Index Futures Trading and Spot Market Liquidity: Evidence from the Recent Chinese Stock Market Crisis

Qian Han Jufang Liang ∗†

Abstract Using a difference-in-difference approach, we find that restrictions placed on index futures trading during the recent Chinese stock market crisis deteriorated spot market liquidity. This is more so for the CSI 300 index component stocks and for the September trade restrictions announcement. Robustness checks further confirm our main results. Our findings are consistent with the contention of Banerjee and Graveline (2014) that during a market crash instituting bans or position limits on derivatives trading may increase the underlying assets scarcity.

Keywords: Index futures; spot liquidity; diﬀerence-in-diﬀerence

JEL Code: G13

1 Introduction

The eﬀect of index futures trading on spot market liquidity has long been debated and remains unresolved in the literature. According to informed trader theory (Gorton and Pennacchi (1993); Subrahmanyam (1991)), index futures markets provide market-wide risk exposure with a lower cost than spot markets, hence the introduction of a futures market may draw away uninformed traders with well-diversiﬁed portfolios from a spot market. This implies that, as a result, the proportion of informed traders in the spot market increases which causes market makers to face a higher adverse selection cost and reduces liquidity in the spot market after the introduction of index futures. On the other hand, Silber (1985) argues that index futures trading may induce a low cost for market makers to hedge their inventory risk, hence narrowing the spread in the spot market. Grossman and Miller (1988) and Grossman (1989) state that index arbitrage between the spot and futures markets may

∗Wang Yanan Institute for Studies in Economics (WISE), MOE Key Lab of Econometrics and Fujian Key Lab of Statistics, Xiamen University, Xiamen, 361005, Fujian, China. Qian Hans research is supported by the China Natural Science Foundation (CNSF project no. 714437). Standard disclaimers apply. †Corresponding author: Qian Han, Email: [email protected], Address: Room A402, Economics Build- ing, Xiamen University, Xiamen, 361005, Fujian, China.

1 improve spot market depth. Froot and Perold (1995) build a model to show that market depth can be increased through a more rapid dissemination of market-wide information due to the introduction of index futures. More recently, Banerjee and Graveline (2014) show theoretically that when an underlying asset becomes scarce, subsequent bans on derivatives trading can increase the underlying assets scarcity and further distort the spot price. Clearly, theories diverge on this important issue. Empirical evidence is also inconclusive. Bessembinder and Seguin (1992) examine the effect of index futures trading activities on spot market volatility and attribute the reduc- tion of volatility to increased liquidity from futures trading. In contrast, Jegadeesh and Subrahmanyam (1993) test the direct effect of index futures trading on average bid-ask spreads. They find a 3.7% increase of spread on the introduction of index futures trading. However, contrary to informed trading theory, they fail to find a significant increase in the adverse selection component of the spread. This article provides additional empirical evidence on this issue using the recent Chi- nese stock market crash as a natural experiment. Critics of the role of index futures in the crash argue that the index futures market serves as a venue of speculative (short selling) trading and that large profits are made through simultaneously dumping stocks in the spot market. From late August 2015, under intensified social pressure the China Financial Futures Exchange (CFFEX) adopted harsh restrictions on the scale of open positions and effectively terminated index futures trading in China. We see this event as a unique op- portunity to reexamine the effect of futures trading on spot market liquidity as previous literature mainly examines the issue by looking at the introduction, not the termination, of futures trading. In addition, the Chinese stock and futures markets are order-driven markets, and thus, do not have market makers as in many other markets such as the Unit- ed States. Theories based on the market maker mechanism may not apply to the Chinese markets. This study, therefore, supplements existing literature by examining the effect of futures trading on spot market liquidity in a different market setting. For our main analysis, we adopt a difference-in-difference (DID) approach to identify the futures trading impact on index component stocks compared with a matched sample of non-component stocks. This is the same approach as Harris (1989), Laatsch (1991), Kumar et al. (1995), and more recently Xie and Mo (2014). Using high frequency data, we construct daily series of the Amihud illiquidity measure to determine spot market liquidity. Statistical results show that, controlling for other liquidity-related variables, after the effective termination of the Chinese index futures trading, the index component stock liquidity is significantly reduced compared with non-component stock liquidity. The robustness analysis reaches the same conclusion, consistent with theoretical predictions that index futures trading increases spot market depth and improves liquidity. In particular, our results are consistent with the prediction of Banerjee and Graveline (2014) that restrictions on derivatives trading may increase the scarcity of the underlying asset in a situation, e.g., a market crash, where the underlying asset is already scarce. The rest of the article is organized as follows. Section 2 introduces the recent Chinese

2 stock market crisis and the regulatory measures adopted to curb index futures trading. Section 3 discusses the methodology, including the stock matching method of the DID approach. Data and sample statistics are also presented. Section 4 presents the major results and robustness checks. Possible explanations of our results are oﬀered. Section 5 concludes and discusses potential policy implications.

