Market liberalization and the extent of informed trading: Evidence from China’s equity markets

Accepted Manuscript Title: Market liberalization and The extent of Informed trading: evidence from China’s Equity Markets Author: Yaseen S. Alhaj-Yaseen Xi Rao Yinghua Jin PII: DOI: Reference:

S1042-444X(16)30101-3 http://dx.doi.org/doi:10.1016/j.mulfin.2016.11.003 MULFIN 515

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Received date: Revised date: Accepted date:

4-1-2016 7-11-2016 9-11-2016

Please cite this article as: Alhaj-Yaseen, Yaseen S., Rao, Xi, Jin, Yinghua, Market liberalization and The extent of Informed trading: evidence from China’s Equity Markets.Journal of Multinational Financial Management http://dx.doi.org/10.1016/j.mulfin.2016.11.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

MARKET LIBERALIZATION AND THE EXTENT OF INFORMED TRADING: EVIDENCE FROM CHINA’S EQUITY MARKETS

YASEEN S. ALHAJ-YASEEN College of Business Administration University of Findlay Findlay, OH 45840 Email: [email protected] XI RAO School of Economics and Business Administration Chongqing University Chongqing, China Email: [email protected] Yinghua Jin School of Economics Zhongnan University of Economics and Law Wuhan, China Email: [email protected]

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Highlights:    

Speculative trading in China has eroded as a result of market liberalization. The atrophy of speculative trades has given rise to rumor-based trading. Stock returns are now more reflective of market fundamentals. Market reforms have improved the informational efficiency of the Chinese market.

Abstract In this study we investigate changes in the extent of speculative (informed) trading in China’s equity markets around market liberalization. To do so, we examine the dynamic relation between trading volume and stock returns’ autocorrelation of all individual stocks traded in mainland China’s equity markets between 1995 and 2010. We develop a “Trade Informativeness” index that tracks changes in the magnitude of informed trading over time. Our results show that the extent of speculative trading has eroded as a result of market liberalization. We also find some evidence that stock returns are more reflective of market fundamentals post market liberalization. Both findings suggest an improvement in the informational efficiency of the Chinese stock markets.

Keyword: Dynamic Volume-Return Relation, Information-Based Trading, Market Liberalization, Informational Efficiency.

MARKET LIBERALIZATION AND THE EXTENT OF INFORMED TRADING: EVIDENCE FROM CHINA’S EQUITY MARKETS

1. INTRODUCTION The purpose of this study is to examine if and to what extent has Chinese market liberalization changed the magnitude of informed trading in Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE). We do so by studying the dynamic relationship between daily trading volume and stock returns’ autocorrelation for all listed stocks in mainland China between 1995 and 2010. Llorente et al. (2002) argue that trading in the stock market is motivated by either speculative (informational) or hedging (non-informational) considerations. Trading for speculative reasons occurs in order to exploit private information. Therefore, returns associated with informed trades are likely to continue in the future as private information is further reflected in the price. On the other hand, trading for hedging purposes occurs to rebalance investment

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portfolios for risk-sharing, implying that returns associated with such trades are likely to reverse in the future. Furthermore, the extent of return continuations or reversals depends on the degree of information asymmetry. This means that stocks with higher degree of information asymmetry (e.g. small firms) are more likely to be associated with speculative trading, and therefore exhibit return continuations on days with high trading volume. 1 On the contrary, stocks with lower degree of information asymmetry (e.g. large firms) are more likely to be motivated by hedging considerations, and therefore exhibit return reversals on days with high trading volume. Llorente et al. (2002) confirmed the theoretical prediction of their theory empirically using all publicly traded firms on NYSE and AMEX between 1993 and 1998. Since China joined the World Trade Organization (WTO) in 2001, rigorous but purposeful market reforms have been implemented by the Chinese authorities to liberalize equity markets. In February 2001, China Securities Regulatory Commission (CSRC) allowed local investors to trade in the B-market, a market that was previously restricted to foreign investors. More than a year later, in November 2002, foreign investors, as Qualified Foreign Institutional Investors (QFII), were allowed to access the A-market, the local Chinese market that was restricted to local investors previously.2 However, it was not until May 2003 when the first QFII license was issued.3 These reforms, among others, played a major role in attracting foreign capital over the last decade.4 As of November 2014, China’s equity market was the second largest in the world with US$4.48 trillion of market capitalization.5 However, since it was established in the early 1990s, the Chinese equity market has faced many challenges that the Chinese authorities continue to work hard to overcome until today, e.g., market manipulation, insider trading, accounting fraud, absence of independent auditing entities and disciplinary authority, government distortion in Although the term “speculative trading” is sometimes confused with gambling that depends on random chance, speculation does not depend on pure or random chance. Instead, the risk of loss under speculation is more than offset by the probability of making huge gains. 2 In mainland China, firms can issue one of two classes of shares, A, B, or both. Although foreign investors had an access to Chinese equity market through B-market, this access was restricted to a limited number of stocks. After November 2002, foreign investors have the ability to access the A-market through QFII program. A- and B-markets were completely segmented before 2001-2002 market reforms. 3 In Section 5, we provide more discussion and details on the first QFII licensees issued in the Chinese market. 4 In the following section we provide an overview for SHSE and SZSE as well as the main market reforms adopted by the Chinese authority over the last decade. 5 http://www.bloomberg.com/news/articles/2014-11-27/china-surpasses-japan-as-world-s-second-biggest-equitymarket 1

