Who Is Smarter? Analysis of Block Trading in Chinese Stock Market

Transcription

1 International Review of Business Research Papers Vol. 11. No. 2. September 2015 Issue. Pp Who Is Smarter? Analysis of Block Trading in Chinese Stock Market JEL Codes: G12, G14, G15 1. Introduction Jiandong Li * and Lin Tan ** We report the specific feature of the block trading in the Chinese stock market. There are discounts in the block trading contrast to the block trading premium in the United States, Japan and the majority stock markets in the world. We show that the discount is rational because the stock s performance after the block trading is negative. Profit taking actions and private information are possible explanations for the discount. The segmentation between regular stock market and block trading market is not supported. We show that the stock returns pre- and post- the block trading are related to the discount magnitude. Block trading discount can be regarded as an important signal about future stock s performance. The benefits of voting power and potential corporate control encourage large block trades. Such benefits prompt investors to pay higher price for blocks than regular smaller trades on exchange. Indeed, because of the private benefits accrued exclusively to very large blockholders, the block price premium can be as high as 20% on average (16% as median) in the U.S.A stock market (Barclay and Holderness 1989). Mikkelson and Regassa (1991) report an average block premium of 9.2% in later U.S.A stock market. The block trading premium phenomenon has been recognized as a stylized fact. In addition, Dyck and Zingales (2004) study 412 control transactions in 39 countries between 1990 and They find the premium ranges from -4% in Japan to 65% in Brazil. They conclude that the emerging market will have a higher block trading premium than the advanced markets. The prominent exception is in China. Unlike the block trading in advanced stock markets where large block trade simultaneously with regular share trading, the block trading happens in an exclusive market in China. In order to reduce the influence of price and liquidity of the large block trades on regular exchange trades, China Securities Regulatory Commission (CSRC) order block trades to be fulfilled separately in a 30 minute trading platform after regular market closes. The block execution price reported in Chinese large block trading system is dominantly at discount rather than premium compared with the close price on regular exchange. The discount is even higher in recent years. * Dr. Jiandong Li, Central University of Finance and Economics (CUFE), Chinese Academy of Finance and Development (CAFD), 39 South College Road, Beijing , China, jiandongli@cufe.edu.cn ** Dr. Lin Tan, corresponding author, Finance, Real Estate and Law Department, California State Polytechnic University, Pomona, 3801 West Temple Ave. Pomona, CA 91768, USA, ltan@cpp.edu

2 Because it is puzzling why Chinese market is so different from other markets, we try to give possible explanations for this exceptional phenomenon. Specifically, we examine the stock s performance pre- and post- block trading. We find that the stocks after block trading show negative returns. This leads to a conclusion that the discount is rational. Discount can be regarded as a preceding signal for the stocks later performance. We show that returns are related to the discount magnitude. Therefore we refuse the explanation that block trading discount exists because the regular stock market and block trading platform market are separated. Due to Chinese stock market development history, profit taking actions and private (inside) information are reasonable explanations for the discount. The remaining of this paper is organized as follows. Section 2 describes the block trading discount phenomena in China. Section 3 introduces background information about Chinese market, especially the block trading platform. Section 4 verifies that the discount is rational and discount can be regarded as a signal for the stock s later performance. Section 5 relates the magnitude of discount to the stock s ordinary market performance. Section 6 concludes. 2. Literature Review Barclay and Holderness (1989), and Mikkelson and Regassa (1991) pioneered the research on block trade premium from the view of control benefits of block trading. Nenova (2003), Dyck and Zingales (2004) and others continue this research thread by making thorough comparisons of voting rights and control around world equity markets. The second main research thread on block trading is related with liquidity premium (Amihud and Mendelson 1986). Block trading involves large amount of shares, which would have had adverse price impact if executed on ordinary secondary market. Madhavan and Cheng (1997) find that although negotiation in the informal upstairs market provides better execution than the downstairs market for large trades, these differences are economically small. They argue that the block trades are liquidity motivated. The third research thread is about informed trading. To quickly capitalize the private information before others, informed traders may choose block trading channel. Seppi (1992) examines whether block prices are correlated with the unexpected part of the firms quarterly earnings. He finds that information revelation does indeed appear to be a significant factor shortly before earnings announcements. Keim and Madhavan (1996) further link the trade size with price movements prior to trade date and conclude that block is shopped upstairs. Different from the scholars works on why there are trade premium and what motivates the block trading, the current existing researches mainly focus on explaining how block trade premium affects the market, or try to explore the economics rationale behind block premium. For example, Brockman, Chung and Yan (2009) find that block trading has adverse impact on trading liquidity, and block ownership and market liquidity is inversed related. Kurek (2014) investigates the WIG20 index components on Warsaw Stock Exchange in Poland, and find block trades usually signal significant abnormal positive (negative) returns. Chung, Hwang and Kim (2014) use a sample of 593 block trades in the United States and find the block ownership comes with cost, the example of the cost is the litigation risk at the time of the block trades, which makes the block premium smaller. Alzahrani, Gregoriou and Hudson (2013) use high 2