2 The Recent Chinese Stock Market Crash

The Chinese stock market has recently taken a rollercoaster ride, as shown in Figure 1. From the beginning of 2015 until Mid-June, the CSI 300 Index, which represents the broad Chinese stock market, rose from 3500 to a high of 5178. Then it collapsed, losing over 34% in twenty days, with 1000 points erased in one week alone. On July 5, the Chinese government adopted a series of supportive measures only to see the market drop another 1000 points in the third week of August. Attempts were made to restrict high frequency program trading and to investigate abnormal individual trading account, but failed. During the crisis, almost half of the listed stocks lost more than 50% of their pre-crash market value, with the biggest loss more than 77%. From mid-June to mid-September, on average, one day in every four trading days saw more than one thousand stocks (about one third of the total number of stocks traded in the market) lose 10%, i.e., hit the lower bound of the daily price fluctuations. By any means, this crash is among the most dramatic stock market crises in history. [Insert Figure 1] Under heavy pressure from both regulators and the public, the CFFEX announced on August 25 that from August 26, three measures would be adopted to curb speculative trading in the index futures market1. First, the initial margin for non-hedging trades would be raised from first 10% to 12%, then 15% and finally 20% over the following three days. Second, any single day’s total opening position greater than 600 contracts would be considered as abnormal trade and be subject to increased scrutiny. Third, the clearing fees for intraday trades would be adjusted upward to 1.15 basis points. With the crisis developing further and the effect of government bail out measures quickly diminishing, September 2 saw the CFFEX announce yet another round of measures to curb speculative trade in the futures market. First, from September 7, any single day non- hedging trading of over 10 contracts would be considered abnormal. Second, the initial margins for all non-hedging trading would be raised from 30% to 40%, and the initial margins for hedging trades would be raised from 10% to 20%. Third, the clearing fees for intraday trades would be adjusted from 1.15 to 23 basis points. With these two rounds

1China launched its CSI 300 futures index on April 16, 2010, and then introduced the CSI 500 and Shanghai A50 futures index ﬁve years later. Guo et al. (2013) show that the CSI 300 index futures has performed well in terms of its price discovery function relative to the spot market and the Singapore A50 futures market.

3 of measures, index futures trading in China nearly came to a complete stop. As stated in Section 1, this provides a unique setting to examine the impact of index futures trading in an order-driven spot market.

3 Data and the Stock Matching Procedure

Our goal is to compare the liquidity change of individual stocks within the index (component stocks) against that of a control sample of stocks that are not in the index (non- component stocks) before and after the two rounds of CFFEX announcements. Since the CSI 500 and Shanghai 50 index futures were just launched on April 16, 2015, we omit the first two weeks of trade to avoid possible initial trading inefficiencies. Thus, our whole sample spans 05/01-09/30. We then divide the sample period into three sub-periods based on the CFFEX policy announcement dates: 05/01-08/25, 08/26-09/06, and 09/07-09/30. Because almost all component stocks of the Shanghai 50 index are also included in the CSI 300 index and the two indices are highly correlated, we believe that the termination of trading of either the CSI 300 index or the Shanghai 50 index futures would generate a similar impact on spot market liquidity, hence for brevity we do not report the effect of the Shanghai 50 index futures trading separately. Since all sub-periods are rather short, we use intraday data for the empirical analysis. The 1-minute and 5-minute stock data and all firm information are from CSMAR, a leading financial data provider in China. Stock index adjustment information is from WIND, another major Chinese financial data provider. We apply the following filters to the raw data: delete all financial stocks, delete all stocks with at least five consecutive non-trading days, delete all stocks under special treatment, and delete all stocks lacking data required for the regressions following. Finally, any stocks added to or deleted from the three indices during the sample period are not included. After filtering we are left with 1155 stocks in the full sample. We use the Amihud illiquidity ratio (Amihud (2002)), denoted by Amihud, to measure daily stock liquidity as it is a widely accepted measurement of the trading volume needed to move the stock price2. Another liquidity measure, T urnover, is used for robustness check. Suppose that prices pi,t,0, pi,t,1, pi,t,2 ··· pi,t,n are n + 1 observed intraday log prices of stock i at date t. Define the intraday return as ri,t,j = pi,t,j − pi,t,j−1. The Amihud illiquidity ratio is given by

1 n |ri,t,j| Amihudi,t = Σj=1 n V olumei,t,j

2See, for example, Goyenko et al. (2009) for stock liquidity and Marshall et al. (2011) for commodity liquidity. In both studies, Amihud liquidity is shown to perform better than other measures in estimating the price impact of trades.

4 where V olumei,t,j is the traded RMB volume of stock i from time j − 1 to j at date t . The Turnover of ﬁrm i at date t is calculated as

TSi,t T urnoveri,t = CSi,t where TSi,t is the number of traded shares of stock i at date t and CSi,t is the number of circulating shares of ﬁrm i at date t3. Realized volatility (RV ), ﬁrm size (F size), and the share proportion of the top ten shareholders (T op10), are used as control variables since previous literature has shown that these variables are highly correlated with stock liquidity. Following Andersen and Bollerslev (2003), we use 1-minute and 5-minute intraday log returns of stock i to calculate the realized volatility of stock i at date t by the formula

q n 2 RVi,t = Σj=1ri,t,j.

