# Which trades move prices in emerging markets?: Evidence from China's stock market

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5 B.M. Cai et al. / Pacific-Basin Finance Journal 14 (2006) those of 10,000 shares or greater. Moreover, the following 23 subcategories 1 are also used to provide more detailed analysis. The method of calculating the cumulative stock price return follows Barclay and Warner (1993) and Chakravarty (2001). A price change that occurs on a given trade is defined as the difference between the trade's price and the price of the previous transaction. For each of the 566 stocks, all price changes that occur on trades in a given trade size category over the sample period are summed. The sum is then divided by the cumulative price changes for the stock over the sample. Finally, the weighted cross-sectional mean of the cumulative stock price change (over the 566 stocks) is estimated, where the weights are the absolute value of the cumulative price change in each stock over the sample. More specifically, the following equations apply. Price Change PC i ¼ P i P i 1 ð1þ where P i is the transaction price. Percentage of cumulative price change for each size category: PcntCPC j ¼ X n PC j;i i¼1 P T P 0 ð2þ where P 0 and P T are the transaction price at the beginning and the end of the sample period. PC i are the n price change of trades in the jth size category. Weighted percentage of cumulative price change for each size category of each stock: CumPC k; j ¼ PcntCPC k; j jp k;t P k;0 j P556 k¼1 jp k;t P k;0 j ð3þ where PcntCPC k, j is the percentage cumulative price change for trade size j of stock k; P k,0 and P k,t are the transaction price at the beginning and the end of the sample period for stock k Weighted least squares regression In order to test the above hypotheses we use Barclay and Warner's (1993) weighted least squares regression. Following Barclay and Warner (1993, p. 293), the absolute values of the cumulative price changes over the period are used as the weights in estimating the cross-sectional mean. The dependent variable in the current regression is the cumulated price change of all trades in a given trade-size category divided by the cumulative price change of all trades during the period. The magnitudes of the denominator affect the level of this variable's sensitivity, especially for stocks with small cumulative price change. The weighting procedure can significantly reduce heteroskedasticity in the dependent variable , , , , , , , , , , , , , , , , , , , 10,000 14,999, 15,000 19,999, 20,000 24,999, and 25,000 and over.

7 B.M. Cai et al. / Pacific-Basin Finance Journal 14 (2006) Table 2 Analysis of whole sample CumPC Num PcntNum P/N Vol PcntVol P/V Small Medium , Large ,000 14, , ,000 24, , Sum For the cross-section of 566 stocks, this table reports the mean percentage of the cumulative stock price changes (CumPC), the corresponding number (Num), the percentages of trades (PcntNum), the ratio of percentage cumulative stock price change over the percentage of number of trades (P/N), the volume (Vol), the percentages of volume (PcntVol), and the ratio of percentage cumulative stock price change over the percentage of volume (P/V ). price manipulation hypotheses. There appears to be little support for the public information hypothesis that the percentage of the cumulative price change occurring in a given trade-size category is directly proportional to the percentage of transactions in that category. Table 3 reports the weighted-least-squares regressions of the percentage of the cumulative price change occurring in each trade-size category and subcategory on dummy variables for the trade-size categories and subcategories, the percentage of transactions (Regression 1), and the percentage of volume (Regression 2) occurring in that category. The weights are equal to the absolute cumulative price change over the sample period. Under the public information hypothesis, the coefficient on each dummy variable in the regression should be zero, and the coefficient on the percentage of transactions in the trade-size category should equal one and for volume it should NOT equal one. If the public information hypothesis holds, the percentage of the cumulative price change occurring in a given trade-size category is directly proportional to the percentage of transactions in that category, thus the coefficient on the percentage of transactions in the trade-size category should be one. Clearly, if this condition is to hold, the coefficient on the percentage of volume should NOT equal one.