3 frequency data to study all block trades on Saudi stock market, and they find asymmetric price impact on block purchases and block sales. Because Chinese market is very special, researches start to investigate this unique market. Huang and Xu (2009) document that blocks have huge discount in China, and they find private control benefits are closely related to block prices. Fan, Hu and Jiang (2012) study the block trades on the Shanghai Stock Exchange (SSE) and report an average of about 4% block discount. They think of that these trades are more likely to be originated from insiders sell. While market generally negatively react to the block trading, Bian, Wang, and Zhang (2012) find a different market reaction to block transactions when the buyer is represented by China International Capital Corporation (CICC), the number one investment bank in China which counts many foreign institutional investors as its clients. The positive reaction supports the hypothesis that some smart institutional buyers enter block trade indicating the buyers assessment of undervaluation. Our paper further investigates this abnormality by looking at both Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE), from both sellers and buyers at the block trade transactions, and tries to give explanations for this exceptional phenomenon. 3. Methodology 3.1 Data We collect all block trades of A-share in Chinese market from 2002 to i The dataset is from Wind. Table 1 presents statistics of our data. We see that the number of block trades increase by years in the sample period. Especially, both the block trading transactions and involved stocks in the block trading increase dramatically in The size of block trade is comparatively large with regard to corresponding daily average trading volume in the ordinary stock market. When we compute a ratio showing percentage change between the daily large block trading volume over corresponding stock s previous 30 days daily trading volume. The average ratio for the whole sample is i.e. the block trading volume is 2.5 times as much as daily average trading volume in the ordinary stock market times seems not too much high but there are usually one or very few transactions only in the block trading platform. 3

4 Table 1: Descriptive statistics of large block trading Year Number of block Number of Stocks involved Stocks listed on exchanges trading transactions in the block trading Sep 4 th Note 1: If several block trade transactions are on the same stock, at same discount on the same day, we count it as one observation; if block trade transactions are on the same stock but with different discount, we count them as different observations. The total observation number in our sample is Note 2: Some restricted non-tradable stock shares become tradable since Sep 4 th, So, we exclusively explore the data after that date. P BP We define the block discount as, where BP is block trade price of a stock. P is the close P price of the same stock on the same day. If it is positive, it is block trade discount; if it is negative, it is block trade premium. As shown in the table below, block price discount dominates price premium. Table 2 shows the statistics of block discount in the Chinese stock market. We find that more than half of those block trades are in discount every year. In the later period from September 4 th 2006 to 2008, the number of trades in discount to trades in premium is even 760:264. i.e. almost 3 in 4 block trades are in discount. The block discount average by year ranges from 0.34% to 3.14%. From the statistics, we conclude that block discount is a distinct feature for the stock market. 4