For 1 minute data, n = 240 and for 5 minute data, n = 48. We then use stock information in the ﬁrst sub-period to match stocks. Our matching method follows Xie and Mo (2014). This matching procedure guarantees that each component stock is matched with one non-component stock. Note that because the matching is implemented independently for the CSI 300 index and the CSI 500 index, it is possible that some non-component stocks are matched with both a CSI 300 component stock and a CSI 500 component stock. Speciﬁcally, the matching uses the following steps. First, we obtain estimatorα ˆ by the regression below:

Yi = α0 + α · Xi + i

1 T 1 T 1 T where Yi = T Σt=1log(Amihudi,t), Xi,1 = T Σt=1RVi,t, Xi,2 = T Σt=1log(F sizei,t), Xi,3 = T op10i and time t = 1 ··· T corresponds to each trading day. We take natural logarithms of the Amihud illiquidity ratio and the ﬁrm size to make these two variables commensurate with the other variables in the regression. 0 0 We next calculate Distancei,j = (Xi − Xj) αˆ αˆ(Xi − Xj) for i 6= j where stock i is a component stock of either the CSI 300 or the CSI 500 index, and stock j is a non- component stock excluded from both indices. We restrict stock pairs (i, j) to being in the same industry as speciﬁed in the 2012 Chinese Securities Regulatory Commission (CSRC) class one security industry code (SIC). Matched pairs are then constructed by minimizing Distancei,j. This procedure is repeated for both the CSI 300 index and the CSI 500 index until all component stocks are matched. After matching is complete, we are left with 161 pairs for the CSI 300 index and 262 pairs for the CSI 500 index. Table 1 presents the summary statistics of our matched and

3The Chinese stock market has some non-circulating stocks that are held by large shareholders and are non-tradable at the secondary market.

5 unmatched samples during the sample period. Consistent with the notion that the CSI 300 index represents the largest firms in the Chinese A-share market, the CSI 300 component stocks tend to be more liquid, less volatile, and have more concentrated share holdings than either the non-CSI 300 component stocks or the CSI 500 component stocks. However, matched stocks for both indices exhibit roughly similar characteristics. [Insert Table 1] Figure 2 illustrates the time series of the log Amihud illiquidity ratio for component and non-component stocks for both indices. Two observations can be made: first, the CSI component stocks are more liquid than non-component stocks, both before and after the market crash, consistent with component stocks being preferred assets for market investors, presumably index arbitragers and stock fund managers; and second, after the CFFEX adopted trade restriction regulations, the liquidity spread between component and non-component stocks narrows, suggesting that trade regulations may hurt spot liquidity. In Section 4, we conduct rigorous tests to see whether this observation is statistically significant. [Insert Figure 2]

4 Empirical Results

4.1 The Dummy Variable Approach Before we carry out the DID analysis, we ﬁrst adopt a dummy variable approach as a preliminary test of the impact of futures trading termination on aggregated stock market liquidity. The following regression is run for the whole sample for each event:

log(Amihudi,t) = β0 + β1T rendi,t + β2RVi,t + β3log(F sizei,t) + β4T op10i + β5Dt + i,t where dummy variable Dt = 1 if t >= Event date, otherwise Dt = 0. Note that in addition to the control variables used in the matching process, we also include the variable Trend to account for the broad stock market illiquidity increasing during the crisis. So T rendi,t = 1, 2, 3 ··· N if t = Jun.15, Jun.16, Jun.17 ··· Sep.30.. Table 2 reports the regression results. The values in brackets are the t statistics calculated by using robust standard errors clustered at the stock level for testing H0 : |βi| = 0 (H1 : |βi| 6= 0) and *, ** and *** denote significance at 10%, 5% and 1%, respectively. The volatility variable, RV, is positively correlated with market illiquidity, consistent with most of the empirical studies in the literature. Log(F size) is negatively associated with the Amihud illiquidity measure, suggesting that, on average, larger firms are more liquid than smaller firms. The coefficient of shareholder concentration, T op10, is significant and positive, consistent with the common sense. Our key variables, the event dummies, are significant and positive for both events, suggesting that trade restrictions reduce spot market liquidity. [Insert Table 2]

6 To check the robustness of our results, we also test the sub-periods 06/15-09/30 and 07/26-09/30. The first sub-period covers only the crisis period while the second sub-period covers an equal length of time both before and after the events. Both tests generate consistent results with those of the whole sample and are hence omitted for brevity purposes, but they are available upon request. Overall, our dummy variable analysis suggests a dampen- ing effect on market liquidity when Chinese index futures trading is effectively terminated. This is in line with stories of index arbitrage (Grossman and Miller (1988)), information dissemination (Froot and Perold (1995)) and scarcity theory(Banerjee and Graveline (2014)), but inconsistent with informed trading theory (Subrahmanyam (1991)). These results are independent of the market trend.