8 460 B.M. Cai et al. / Pacific-Basin Finance Journal 14 (2006) Table 3 Regression results of whole sample A. Regression 1 B. Regression 2 Variable Estimate t value Estimate t value DummyS *** *** DummyM *** *** DummyL *** *** PcntNum ** PcntVol *** adjrsq Wald tests Wald tests Label Statistics ProbChiSq Statistics ProbChiSq S=0, M=0, L= *** *** PcntNum= *** PcntVol= S=M=L *** *** M=L S=L *** *** S=M *** *** C. Regression 3 D. Regression 4 Variable Estimate t value Estimate t value Dummy Dummy *** *** Dummy *** *** Dummy *** *** Dummy ** ** Dummy *** *** Dummy Dummy Dummy Dummy Dummy ** Dummy *** *** Dummy *** *** Dummy *** *** Dummy *** *** Dummy ** Dummy *** * Dummy * Dummy Dummy *** ** Dummy ** Dummy ** Dummy *** PcntNum *** PcntVol *** adjrsq Table 3 reports the weighted-least-squares regressions of the percentage of the cumulative price change occurring in each trade-size category and subcategory on dummy variables for the trade-size categories and sub-categories, the percentage of transactions (regressions 1 and 3), and the percentage of volume (regressions 2 and 4) occurring in that category. The weights are equal to the absolute cumulative price change over the sample period. It also reports the Wald tests for regressions 1 and 2, which test if all dummy variables are all equal to zero, if the coefficient for the percentage of transactions (or volume) is equal to one, and if all dummy variables are equal to each other. *, **, and *** denote significant at 10%, 5% and 1% levels respectively.

9 B.M. Cai et al. / Pacific-Basin Finance Journal 14 (2006) Table 4 Analysis of significant price decreases CumPC Num PcntNum P/N Vol PcntVol P/V Small Medium , Large ,000 14, ,000 19, ,000 24, , Sum For the cross-section of 141 stocks, this table reports the mean percentage of the cumulative stock price changes (CumPC), the corresponding number (Num), the percentages of trades (PcntNum), the ratio of percentage cumulative stock price change over the percentage of number of trades (P/N), the volume (Vol), the percentages of volume (PcntVol), and the ratio of percentage cumulative stock price change over the percentage of volume (P/V ). The results in regression (1) are not consistent with the public information hypothesis. The hypothesis that the coefficient for the percentage of transactions is equal to one can be rejected at the 0.01 level of significance. The hypothesis that all of the dummy variables are equal to zero can also be rejected at the 0.01 level. The results in regression (2) show that the coefficient for the percentage of volume is equal to one cannot be rejected at the 0.01 level of significance. The dummy variables being equal to zero is also rejected at the 0.01 level. The results indicate that the percentage of the stock-price change is smaller than predicted by the public information hypothesis in the small-size category, and larger than predicted in the medium and large-size categories. The coefficients are , and respectively in regression (1). It supports the finding in Table 2 that medium size trades have the largest contribution to the price discovery, while small size trades move price in a different direction which is counter to the overall price movement during the sample period. The results reject the public information hypothesis but support the stealth and large block trades/manipulation hypotheses with the cumulative price changes occurring on medium and large-size trades.