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favor of State-Owned Enterprises (SOEs), and disconnection with economic fundamentals (Cheng & Li, 2015; Girardin & Liu, 2003; Green, 2004, 2003). In 2001, Wu Jinglian, an eminent Chinese Economist, described the chaotic environment in the Chinese stock market as a "Casino" or perhaps even "worse than a Casino" as he notes a Casino still operates on rules. In this study, we are motivated by recent reforms adopted by the Chinese authorities to liberalize equity markets and how they impact the extent of informed trading. More specifically, we aim to examine whether and to what extent has Chinese market liberalization mitigated the severity of speculative trading that previously dominated the market. In recent years, the Chinese authorities' crackdown on private trading is getting more aggressive (Du & Wei, 2004; Tong et al., 2013). Stricter legal enforcement and harsher punishment against insider trading should reduce the value of insider’s private information, and therefore form an environment with less informational asymmetry (Chi, 2013). Therefore, market liberalization in China is expected to improve the informational efficiency of their equity markets, and hence reduce the extent of speculative trading. That is, we should expect a significant drop in the magnitude of informed trading in China as a result of market liberalization.

Following the approach of Llorente et al. (2002), we investigate the extent of informed trading in SHSE and SZSE by examining the dynamic relationship between daily trading volume and first-order return autocorrelation for individual stocks. In addition, we extend their work in two dimensions. Firstly, Llorente et al. (2002) average the magnitude of informed trading over the full period, 1993-1998, and show how it correlates with the degree of information asymmetry over the same period. In our study, we do the same for a 15-year period, but we also study changes in the magnitude of informed trading as a result of market liberalization. Likewise, we study the existence of a monotonic relationship between the extent of informed trades and information asymmetry measures, for the full period and around market liberalization. Such analysis should demonstrate how reflective stock returns are to market fundamentals around market liberalization.6

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Market efficiency implies that the extent of informed trading should be positively correlated with the degree of information asymmetry (Blume et al., 1994). 4

Secondly, in order to avoid the flaws of aggregation, we develop an index that tracks quarterly changes in the magnitude of informed trading. It shows the relative significance of information-based

trades

versus

non-information-based

trades.

This

index,

“Trade

Informativeness”, represents a rich source of information that is useful for market observers as well as participants. It can help them better understand the stock markets’ microstructures and fundamentals, which can further refine the accuracy of predicting future price movements. Furthermore, although not the main goal of this study, analyzing volume-return interactions and their extent to the degree of information asymmetry provides valuable inferences regarding market efficiency. Many studies concerned with the effect of market liberalization focus on its potential impact on volatility, return behavior, and market integration. Fewer studies, however, focus on changes in market informational efficiency as a result of market liberalization. Empirical evidences from these studies, unfortunately, are inconclusive and conflicting. While some studies reject the hypothesis of weak efficiency in emerging markets as a result of financial deregulations (Kawakatsu & Morey, 1999; Laopodis 2004), others fail to reject it and conclude that market liberalization indeed improves market efficiency (Füss, 2005; Kim & Singal, 2000a, 2000b; Rejeb & Boughrara, 2013). The inconclusive and conflicting results are, to a great extent, attributed to the methodologies used in these studies (Laopodis, 2004). On one hand, many studies evaluate the impact of financial deregulation on the market efficiency around a specific date, “liberalization date”, without taking into account the gradual nature of market liberalization (Bekaert, 1995).7 On the other hand, financial deregulations are typically preceded by financial crisis, especially in emerging markets, and assuming the stability of the model parameters over time is inconsistent with the structural changes induced by such crisis (Rejeb & Boughrara, 2013). 8 We avoid the first obstacle by choosing different market liberalization windows that span over as short as one quarter and as long as ten quarters. Also, we avoid the second obstacle by developing a dynamic index, “Trade Informativeness”, which relies on new variable inputs, and therefore variable estimated coefficients, for each quarter. This allows us to track variations in the magnitude of informed trading over time. Empirical analysis of this study reveals important results. Firstly, we document a significant erosion of informed trading in the post-liberalization period, a sign of improved market