4.2 The Difference-in-Difference Analysis The above dummy variable approach may be questionable as factors other than the three control variables considered can affect market liquidity during a crisis and they may not be considered in the model. Therefore, a standard DID analysis is applied to the matched samples to identify the net effect of futures trading termination on the spot liquidity. Specifically, for each event, we run the following DID regressions for the CSI 300 and CSI 500 indices: log(Amihudi,t) = β0+β1T rendi,t+β2RVi,t+β3log(F sizei,t)+β4T op10i+β5Di+β7Di×Dt+i,t where Di = 1 if i is a component stock of CSI 300, Di = 0 otherwise. We say that Dt = 1, if t >= Event date, Dt = 0 otherwise. The coefficient β7 captures the net effect of index futures trading termination on spot liquidity. Table 3 and Table 4 report the regression results for the CSI 300 index and the CSI 500 index cases, respectively. In all regressions, the coefficients of the control variables have the same signs as those in the dummy variable approach and are consistent with the literature. The coefficients of Di are significantly negative, consistent with Figure 2 and indicating that index component stocks are, in general, more liquid than non-component stocks. The coefficients of Dt are positive at the 1% level, suggesting that after each event market liquidity gets worse. This is consistent with the findings in the dummy variable analysis. The coefficients of DID variable, Di × Dt, are positive and significant at the 5% level. This is a strong indication that futures trading restrictions reduce the liquidity of component stocks more than that of non-component stocks. More interestingly, a comparison of the DID coefficients across the two events suggests that, in the CSI 300 index case, the second round of trading restrictions has a greater liquidity effect than the first. This makes sense as the September round limits daily non-hedging opening positions to a mere 10 contracts, compared to the initial restriction to 600 contracts, and dramatically increases the initial margin and clearing fees. Hence, we interpret the stronger effect of the second event as further evidence that futures trading restrictions lower spot liquidity. In addition, the DID coefficients in the CSI 300 index case are all larger than those in the CSI 500 index case,

7 suggesting that the liquidity eﬀect of futures trading termination is greater for the more matured CSI 300 index futures. [Insert Table 3] [Insert Table 4]

4.3 Robustness Checks We now perform a set of robustness checks to test the reliability of our findings. We first change the data frequency from 1 minute to 5 minutes and report the DID results for the CSI 300 index in Table 5. It is clear that our main results remain qualitatively the same. In particular, the liquidity effect of futures trading restrictions for the second event is still stronger than for the first event. We do not report the test results for the CSI 500 index due to space considerations. A dummy variable approach using 5-minute data is also performed. Our conclusions remain the same. [Insert Table 5] Next, to check the sensitivity of our results to different sample periods, we conduct DID tests for the same sub-periods of 06/15-09/30 and 07/26-09/30 as for the dummy variable robustness checks. Results for the CSI 300 index are reported in Table 6. Although, when compared with Table 3, the liquidity effects of futures trading restrictions are weaker in magnitude, they are still statistically significant at the 5% level. Tests for the CSI 500 index, not presented here, show insignificant results for both events, possibly because the CSI 500 index futures has been in existence for only several months and the market is far from mature4. In fact, in all the above tests, the liquidity effect of futures trading restrictions is weaker for the CSI 500 index than it is for the CSI 300 index. This can be explained by the relative maturity of the CSI 300 index as its futures contracts are the more actively traded. [Insert Table 6] Then, we repeat all tests using stock turnover as an alternative measure of stock liquidity. Results are reported in Table 7. A comparison with previous results using the Amihud illiquidity measure provides several interesting findings. The signs of shareholder concentration are consistent across different stock liquidity measures; firm size is now significantly positive for the CSI 500 index, but insignificant for the CSI 300 index; volatility has the opposite effect to that when using Amihud illiquidity. These differences are probably due to the Amihud and Turnover measures assessing different aspects of stock liquidity. Despite these, our main interest, the DID coefficient, is still significantly negative for both indices and both events. Again, for the CSI 300 index, the second event has a greater negative liquidity impact than the first event. [Insert Table 7]

4Yang et al. (2012) find that in the first few months after its inception the CSI 300 index futures is also not efficient.

8 Finally, we control for the time fixed effect to avoid possible biases resulted from market trends in estimating the treatment effect. Specifically, we conduct a time fixed effect regression using a dummy variable for each date in the sample period. Table 8 reports the results. Consistent with Table 3, for the CSI 300 index, the coefficient of the treatment effect is more significant for the September event than for the August event. But for the CSI 500 index, the coefficients of the treatment effect are insignificant for both events. [Insert Table 8] In summary, the robustness checks further confirm our findings that the termination of index futures trading by the CFFEX during the recent market crash is closely associated with a worsening stock market liquidity.