10 462 B.M. Cai et al. / Pacific-Basin Finance Journal 14 (2006) Table 5 Regression results of significant price decreases A. Regression 1 B. Regression 2 Variable Estimate t value Estimate t value DummyS *** *** DummyM *** *** DummyL *** *** PcntNum * PcntVol *** adjrsq Wald tests Wald tests Label Statistics ProbChiSq Statistics ProbChiSq S=0, M=0, L= *** *** PcntNum= *** PcntVol= S=M=L *** *** M=L S=L *** *** S=M *** *** C. Regression 3 D. Regression 4 Variable Estimate t value Estimate t value Dummy ** ** Dummy *** *** Dummy *** *** Dummy *** *** Dummy * Dummy *** *** Dummy Dummy Dummy Dummy Dummy Dummy *** *** Dummy *** *** Dummy *** *** Dummy *** *** Dummy ** Dummy *** ** Dummy ** Dummy * Dummy *** *** Dummy *** Dummy ** Dummy *** PcntNum *** PcntVol *** adjrsq Table 5 reports the weighted-least-squares regressions of the percentage of the cumulative price change occurring in each trade-size category and subcategory on dummy variables for the trade-size categories and sub-categories, the percentage of transactions (regressions 1 and 3), and the percentage of volume (regressions 2 and 4) occurring in that category. The weights are equal to the absolute cumulative price change over the sample period. It also reports the Wald tests for regressions 1 and 2, which test if all dummy variables are all equal to zero, if the coefficient for the percentage of transactions (or volume) is equal to one, and if all dummy variables are equal to each other. *, **, and *** denote significant at 10%, 5% and 1% levels respectively.

11 B.M. Cai et al. / Pacific-Basin Finance Journal 14 (2006) Table 6 Analysis of significant price increases CumPC Num PcntNum P/N Vol PcntVol P/V Small Medium , Large ,000 14, ,000 19, ,000 24, , Sum For the cross-section of 141 stocks, this table reports the mean percentage of the cumulative stock price changes (CumPC), the corresponding number (Num), the percentages of trades (PcntNum), the ratio of percentage cumulative stock price change over the percentage of number of trades (P/N), the volume (Vol), the percentages of volume (PcntVol), and the ratio of percentage cumulative stock price change over the percentage of volume (P/V ). Furthermore, all the results from the twenty-three subcategories are consistent with those from the three categories. For robustness purposes, stocks in the 1st and 4th quartiles are separated from the whole sample. As shown in Table 1, stocks in the 1st quartile have negative price changes between 25.05% and 6.55%, and stocks in the 4th quartile have positive price changes between 2.00% and 29.28%. The separation of the price-increase and price-decrease stocks provides more insights given Aggarwal and Wu's (2000) prediction that prices increase during manipulation periods Cumulative price decreases The results from Table 4 are consistent with those from Table 2 that most of the cumulative price decreases occur in the medium- and large-size trade categories. Trades in the medium-size category are responsible for about 144% of the cumulative price decrease with only about 45% of transactions and 56% of volume. The large-size trades cause about 52% of the cumulative price

12 464 B.M. Cai et al. / Pacific-Basin Finance Journal 14 (2006) Table 7 Regression results of significant price increases A. Regression 1 B. Regression 2 Variable Estimate t value Estimate t value DummyS *** *** DummyM * *** DummyL ** PcntNum *** PcntVol *** adjrsq Wald tests Wald tests Label Statistics ProbChiSq Statistics ProbChiSq S=0, M=0, L= *** *** PcntNum= *** PcntVol= S=M=L *** *** M=L *** S=L *** S=M *** *** C. Regression 3 D. Regression 4 Variable Estimate t value Estimate t value Dummy ** ** Dummy *** *** Dummy *** *** Dummy *** *** Dummy Dummy * ** Dummy Dummy Dummy Dummy Dummy *** Dummy Dummy * Dummy ** Dummy ** Dummy Dummy Dummy Dummy Dummy *** Dummy Dummy Dummy *** PcntNum *** PcntVol *** adjrsq Table 7 reports the weighted-least-squares regressions of the percentage of the cumulative price change occurring in each trade-size category and subcategory on dummy variables for the trade-size categories and sub-categories, the percentage of transactions (regressions 1 and 3), and the percentage of volume (regressions 2 and 4) occurring in that category. The weights are equal to the absolute cumulative price change over the sample period. It also reports the Wald tests for regressions 1 and 2, which test if all dummy variables are all equal to zero, if the coefficient for the percentage of transactions (or volume) is equal to one, and if all dummy variables are equal to each other. *, **, and *** denote significant at 10%, 5% and 1% levels respectively.

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