5 Table 2: Descriptive statistics of large block price discount Year Number of block trading Number of trades in discount Number of zero discount Number of trades in premium Mean of block discount % % % % % % % Sep 4 th % % Note: the block discount is defined as the same stock on the same day. P BP 3.2 Market Mechanism for Chinese Block Trading P, where BP is block trade price of a stock, P is the close price of The block trading with discount is not a rare case and it has been in Chinese stock market for consecutive seven years. There must be rationales for block traders to continuously trade at discount. We try to find such rationales and help investors understand Chinese market more thoroughly in the paper. We begin with introducing the trading mechanism in the Chinese stock market. According to the laws and regulations in China, block trading was first regulated for B-share block trades in August 1993 and it was in February 2002 that the regulations on A-share block trades were set up. We therefore have our sample beginning with On July 1 st, 2006, the block trading regulations were revised. Block trades are defined as each transaction to be more than 500,000 A-shares or more than 3 million RMB. The block trade price is negotiated by buyers and sellers. Buyers and sellers are registered institutions or agencies in the stock market. ii However, it must be within the ±10% limits from previous closing price. For those block trades when the referring stock has no limits, the block price must be with the highest and the lowest price of the day. If the referring stock has no trade in the board that day, the block price can range from 30% below or 30% above the close price of the nearest trading day (for example, some companies might be suspended of trading due to restructure or other legal reasons for several months). The trading time of blocks trade is within half an hour after regular trading hours. Regular market closes at 3:00pm so the block trading period is 3:00pm-3:30pm every day. iii The stocks traded in this block trading system can be traded in regular hours. And all shares represent the same dividend claiming rights and voting rights. Why investors choose to trade block in this exclusive platform? The first reason lies in the trading cost. Block trading benefits from a 70% discount in the trading cost according to the exchange regulations. 5

6 The second reason is that some forbidden shares have to be traded in the block trading platform since April Due to the setting up when a stock went public, not all shares are tradable. Some of them have a locking period such as five years. Some of them were even not setup a timeline when can be traded. This is obviously against the market principle. On September 4 th, 2005, China Securities Regulatory Commission (CSRC) enacted the way how the non-tradable shares can be traded (so called stock market reform ). If the amount of the non-tradable shares is less than 5% of all issued shares, the holder can sell them one year later. These shares are called Xiao Fei, representing small amount of non-tradable. Accordingly, those non-tradable shares held by more than 5% are called Da Fei, representing big amount of non-tradable. The Da Fei shares can go to market no more than 5% after the first year, no more than another 5% after the second year. Therefore, we consider the subperiod of from Sep 4 th 2006 to the end of 2008 because that period includes such Xiao Fei and Da Fei. On April 20 th, 2007, CSRC ordered those new tradable shares to be traded in the block trading platform if the amount of traded shares is over 1% of the company, and if the shares are sold within one month. Not all block trades are those Xiao Fei and Da Fei shares, and the data published by the exchanges are not detail enough to tell which transactions stand for them. However, the block trades obviously increased after the forbidden shares get freedom. The degree of block trade discount is increasing as well. On June 30 th, 2007, CSRC added more constraints for those State-Owned-Enterprises (SOE). The SOE s Da Fei shares can only sell no more than 5% within 3 years after it becomes tradable, or no more than 3% within 3 years if the company has over 1 billion issued shares. And, the controlling right should not be lost after trading those Da Fei shares. Given above background of Chinese stock market, we are searching the reasons why block trades are mostly traded in discount. Since the block trade prices are negotiated by the buyers and sellers, it is most case that the negotiated prices are set much earlier than when transactions happen. According to the regulations of exchanges, the block trade order can be put during 9:30am-11:30am and 1:00pm- 15:30pm. So, if the market goes up while the negotiated price is honored, we observe trading in discount. However, it is still unusual that the regular closing prices mostly go up in our sample. 3.3 Models Putting aside the liquidity demand, the very reason for trading is that both parties believe that they are right in predicting the future. If investors forecast future price goes up, they would buy; if goes down, they would sell. The forecasting may root in their private information or own judgment on same public information. Then we want to see which party is smarter. We compute the stock s cumulative return after day of the block trades as shown in the Table 3. 6