4.4 Discussions Why do restrictions on index futures trading deteriorate liquidity in the spot market? Our results seem to be in line with Grossman and Miller (1988) and Grossman (1989) who argue that the index arbitrage trading between spot and futures market improve liquidity of spot markets. Restrictions of index futures trading make index arbitrage infeasible, thus reducing the spot liquidity. However, a careful thought would suggest that index arbitrage trading cannot be the main reason for our findings because since July 2015, over one month before the events we considered, stock short selling has effectively been frozen. With the futures basis kept at a negative level throughout the crisis, this implies that index arbitrage trading, which requires shorting stocks, has already become very thin before the CFFEX announced the two rounds of trade restrictions. Hence the liquidity impact difference between component stocks and non-component stocks cannot be attributed to the index arbitrage story. Instead, we are inclined to support Banerjee and Graveline (2014)’s theoretical prediction that bans of derivatives trading would shut the door of short-selling and make the underlying assets scarcer than otherwise in a market downturn. Chen et al. (1995) point out that stock (representing a stock index) has the same basic final payoff as index futures, however, a stock position may be tailored by a particular investor, generating additional customization value. Indeed, our conversation with market professionals confirms that, before the market crash, many institutional investors adopt the alpha strategy, i.e. hold a systematic-risk-neutral portfolio that long stock portfolios and short index futures to profit from the excess return of stock portfolios relative to the index. Because the index component stocks are more liquid and more correlated with the index futures than non- component stocks, the stock portfolios in the alpha strategy are mainly composed of index component stocks. When a market crashes, those alpha strategy investors should be neutral from the crash risk if there is no restriction on the futures trading. But if the index futures trading is terminated as in the Chinese case, these alpha portfolios are suddenly exposed to huge systematic risk. Investors would have to liquidate their portfolios, which increases the

9 selling pressure in the spot market. Before the futures trading regulations, investors can partly substitute their stock selling by shorting index futures. However, this becomes unlikely due to the futures trading bans, so the spot liquidity can only get worse and worse when investors keep dumping their stocks and the market spirals down.

5 Conclusions

We use the recent Chinese stock market crash and the subsequent index futures trading restrictions as a natural experiment to examine the effect of index futures trading on spot market liquidity. A difference-in-difference approach is adopted to identify the net effect. We find that after the Chinese futures exchange effectively terminates index futures trading, the spot market liquidity is significantly reduced. That is, the index component stocks liquidity is affected more by the termination of futures trading than are non-component stocks. We also find that the impact is stronger for the CSI 300 index, and the second round of trade restrictions has a greater impact on spot liquidity than the first round. Our results are inconsistent with the informed trading theory, and cannot be explained by the index arbitrage story. Our findings are more aligned with with the model predictions of Banerjee and Graveline (2014) that derivatives trading restrictions can increase the scarcity of the underlying asset. Our results may have some important policy implications. Since the market crash, Chinese regulators and the public have cast serious doubts on the functioning of index futures. So far there has been no sign that index futures trading will be resumed any time soon. Our findings show that restrictions on futures trading only deteriorate stock liquidity in an already illiquid market during a crash. This increased illiquidity in the spot market may push spot prices even lower. Hence, we do not support the futures trading restrictive measures adopted during the crisis. In addition, after the CFFEX trade restriction regulations, the trading volume of Sin- gapore A50 index futures soared as international investors rushed to find an alternative to hedge their risk exposure to the Chinese A-share market. On October 12, 2015, the Chicago Mercantile Exchange (CME) launched its FT China 50 index futures. It is plausi- ble that if Chinese regulators keep shutting down futures trading, then the CSI 300 index futures market share will quickly shrink and the CFFEX may lose ground to its overseas competitors.

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12 6 Appendix

Figure 1: The Recent Chinese Stock Market Crash

This ﬁgure shows the time series of the CSI 300 index from April 16, 2015 to September 30, 2015. The two greyed regions correspond to the two event windows as deﬁned in the main text: Aug. 26, 2015 to Sep. 06, 2015 and Sep. 07, 2015 to Sep. 30, 2015.

13 Figure 2: Time Series of Matched Stocks Liquidity

This ﬁgure shows the time series of the matched stocks liquidity for both the CSI 300 and CSI 500 indices. The shaded area is from Jun. 15, 2015 to Sep. 30, 2015.

14 Table 1: Data Summary Statistics

Ln(Amihud) RV Ln(F size) Top 10 Number of Stocks Mean Std Mean Std Mean Std Mean Std All Stocks 1155 -15.46 1.05 4.99 0.78 16.37 1.04 43.96 21.66 CSI 300 Stocks 161 -16.58 0.77 4.53 0.75 17.80 0.72 52.82 24.30 CSI 300 Matched Stocks 161 -15.39 0.78 5.42 0.65 6.44 0.47 37.28 23.76 CSI 500 Stocks 262 -15.51 0.70 5.12 0.65 16.46 0.41 43.08 20.21 CSI 500 Matched Stocks 262 -15.26 0.74 5.41 0.66 16.19 0.50 36.95 22.39

This table presents the daily summary statistics for the whole sample period from May 01, 2015 to Sep. 30, 2015. Log(Amihud) and RV are computed using intraday 5 minute stock price data. Log(Fsize) is the logarithm of an individual stocks market value. Top10 is the proportion of shares held by the top ten shareholders of each stock. All data are from CSMAR.