7 8

After all, market liberalization is a continuous process and it may take years before its full impact is evident. In Section 6, we explain in details how we develop the index for both markets, SHSE and SZSE. 5

efficiency. In this finding we are in line with Chan et al. (2008), Carpenter et al. (2015), and Chi (2014). Secondly, we find a strong evidence that positive and monotonic relationship between the extent of informed trading and information asymmetry measures is present in the postliberalization period, but not in the pre-liberalization period, another sign of improved market efficiency. Thirdly, comparing our results to those from the U.S. market, we find that, on average, the extent of informed trades is lower in the Chinese market, suggesting the dominance of uninformed trades post market liberalization. Moreover, our results are robust to different liberalization windows, and alternative measures of information symmetry. In our robustness testing, we consider different liberalization windows, and pre- and post-liberalization periods. We also consider a third proxy for information asymmetry, State-Owned Enterprise (SOE) ratio.9 Finally, we account for potential high synchronicity between individual stock returns and market-wide returns by controlling for market index daily returns in our models.10 The main contribution of this study is that it examines the joint volume-return relation and how it correlates with the degree of information asymmetry in SHSE and SZSE. By doing so, we are trying to investigate if and to what extent has Chinese market liberalization changed the magnitude of informed trading. We are the first to explore this dynamic relationship between trading volume and stock returns’ autocorrelation in an emerging market like China, and over a long period of time, (1995q4 -2010q4).11 In this study we also explore changes in the magnitude of informed trading around market liberalization in China stock market and develop an index that tracks the extent of informed trading over time. 12 Finally, findings of this study provide supporting evidences to studies concerned with short-horizon return autocorrelations in emerging markets like China.13 Our paper proceeds as follows. In Section 2, we provide a brief background of China’s equity markets, SHSE and SZSE. In Section 3, we discuss our model’s theoretical background and develop our hypotheses. Section 4 introduces the methodology used in this study, while Section 9

Chi (2014) finds that firms with high SOE ratios are prone to more speculative (informed) trading. The main models of this study are introduced in Section 4, while the modified ones are presented in Section 7. 11 Whereas Llorente et al. (2002) study NYSE and AMEX over 6 years, 1993-1998, we examine the same relationship in the Chinese market over 15 years, 1996-2010. 12 As we will see in Section 6, the graphical depiction of this relationship can serve as an adequate visual reference to identify periods with trades motivated by hedging (non-informational) or speculative (informational) considerations. 13 There is a large body of separate literature interested in the relation between trading volume and stock returns over the intermediate and long horizons. For more details, see Lee and Swaminathan (2000). 10