7 Table 3: Average of cumulative returns after block trading Whole sample Sep 4 th trades in premium Trades at par price trades in discount Discount average 2.83% 3.05% -2.35% 0.00% 5.04% Cumulative 1 week -0.62% -0.71% -1.12% 2.68% -0.94% Cumulative 2 weeks -2.02% -2.36% -0.57% -0.41% -2.77% Cumulative 1 month -3.98% -4.46% -3.12% -0.46% -4.81% Cumulative 2 month -2.82% -3.18% 2.12% 4.81% -5.67% Cumulative 3 months -2.65% -2.88% 3.80% 6.16% -6.20% Cumulative 6 months 8.70% 9.78% 18.18% 22.66% 3.33% Cumulative 1 year 23.61% 25.63% 29.36% 29.46% 20.75% Note: the table gives the cumulative returns over 1 week, 2 weeks, 1 month, 2 months, 3 months, 6 months, and 1 year after the block trading happens. For block trades after Aug 31 st 2008, we cannot compute the 1 year cumulative return after block trading since our trading information ends on Aug 31 st The row of 1 year cumulative return is just indicative. Results show that in the short term (1 week to 3 months), block trade sellers gain; in the long term (6 months and 1 year), buyers gain. This result re-appears in the sub-period of from Sep 4 th 2006 to the end of To see if there is difference between block trade discount and premium, we classify all transactions by trade discount. Stocks that are traded in discount have worse cumulative returns than those non-discount stocks. Specially, stocks that are traded with premium show that the underlying stock s cumulative return after block trading turn to positive quickly. After 1 month, Cumulative return becomes positive. These results show that the discount is reasonable. Trade discount serves as a signal that the later stock performance is bad in the near future and is worse than those non-discount stocks. Why the stock is traded in discount might due to the private information of the sellers in the block trades. To test if the signal is a true signal, we have to take into account the market performance. Now, we test the Cumulative Abnormal Returns (CAR). The results are shown in the below table, which shows that CAR of 1 week, 2 weeks, 1 month, 2 months, 3 months, 6 months, and 1year. The conclusion is that all stocks involved in block trades have negative CAR. This is in contrast to the literature. Barclay and Holderness (1991) report evidence that the trades of large blocks are associated with abnormal stock price increases. The stocks traded in discount have better CAR of 1 week and 2 weeks, worse CAR of 1 month, 2 months, and 3 months than stocks traded not in discount. This result verifies that trade discount is a signal about near future performance (1 month-3months). However, the difference is not significant. 7

8 Table 4: Average of cumulative abnormal returns after block trading Sep 4 th trades in premium Trades at par price Trades not in discount trades in discount Whole sample Discount average 2.83% 3.05% -2.35% 0.00% -1.63% 5.04% CAR 1 week -0.35% -0.40% -0.71% -0.03% -0.50% -0.27% CAR 2 weeks -0.39% -0.51% -0.43% -0.50% -0.45% -0.36% CAR 1 month -1.13% -1.17% -0.88% -1.59% -1.10% -1.14% CAR 2 months -1.53% -1.45% -1.30% -1.55% -1.37% -1.60% CAR 3 months -1.11% -0.84% -0.23% -1.59% -0.64% -1.34% CAR 6 months -1.00% -0.13% -2.30% -1.25% -1.98% -0.52% CAR 1 year -1.22% 0.37% -1.89% -5.14% -2.88% -0.40% For block trades after Aug 31st 2008, we cannot compute the 1 year cumulative return after block trading since our trading information ends on Aug 31st The data row of 1 year cumulative return is just indicative. We use CAPM model as benchmark to calculate the CAR. The market portfolio is proxied by Shanghai Stock Exchange Composite index. The beta is obtained by using previous 1 year historic data. Why do all stocks involved in block trading have negative CAR? This might because the block trades are seller-initiated and sellers have some private information. Because we have the executed transaction information rather than order information, we cannot justify our conjecture. However, there is ground to believe so. Most non-tradable shares have been obtained in very low prices. So, when they are allowed to go to market, it is very natural that there is profit taking actions. Although we believe that those non-tradable share holders are rational investors and wouldn t sell those shares at any cost, most ordinary individuals and even the CSRC regulators believe that overwhelming sale actions are expected after Da Fei and Xiao Fei become free. If the profit taking action is the case, we would see most of the block trades are seller-initiated. And, if most block trades are sellerinitiated, we definitely would observe block trade discount. This is profit-taking action hypothesis, to which we cannot test. Since the block trade platform is independent of the regular trading market. One might think of whether the block trades are in discount or in premium is only related to the supply and demand within the block trade platform. It is hard to test whether two markets are independent or interlinked. We cannot use correlation analysis or volatility transmission analysis methods because block trades of individual stock happen at very low frequency. Nonetheless, we use following method to see whether the trade discount is linked with the ordinary stock market. We first use multinominal model (generalized logistic model) to see when the block trades are at discount, at par, and at premium. The model is expressed as: Pr( par X ) 1 exp( i,0 exp( i,0 i,0 i,0 i,0 X ) X ) exp( i,0 i, i, i, X ) exp( i, i, X ) Pr( premium X ) 1 exp( X ) exp( i, X ) 8