Table 2: Dummy Variable Regression Event Date Aug. 26 Sep. 07 Aug. 26 Sep. 07 -3.2079*** -3.2390*** -3.5073*** -3.6120*** Intercept (-9.80) (-9.77) (-10.67) (-10.96) 0.0093*** 0.0117*** T rend t (21.10) (27.47) 0.0867*** 0.1113*** 0.0807*** 0.0920*** RV i,t (26.05) (31.94) (24.20) (26.25) -0.8017*** -0.8050*** -0.7906*** -0.7888*** Log(F size ) i,t (-38.92) (-38.73) (-38.41) (-38.26) 0.0052*** 0.0054*** 0.0050*** 0.0051*** T op10 i (4.54) (4.68) (4.45) (4.49) 1.1331*** 1.1868*** 0.6971*** 0.6229*** D t (58.54) (57.06) (40.07) (35.12) Adj. R2 0.5265 0.5148 0.5357 0.5328

This table reports the estimation results for the regression:

log(Amihudi,t) = β0 + β1T rendi,t + β2RVi,t + β3log(F sizei,t) + β4T op10i + β5Dt + i,t where T rendi,t = 1, 2, 3 ··· N if t=Jun. 15, Jun. 16, Jun. 17··· Sep. 30; Dt = 1 if t >= Event date, Dt = 0 otherwise; Data is from May 01, 2015 to Sep. 30, 2015. The values in brackets are the t statistics calculated by using robust standard errors clustered at the stock level for testing H0 : |βi| = 0 (H1 : |βi|= 6 0) for i ∈ {0, 1, ··· , 5} and *, ** and *** denote signiﬁcance at 10%, 5% and 1%, respectively.

15 Table 3: Diﬀerence-in-Diﬀerence Regression For the CSI 300 Index Using 1-minute Data Event Date Aug. 26 Sep. 07 Aug. 26 Sep. 07 -5.3001*** -5.0408*** -6.3209*** -6.3586*** Intercept (-3.95) (-3.65) (-4.88) (-4.88) 0.0171*** 0.0182*** T rend t (15.76) (17.49) 0.1028*** 0.1305*** 0.0947*** 0.1059*** RV i,t (8.85) (10.99) (8.22) (8.94) -0.6735*** -0.6949*** -0.6241*** -0.6256*** Log(F size ) i,t (-8.08) (-8.11) (-7.76) (-7.73) 0.0067** 0.0069** 0.0064** 0.0064** T op10 i (2.31) (2.35) (2.22) (2.23) -0.3569** -0.3029* -0.4434*** -0.4287*** D i (-2.21) (-1.84) (-2.81) (-2.69) 1.1944*** 1.2601*** 0.3894*** 0.3869*** D t (11.22) (10.83) (4.19) (3.75) 0.2533** 0.3067** 0.2575** 0.3069** D × D i t (2.31) (2.55) (2.36) (2.60) Adj. R2 0.5434 0.5298 0.5799 0.5815

This table shows the estimation results for the DID analysis using the sample period of May, 01, 2015 - Sep. 30, 2015. We run the following regression:

log(Amihudi,t) = β0 + β1T rendi,t + β2RVi,t + β3log(F sizei,t) + β4T op10i + β5Di + β7Di × Dt + i,t where T rendi,t = 1, 2, 3 ··· N if t=Jun. 15, Jun. 16, Jun. 17··· Sep. 30; Di = 1 if i is a component stock of the CSI 300 index, Di = 0 otherwise; Dt = 1, if t >= Event date, Dt = 0 otherwise. The values in the brackets are t statistics calculated by using robust standard errors clustered at the stock level for testing H0 : |βi| = 0 (H1 : |βi| 6= 0) for i ∈ {0, 1, ··· , 7}. *, ** and *** denote signiﬁcance at 10%, 5% and 1% respectively.

16 Table 4: Diﬀerence-in-Diﬀerence Regression For the CSI 500 Index Using 1-minute Data Event Date Aug. 26 Sep. 07 Aug. 26 Sep. 07 0.7060 -2.3428* -1.1409 -4.1300*** Intercept (0.46) (-1.96) (-0.70) (-3.49) 0.0127*** 0.0123*** T rend t (11.99) (15.02) 0.0913*** 0.1074*** 0.0805*** 0.0849*** RV i,t (10.42) (20.40) (9.13) (16.29) -1.0448*** -0.8558*** -0.9396*** -0.7516*** Log(F size ) i,t (-11.04) (-11.60) (-9.48) (-10.36) 0.0090*** 0.0046*** 0.0087*** 0.0043*** T op10 i (3.52) (2.82) (3.49) (2.68) -0.1183 -0.0378 -0.1446 -0.0744 D i (-1.12) (-0.60) (-1.40) (-1.02) 0.9867*** 1.0516*** 0.4239*** 0.4875*** D t (9.87) (19.63) (5.01) (12.02) 0.2378** 0.1308** 0.2406** 0.1365** D × D i t (2.27) (2.31) (2.31) (2.44) Adj. R2 0.4210 0.3245 0.4417 0.3494