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5 introduces the data. Section 6 provides the presentation and discussion of the empirical results. Finally, Section 7 contains robustness testing, and Section 7 concludes. 2. CHINA’S EQUITY MARKET The Shanghai stock exchange (SHSE) and Shenzhen Stock Exchange (SZSE) are the two major stock exchanges in Mainland China. SHSE was established in December 1990 and SZSE in July 1991. By the end of 2012, 923 Chinese firms were listed on SHSE with a total market capitalization of US$2.490 trillion; 1476 firms were listed on SZSE with a market capitalization of US$1.085 trillion. 14 Both markets run order-driven automated systems. Initially common shares were only available for local investors to trade but not foreign investors, i.e. A-shares (restricted shares). In 1992, a second class of shares called B-shares (unrestricted shares) was introduced in both exchanges in order to attract more foreign capital. The introduction of Bshares in 1992 created two segmented markets, an A- and B-market. Whereas local Chinese investors were only allowed to trade in the A-market, foreign investors were strictly limited to trade in the B-market. A- and B-shares had identical rights in all respects, i.e., dividends and voting rights were the same. The only difference between the two classes is that A-shares were denominated in local currency while B-shares were denominated in US Dollars at SHSE and Hong Kong Dollars at SZSE. Dual-listing in SHSE and SZSE by local companies was not allowed. In February 2001, in an attempt to liberalize equity markets, the China Securities Regulatory Commission (CSRC) opened the B-market for local traders. Following that, the CSRC opened the A-market for foreign investors in November 2002. However, UBS and Nomura Securities were the first to receive the qualified foreign institutional investor (QFII) status in May 2003 and started trading in the A-market at SHSE and SZSE afterward. The two segmented markets, Aand B-markets, became more integrated after 2001-2002 market liberalization. After peaking in 2001, SHSE saw a four-year tumble. Since 2005, a ban on new IPOs was implemented to halt the tumble and allow several hundred billion dollar value of state-owned NTS (Non-Tradable Shares), about 47% of total market shares of all listed firms, to be converted to tradable shares. With a stock market frenzy starting in 2005 in China, the SHSE composite index rocketed from 998 points in May 2005 to 6124 points in October 2007, an increase of more 14

We use the number of outstanding shares and the adjusted closing price as of 12/31/2012 obtained from the China Securities Regulatory Commission (CSRC) to calculate the market capitalization for both markets, SHSE and SZSE. 7

than six times within about two years. On May 30 of 2007, the number of stock trading accounts in China reached 100 million and the total market equity value reached around 2.37 trillion US dollars, which is equivalent to the total national saving in China around that period of time. However, after it reached an all-time high of 6124 points on October 16, 2007, the SHSE composite index ended 2008 with a fall of 73% due to the global financial crisis starting from mid-2008. Although it bounced back to about 3500 points in August 2009, the SHSE composite index has been plunging between 2009 and 2015. In 2015, the SHSE composite index has had solid raise again from 3239 points at the end of 2014 until 5178 points on June 12, 2015. But after that, China’s stock market crashed again with one-third of value of A-shares lost within one month. As one of the largest markets in the world, the Chinese equity market is known for its extreme volatility with bench market indices such as SHSE composite index swinging as much as 10 percent hourly. Before market liberalization, the market was incomplete in terms of unequal rights for same stock shares just due to different holders, small sized, between NTS held by the state and financial institutions and tradable shares held by individuals. Therefore, it can be termed a semi-closed equity market with less volatility. After market liberalization, all stock shares can be tradable and open to global investors, and its size has been expanded significantly.

3. MODEL THEORETICAL BACKGROUND AND HYPOTHESIS DEVELOPMENT In developing our testable hypotheses, we rely on several studies that investigate the relationship between trading volume and predictable returns patterns. For example, Campbell et al. (1993) investigate the relationship between volume and returns’ serial correlation. In their model, they have two kinds of investors, liquidity or non-informational traders and risk-averse or market makers. A fall in stock prices could be due to either public information that can be thought of as a negative shock to the stock market in general, or significant selling by noninformational traders who sell stocks for exogenous reasons. The first case suggests that there will be no change in expected returns on the stock market. However, the second case implies that market makers who bought stocks today will expect a high return tomorrow that will be transformed into an increase in stock prices in subsequent days. Campbell et al. (1993) suggest that trading volume can be used to distinguish between the two cases. While it is not reasonable to expect a high volume shock when public information is revealed, significant selling by 8