9 1 Pr( discount X ) 1 exp( i, 0,0 X ) i exp( i, i, X ) Where each case s probability is modeled as a generalized logistic function. The sub 0 indicates trading at par, sub (-1) indicates trading at premium. Trading at discount is treated as reference category. X is a set of explanatory variables. 4. Empirical Results The explanatory variables may include the stock s historic trading data and financial data before the block trading. Since all financial data are stale (quarterly) and public available. We focus on historic trading information on ground that recent trading data reflect some leaked updated information. We report estimate results below using the one variable of cumulative returns of 3 month before the block trading. Table 5: Historical trading information affects block trade prices At premium At Par Variable Estimate StdErr WaldChiSq ProbChiSq Intercept (-1) E-21 Cumulative 3 month (-1) E-06 Intercept(0) E-44 Cumulative 3 month (0) E-10 The model is multinominal model. There are three categories: trade at premium, trade at par, and trade at discount. Trade at discount is treated as reference category. Explanatory variable _cum_3m is the stock s cumulative returns of 3 month before the block trading. From the result table, we can interpret as following: Pr( premium) log( ) * _ cum _ 3m pr( discount ) Pr( par) log( ) * _ cum _ 3m pr( discount ) The results tell that the logarithm of probability of being at premium (or being at par) over the probability of being at discount is positively related to past 3 month s stock performance. If past 3 month s cumulative return increase by 1%, then the probability of being at premium over the probability of being at discount increases by 45% (e =45% ), the probability of being at par over the probability of being at discount increases by 193%. In other words, when the stock show weak performance in the last 3 months, it is more likely that the block trades are at discount. Block trades are partially based on past performance. We can draw above conclusion even without bothering those complicated models. Run the simple regression model such as following: discount int ercept * _ cum _ 3m 9

10 The dependent variable is the block trade discount; the explanatory variable is the previous 3 month cumulative return. iv The regression results in an estimation: discount = *_cum_3m. If the previous 3 month cumulative return decreases 1%, the discount will increase 0.03%. In other words, bad past performance is more likely to lead to block trade discount. Table 6: Historical trading information affects block trade discount magnitude Dependent variable: block trade discount Variable Estimate Std Error t-value Probability Intercept E-29 Cumulative 3 months E-10 discount int ercept * _ cum _ 3m Note: The model is linear simple regression:. Dependent variable is the discount magnitude. Explanatory variable _cum_3m is the variable of cumulative returns of 3 month before the block trading. Of course, we can search for more explanatory variables to strengthen the models. However, our purpose of using this generalized logistic model is not to predict but to show that the trade discount is not independent of ordinary stock market. It removes the suspect that the block trading platform is separated from ordinary stock market. Not only is the block trading affected by the ordinary stock market, the block trades also impact the ordinary stock market because the stock holders changed. We have evidence in the below table that the block trade discount is significantly correlated to the return after block trading. Table 7: Correlation coefficients of block trade discount and thereafter stock cumulative returns Correlation Coefficients between discount Probability Cumulative 1 week Cumulative 2 weeks Cumulative 1 month Cumulative 2 months Cumulative 3 months Cumulative 6 months Cumulative 1 year Note: the table reports the correlation coefficients of block trade discount and cumulative returns after block trading. From above table, we clearly notice that the discount is negatively related to the cumulative returns. This indicates that the block trades happen in the path of price s going down. Stock prices weakening leads to the block trade discount. The discount, vice versa, leads to continuous weakening stock prices. This again verifies our conclusion: the discount is a signal. 10