This table shows the estimation results for the DID analysis using the sample period of May 01, 2015 - Sep. 30, 2015. We run the following regression:

log(Amihudi,t) = β0 + β1T rendi,t + β2RVi,t + β3log(F sizei,t) + β4T op10i + β5Di + β7Di × Dt + i,t where T rendi,t = 1, 2, 3 ··· if t=Jun. 15, Jun. 16, Jun. 17··· Sep. 30; Di = 1 if i is a component stock of the CSI 500 index, Di = 0 otherwise; Dt = 1, if t >= Event date, Dt = 0 otherwise. The values in the brackets are t statistics calculated by using robust standard errors clustered at the stock level for testing H0 : |βi| = 0 (H1 : |βi| 6= 0) for i ∈ {0, 1, ··· , 7}. *, ** and *** denote signiﬁcance at 10%, 5% and 1% respectively.

17 Table 5: Diﬀerence-in-Diﬀerence Regression For the CSI 300 Index Using 5-minute Data Event Date Aug. 26 Sep. 07 Aug. 26 Sep. 07 -6.8750*** -6.4468*** -7.5764*** -7.5617*** Intercept (-6.03) (-5.58) (-6.62) ( -6.58) 0.0095*** 0.0116*** T rend t (11.37) ( 13.84) 0.0798*** 0.0929*** 0.0805*** 0.0867*** RV i,t (15.98) (17.67) (15.93) (16.78) -0.6317*** -0.6598*** -0.5984*** -0.6025*** Log(F size ) i,t (9.06) (-9.35) (-8.61) ( -8.64) 0.0030 0.0032* 0.0029 0.0029 T op10 i (1.61) (1.69) (1.54) (1.56) -0.2484** -0.1920* -0.29178*** -0.2732** D i (-2.19) (-1.66) (-2.59) ( -2.41) 0.8379*** 0.8466*** 0.4010*** 0.3053*** D t (14.92) (15.25) (8.96) (7.63) 0.2722*** 0.3214*** 0.2725*** 0.3210*** D × D i t (5.00) (5.87) (5.01) (5.90) Adj. R2 0.4663 0.4515 0.4797 0.4770

This table shows the estimation results for the DID analysis using the sample period of May 01, 2015 - Sep. 30, 2015. We run the following regression:

log(Amihudi,t) = β0 + β1T rendi,t + β2RVi,t + β3log(F sizei,t) + β4T op10i + β5Di + β7Di × Dt + i,t where T rend(i, t) = 1, 2, 3 ··· N if t=Jun. 15, Jun. 16, Jun. 17 ··· Sep. 30; Di = 1 if i is a component stock of the CSI 300 index, Di = 0 otherwise; Dt = 1, if t >= Event date, Dt = 0 otherwise. The values in the brackets are t statistics calculated by using robust standard errors clustered at the stock level for testing H0 : |βi| = 0 (H1 : |βi|= 6 0) for i ∈ {0, 1, ··· , 7}. *, ** and *** denote signiﬁcance at 10%, 5% and 1% respectively.

18 Table 6: Diﬀerence-in-Diﬀerence Regression For the CSI 300 Index Using Two Sub-periods Jun. 15 - Sep. 30 Jul. 26 - Sep. 30 Event Date Aug. 26 Sep. 07 Aug. 26 Sep. 07 -7.2200*** -7.3162*** -5.6910*** -6.0893*** Intercept (-5.99) (-6.02) (-4.35) (-4.53) 0.0123*** 0.0156*** 0.0043** 0.0146*** T rend t (11.46) (15.62) (2.04) (11.22) 0.0812*** 0.0993*** 0.0119 0.0523*** RV i,t (9.33) (11.45) (0.88) (4.06) -0.5597*** -0.5643*** -0.6088*** -0.6197*** Log(F size ) i,t (-7.70) (-7.71) (-7.63) (-7.65) 0.0029 0.0030 0.0040* 0.0042* T op10 i (1.34) (1.37) (1.74) (1.83) -0.3659*** -0.3390*** -0.3486** -0.2972** D i (-2.90) (-2.65) (-2.41) (-2.03) 0.5660*** 0.4717*** 0.6917*** 0.3936*** D t (11.13) (10.01) (10.87) (8.74) 0.1822*** 0.2393*** 0.1191** 0.1966*** D × D i t (2.95) (3.74) (2.17) (3.55) Adj.R2 0.4268 0.4255 0.3955 0.3876