uninformed traders is more likely to create a considerable volume shock. Hence, their model with heterogeneous investors suggests that “price change accompanied with high volume will tend to be reversed”. Another study by Blume et al. (1994) considers a simplified rational expectation model, developed by Brown and Jennings (1989) and Grundy and McNichols (1989) in which investors use previous stock prices and trading volume to learn about these stocks. They argue that investors do not really know with certainty the real value of the asset because of the common error term, which is hard to capture. Meanwhile, volume-price interaction provides traders with all available information in the market about the stock. Therefore, the only way to obtain all this information is by analyzing the sequences of price and volume. This analysis is really valuable because it helps traders deduce all the information available in the market. Also, the model of Blume et al. (1994) suggests that smaller and less widely followed stocks will have greater uncertainty about their future prospects and lower prior precision. Hence those stocks will be more affected by private rather than public information. Wang (1994) extends the model of Campbell et al. (1993) and presents two kinds of investors, informed and uninformed investors. Informed investors trade stocks based on some private information they obtain on their own, while uninformed investors use realized dividends, prices, and public information available in the market. Both kinds of investors trade competitively in the stock market. The informed investors base their trades on information about the stock’s future cash flow, giving rise to their informational trading. They also can trade to rebalance their portfolio, giving rise to their non-informational trading. On the other hand, uninformed investors trade in the stock market solely for non-informational reasons. Therefore, trade will exist between the two parties since informed investors can trade for non-informational as well as informational reasons. Wang’s model analyzes the dynamics of trading volume and stock returns and how they interact with the degree of information asymmetry in the market. He argues that the ability of uninformed investors to identify the reasons behind the informed investor’s transactions in the market is very limited. Hence, the risk of information asymmetry arises, decreasing the trading volume as the non-informed investors refrain from trading. Also, Wang’s model suggests that trading volume is positively correlated with the absolute change in stock prices, and this correlation increases with the degree of information asymmetry. 9

Two other noteworthy suggestions of Wang (1994) uncover the predictability power of his model regarding the future stock returns. The first is that high returns accompanied by high volume implies high future returns when there is informational trading; the second suggests that high returns accompanied by high volume implies low future returns when there is noninformational trading. Therefore, it is clear that the degree of information asymmetry, which determines the informational and non-informational trading, controls the dynamic relation between trading volume and returns. A more recent study by Llorente et al. (2002) also extends the model of Campbell et al. (1993). However, their starting point is that hedging or non-informational trades generate negatively auto-correlated returns, and speculative or informational trades generate positively auto-correlated returns. They argue that trading volume provides valuable information and helps determine periods with non-informational and informational trades which in turn can help predict the future movement of stock prices. In high volume periods, if trading is motivated by speculative considerations, then it should exhibit positive return autocorrelation. However, if trading is motivated by hedging considerations, then it should exhibit negative return autocorrelation. The model of Llorente et al. suggests that “return, volume, and future returns depend on the relative significance of speculative trade versus hedging trade” (2002, p. xxx). That is, if speculative trades for a stock dominate, then returns associated with high trading volume are most likely to continue in the future. In contrast, if hedging dominates trading of a stock, then returns associated with high trading volume are most likely to reverse in the future. Llorente et al. (2002) empirically examine the predictions of their model using all publicly traded firms on NYSE and AMEX over a six-year period, from January 1, 1993 to December 31, 1998. They use firms’ market capitalization and bid-ask spreads as proxies for the degree of information asymmetry. Their results are consistent with the prediction of their model; they find stocks of small firms and high bid-ask spreads exhibit a tendency for return continuation following high-volume shocks. In contrast, stocks of large firms and low bid-ask spreads exhibit a tendency for return reversal following high-volume shocks.15

Another recent study in this arena is by Gagnon and Karolyi (2009). They modified Llorente et al.’s model to a multimarket international setting in order to examine the volume-return dynamics of cross-listed stocks in the home and U.S. market, and how these dynamics are correlated with the extent of information asymmetry. They find that stock returns with low-degree of information asymmetry, i.e. large firms, have a tendency to reverse in one market following high-volume days in the other market. In contrast, stock returns with a high-degree of information 15

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Among other challenges, the Chinese stock market suffered from insider trading in its early years, which can be attributed to the weak law enforcement in China. In recent years, however, the Chinese authorities have tightened their insider-trading restrictions. Many studies have documented an erosion of insider trading in China as a result of that (Carpenter et al., 2015; Chan et al., 2008; and Chi, 2014). Following the intuition of Llorente et al. (2002), if insider trading dominated the Chinese stock market prior market liberalization, we expect returns associated with positive high trading volume to continue in the future as private information is further reflected in the price. On the other hand, market liberalization is expected to improve the informational efficiency of the Chinese stock market and therefore mitigate the severity of speculative trading. That it, we expect trades for hedging considerations to dominate the postliberalization period, and therefore returns associated with such trades are most likely to reverse in the future, or at least experience a significant drop in the extent of return continuations that dominated the pre-liberalization period. Furthermore, the extent of return continuations or reversals depends on the degree of information asymmetry. That is, stocks with higher degree of information asymmetry (e.g. smaller and less widely followed stocks) are more apt to insider trading, and therefore their returns are more likely to exhibit a tendency for continuation following high-volume shocks as opposed to stocks with low degree of information asymmetry (e.g. large and widely followed stocks). This relationship is consistent with the view of Blume et al. (1994), who find that informational efficiency of a market implies that the magnitude of information-based trades should be positively correlated with the degree of information asymmetry. In China, where an improvement in market informational efficiency is anticipated as a result of market liberalization, we expect the extent of informed trading and the degree of information asymmetry to be positively correlated during the post-liberalization period.