11 5. Conclusion Block trading is usually accompanied with trading premium. This is explained by the private benefits obtained from corporate control. However, we find that Chinese stock market exhibits an abnormal pattern in the block trading. Majority of block trading are in discount. To our knowledge, this is one of very few papers addressing the distinct feature of block trading at discount in Chinese stock market. Utilizing the market data, we find that the discount is related to past stock performance. Bad performance is more likely to lead a block trade at discount. Vice versa, trade discount is a signal of near future weak performance. Sellers are smarter in that the near future stock performance is negative. In this sense, the trade discount is reasonable. The reasons behind the trade discount might be the profit taking action or private information, to which this paper doesnt have resources to test. We find the discount is not independent of ordinary stock market. That is to say, we cannot think the block trading platform is isolated and the discount is only a matter of demand and supply in the block trading platform. Past performance affect if the block trade is in discount and the discount magnitude. Discount magnitude, on the other hand, implies future performance. Chinese stock market is getting more and more important. The following two years are very worthy of more attention because more Da Fei (big amount of non-tradable shares) are freed through the block trading platform. As more block trades happen, we will return and review this paper s conclusion. Endnotes i Chinese stocks are classified into A-share, B-share, and H-share according to where they are listed and who can trade them. There are also B-share, fund, and bond block trades in the Chinese block trading market. Our paper is restricted on A-shares. ii There are Shanghai Stock Exchange (SSE) and ShenZhen Stock Exchange (SZSE) in China. Investors always choose to open accounts or register membership in the two markets simultaneously. A company can only be listed in one of these two markets. Rule of thumb is that SSE lists big-cap companies and SZSE lists mid-cap companies. So, the two markets look like one market to investors. Of course, there are some minor differences between two exchange markets such as the trading cost. QFIIs (Qualified Foreign Intuitional Investors) are allowed to use block trading platform. However, there are many restrictions on their doing so. iii The block trade price doesn t change the closing price of that stock. But the block will be counted in the daily trading volume. If the stock is an index component, the block trade doesn t affect the calculation of that index. iv Using cumulative return of other periods such as 1 week, 2 weeks, 1 month, 2 months, 6 months generates similar results for Table 5 and Table 6. Details are available upon request. References Alzahrani, A, Gregoriou, A, & Hudson, R 2013, Price Impact of Block Trades in the Saudi Stock Market, Journal of International Financial Markets, Institutions and Money, 23, 1, pp , EconLit, EBSCOhost, viewed 9 February Amihud, Y, & Mendelson, H 1986, Asset Pricing and the Bid-Ask Spread. Journal of financial Economics, 17, 2, pp

12 Barclay, MJ, & Holderness, CG 1989, Private Benefits from Control of Public Corporations,Journal of Financial Economics, vol. 25, no. 2, pp Bian, J, Wang, J, & Zhang, G 2012, Chinese Block Transactions and the Market Reaction, China Economic Review, 23, 1, pp Brockman, P, Chung, D, & Yan, X 2009, Block Ownership, Trading Activity, and Market Liquidity, Journal of Financial and Quantitative Analysis, 44, 6, pp , EconLit, EBSCOhost, viewed 9 February Chung, J, Hwang, J, & Kim, J 2014, Block Premium and Shareholder Litigation, Asia-Pacific Journal of Financial Studies, 43, 3, pp Dyck, A, & Zingales, L 2004, Private Benefits of Control: An International Comparison, Journal of Finance, 59, 2, pp Fan, L, Hu, B, & Jiang, C 2012, Pricing and Information Content of Block Trades on the Shanghai Stock Exchange, Pacific-Basin Finance Journal, 20, 3, pp Huang, Z, & Xu, X 2009, Marketability, Control, and the Pricing of Block Shares, Journal of Banking and Finance, vol. 33, no. 1, pp Keim, DB, & Madhavan, A 1996, The Upstairs Market for Large-Block Transactions: Analysis and Measurement of Price Effects, Review of Financial Studies, 9, 1, pp Kurek, B 2014, The Information Content of Equity Block Trades on the Warsaw Stock Exchange: An Estimation of Shares Returns with the Usage of Simple Linear Regression and Multivariate Adaptive Regression Splines, Journal of Forecasting, 33, 6, pp Madhavan, A. & Cheng, M 1997, In Search of Liquidity: Block Trades in the Upstairs and Downstairs Markets. Review of Financial Studies, 10, 1, pp Mikkelson, W, & Regassa, H 1991, Premiums Paid in Block Transactions, Managerial and Decision Economics, 12, 6, pp Nenova, T 2003, The Value of Corporate Voting Rights and Control: A Cross-Country Analysis, Journal of Financial Economics, 68, 3, pp Seppi, DJ 1992, Block Trading and Information Revelation around Quarterly Earnings Announcements, Review of Financial Studies, 5, 2, pp