This table shows the estimation results for the DID analysis using the sample sub-periods of Jun. 15, 2015 - Sep. 30, 2015 and Jul. 26, 2015 - Sep. 30, 2015. We run the following regression:

log(Amihudi,t) = β0 + β1T rendi,t + β2RVi,t + β3log(F sizei,t) + β4T op10i + β5Di + β7Di × Dt + i,t where T rendi,t = 1, 2, 3 ··· N if t=Jun. 15, Jun. 16, Jun. 17 ··· Sep. 30 for the sub-periods of Jun. 15 - Sep. 30 and t=Jul. 26, Jul. 27, Jul. 28 ··· Sep. 30 for the sub-periods of Jul. 26 - Sep. 30; Di = 1 if i is a component stock of the CSI 300 index, Di = 0 otherwise; Dt = 1, if t >= Event date, Dt = 0 otherwise. The values in the brackets are t statistics calculated by using robust standard errors clustered at the stock level for testing H0 : |βi| = 0 (H1 : |βi|= 6 0) for i ∈ {0, 1, ··· , 7}. *, ** and *** denote signiﬁcance at 10%, 5% and 1% respectively.

19 Table 7: Diﬀerence-in-Diﬀerence Regression Using Turnover CSI 300 CSI 500 Event Date Aug. 26 Sep. 07 Aug. 26 Sep. 07 6.7585*** 6.7672*** 2.1561 2.0208 Intercept (4.53) (4.53) (1.03) (0.97) -0.0198*** -0.0235*** -0.0143*** -0.0225*** T rend t (-9.54) (-12.44) (-6.70) (-11.09) 0.3785*** 0.3744*** 0.4590*** 0.4581*** RV i,t (18.37) (17.81) (25.40) (24.72) -0.1023 -0.0996 0.2317* 0.2463* Log(F size ) i,t (-1.03) (-1.01) (1.76) (1.87) -0.0466*** -0.0466*** -0.0618*** -0.0619*** T op10 i (-9.46) (-9.46) (-14.66) (-14.66) -0.4968** -0.5067** -0.8750*** -0.9147*** D i (-2.18) (-2.22) (-5.42) (-5.71) -0.3436*** -0.0591 -0.5774*** -0.0035 D t (-2.70) (-0.51) (-4.39) (-0.03) -0.4424*** -0.5711*** -0.4658*** -0.4444*** D × D i t (-3.08) (-3.99) (-3.24) (-3.04) Adj. R2 0.3425 0.3410 0.2964 0.2922

This table shows the estimation results for the regression

T urnoveri,t = β0 + β1T rendi,t + β2RVi,t + β3log(F sizei,t) + β4T op10i + β5Di + β7Di × Dt + i,t where T rend(i, t) = 1, 2, 3 ··· N if t=Jun. 15, Jun. 16, Jun. 17 ··· Sep. 30; Di = 1 if i is a component stock of the CSI 300 or CSI 500 index; Di = 0 otherwise; Dt = 1, if t >= Event date, Dt = 0 otherwise. Data is from May 01, 2015 to Sep. 30, 2015. The values in the brackets are t statistics calculated by using robust standard errors clustered at the stock level for testing H0 : |βi| = 0 (H1 : |βi| 6= 0) for i ∈ {0, 1, ··· , 7}. *, ** and *** denote signiﬁcance at 10%, 5% and 1% respectively.

20 Table 8: Difference-in-Difference Regression with Time Fixed Effects CSI 300 CSI 500 Event Date Aug. 26 Sep. 07 Aug. 26 Sep. 07 Intercept -6.3020*** -6.2970*** -0.6015 -0.6112 (-4.66) (-4.66) (-0.37) (-0.38) RVi,t 0.0755*** 0.0750*** 0.0106 0.0107 (-2.57) (-2.55) (-0.49) (-0.50) Log(F sizei,t) -0.6232*** -0.6234*** -0.9476*** -0.9476*** (-7.35) (-7.35) (-9.31) (-9.31) T op10i 0.0069** 0.0069** 0.0081*** 0.0081*** (-2.20) (-2.20) (-3.29) (-3.29) Di 2.2398*** 2.1860*** 1.8346*** 1.8632*** (-15.51) (-14.81) (-15.51) (-15.37) Dt -0.4511*** -0.4517*** -0.1143 -0.1013 (-2.70) (-2.69) (-1.06) (-0.93) Di × Dt 0.1935** 0.2614*** 0.1217 0.0834 (-1.95) (-2.49) (-1.25) (-0.79) Adj. R2 0.665 0.665 0.607 0.607

This table shows the estimation results for the regression log(Amihudi,t) = β0 + Σtφt × Tt + β1RVi,t + β2log(F sizei,t) + β3T op10i + β4Di + β5Dt + β6Di × Dt + i,t where Tt is each date in the sample period except the last trading day of September 30th; Di = 1 if i is a component stock of the CSI 300 or CSI 500 index, Di = 0 otherwise; Dt = 1, if t >= Event date, Dt = 0 otherwise. Data is from May 01, 2015 to Sep. 30, 2015. The values in the brackets are t statistics calculated by using robust standard errors clustered at the stock level for testing H0 : |βi| = 0 (H1 : |βi|= 6 0) for i ∈ {0, 1, ··· , 6}. *, ** and *** denote signiﬁcance at 10%, 5% and 1% respectively.