P Dow Jones Indices website. S&P Dow Jones Indices define the effective rebalance date for the S&P/ASX 200 Index as the 3 rd Friday of March, June, September and December. However, the index deletion is announced to the public on the 10 th business day (after market closed) prior to the effective rebalance date. Hence, we define our event day (i.e., the announcement day) as the 9 th business day prior to the 3 rd Friday of March, June, September and December. 8 In each quarter over the period March 2005 to December 2012, we compute the average individual and institutional lambda magnitudes on deleted S&P/ASX 200 stocks over the window [-11, +11] around the announcement of the deletion. 9 Figure 1 shows the average and 95% confidence intervals for individual buy/sell lambda and institutional buy/sell lambda surrounding the announcement of index deletion. Panel A of Figure 1 shows a decrease in individual buy lambda immediately after the announcement of index deletion. This indicates that individual investors tend to 8 The information on the ASX 200 quarterly rebalancing date can be downloaded from S&P Dow Jones Indices website: The screening applied is that if firms had been deleted from the index for reasons other than the breach of market capitalization (spin-off, M&A and other corporate events), we remove those firms from our sample. 9 The S&P/ASX 200 Index was launched on 31 March 2000 and the S&P/ASX 200 Index quarterly announcement files are only available up to March 2005 from S&P Dow Jones website. For the period of March 2000 to December 2004, we backed out the deleted index stocks, however, without knowing the nature of the deletion from the announcement file, we decide to restrict our sample period from March 2005 to December However, the result remains robust with the inclusion of March 2000 to December

25 reduce their purchase of stocks that are to be deleted from the index. Panel B of Figure 1shows that there is a large increase in individual sell lambda during the window [+2, +5] after the announcement. The large increase in individual sell lambda can be attributed to the downward price momentum on removed stocks, which further induces individual investors selling these removed stocks. Panel C of Figure 1 shows that institutional buy lambda remains relatively stable throughout the event window, except that there is a modest decrease in institutional buy lambda on those removed stocks on the announcement date. The magnitude of the institutional buy lambda remains relatively small after the announcement date. This indicates that there are fewer institutional buy demand on the removed stocks. Lastly, in Panel D of Figure 1, there is a sharp increase in institutional sell lambda on the announcement of deletion. The increase in institutional sell lambda on the announcement corresponds to the decrease in institutional buy lambda in Panel C of Figure 1. The increase in institutional sell lambda indicates the increase of institutional relative demand to sell the removed stocks for minimizing the tracking errors. Therefore, the relationship between the announcement of index deletion and changes in the institutional order illiquidity provide an indirect support for the liquidity effect hypothesis. 6. Robustness Tests In this section, we show that our results are robust to microstructure bias correction and also in different subsample periods. 6.1 Weighted least square: microstructure bias correction 24

26 Asparouhova, Bessembinder and Kalcheva (2010) show that estimated illiquidity premium produced by standard cross-sectional regression suffers significantly upward bias due to bid-ask bounce. Therefore, we address this issue by conducting the weighted least square (WLS) regression with the prior-month gross return (i.e. one plus the return in month t 1 ) as the weighting variable. Table 8 shows that after accounting for potential market microstructure bias in the cross-sectional regression, all lambda variables remain statistically significant at the 1% level. In contrast, other low-frequency estimated liquidity proxies become insignificant and the statistical significance of Amihud (2002) measure reduces drastically. [Insert Table 8] 6.2 Subsample Analysis In this section, we provide a detailed subsample analysis to demonstrate that our result is not driven by a particular subsample. Panel A of Table 9 only includes non-crisis period. More specifically, we exclude the Asian Financial Crisis (from July 1997 to December 1998), the Dotcom Crash (from March 2000 to October 2002) and Global Financial Crisis (from July 2007 to June 2009). Panel A shows that all lambda variables are statistically significant during non-crisis period. We also take into account the effect of the launch of Chi-X Australia on market liquidity in the ASX in October Panel B of Table 9 indicates that the exclusion of post-launch of Chi-X Australia does not affect the statistical significance of lambda variables and liquidity shocks. Finally, after excluding the last four high-frequency-trading years (from January 2006 to December 2012) from 10 Chi-X Australia is an alternative equity trading venues offered by Chi-X Global, it was initiated to compete with the primary exchange ASX in Australia. 25

27 the sample, Panel C of Table 9 shows that the statistical significance of lambda variables remain robust. [Insert Table 9] 7. Conclusion This paper extends the asymmetric illiquidity-return asset pricing framework advocated by Brennen et al. (2012). Using a broad trade direction stamped intraday transaction data, this study is the first to show that sell-order illiquidity commands higher return premium than buy-order illiquidity in the limit order book market. Most importantly, our study decomposes buy- and sell-order illiquidity by investor types into institutional and individual components and find that institutional sell-order illiquidity is the most significantly priced illiquidity premium. After institutional order illiquidity effect is controlled for, individual order illiquidity only plays a minor role in explaining stock returns. Our results are robust to the FFC risk factors as well as other well-known stock return determinants. We further examine the underlying mechanisms for the greater effect of institutional order illiquidity on stock returns. We find that the preannouncement institutional buy (sell) lambda is positively (negatively) related to the announcement-period abnormal returns for unscheduled announcements. We also observe a sharp increase in the institutional sell lambda around index rebalancing period for the stocks that will be removed from the index. These findings imply that the underlying mechanisms of the significant institutional order illiquidity (i.e., price impact) is in fact driven by both information and liquidity effects. Overall, our findings support the significance of sell-order illiquidity in explaining the cross-sectional stock returns. More importantly, our study highlights the importance of incorporating trader identities in the analysis of the illiquidity-return relation. 26

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