4. METHODOLOGY To investigate the relationship between trading volume and stock returns in the Chinese equity markets, we use the two-stage regression analysis approach developed by Llorente et al.

asymmetry, i.e. small firms, have a tendency to continue in one market following high-volume days in the other market. 11

(2002). In the first stage, we estimate the coefficients of returns first-order autocorrelations and their interactions with trading volume innovations using the following time-series regressions: 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑅𝑖,𝑡 = 𝐶0𝑖 + 𝐶1𝑖 ∙ 𝑅𝑖,𝑡−1 + 𝐶2𝑖 ∙ 𝑉𝑖,𝑡−1 ∙ 𝑅𝑖,𝑡−1 + 𝑒𝑖,𝑡

(1)

𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑅𝑖,𝑡 = 𝐶0𝑖 + 𝐶1𝑖 ∙ 𝑅𝑖,𝑡−1 + 𝐶2𝑖 ∙ 𝑉𝑖,𝑡−1 ∙ 𝑅𝑖,𝑡−1 + 𝑒𝑖,𝑡

(2)

𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 where 𝑅𝑖,𝑡 and 𝑅𝑖,𝑡−1 are firm i return at time t and t-1, respectively, in SHSE. 𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑅𝑖,𝑡 and 𝑅𝑖,𝑡−1 are firm i return at time t and t-1, respectively, in SZSE. 𝑆𝐻𝑆𝐸 𝑆𝑍𝑆𝐸 𝑉𝑖,𝑡−1 and 𝑉𝑖,𝑡−1 are the volume innovation of firm i’s return at time 𝑡 -1 in the SHSE

and SZSE, respectively. The coefficient 𝐶0𝑖 is a constant, 𝐶1𝑖 is the firm’s return auto-correlation coefficient, and 𝐶2𝑖 is firm i’s volume-return interaction coefficient. The interpretation of 𝐶0𝑖 , 𝐶1𝑖 , and 𝐶2𝑖 in equation (1) and (2) are the same. To calculate the volume innovation, we use the stock’s daily turnover, calculated as the daily trading volume divided by the number of outstanding shares for each stock. Following Llorente et al. (2002)’s approach, we de-trend the daily turnovers to take into account the non-stationary characteristic of it. We do so by subtracting the 100-day moving average log-turnover after adding a small constant (0.00000255) to avoid problems with zero trading volumes as follows16: −1

𝑉𝑡 = 𝐿𝑜𝑔(𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟𝑡 ) − 1/100 ∑ log(𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟𝑡+𝑠 ) −100

where 𝐿𝑜𝑔(𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟𝑡 ) = 𝐿𝑜𝑔(𝑇𝑢𝑟𝑛𝑜𝑣𝑒𝑟 + 0.00000255) For each firm-quarter, equation 1 and 2 are estimated for individual stocks and the volumereturn interaction coefficients, 𝐶2 , are then averaged for each quarter. Theoretically, trading 16

We use different moving average (MA) windows to de-trend daily turnovers; 200-day, 150-day, 100-day and 50day. After de-trending, Dickey-Fuller test (ADF) reveals that only data de-trended using 100-day and 50-day MA windows are indeed stationary. To ensure that our inferences are not influenced by the length of the de-trending window, we conduct our whole experiment using a 50-day moving average window as well. Results obtained using the 50-day MA de-trending window are qualitatively similar to those reported in Section 6 . 12

volume encompasses both allocational and informational elements, and the lagged volume-return interaction coefficient, 𝐶2 , reveals the relative importance of one type relative to the other. This parameter is expected to be negative and statistically significant if trading in the stock is motivated by allocational considerations, and positive and statistically significant if motivated by informational considerations. However, if the trading in the stock is neither motivated by allocational nor informational considerations, then 𝐶2 should have a value that is close to zero and statistically insignificant. Furthermore, a monotonic relation should exist between the 𝐶2 and the extent of speculative trade relative to hedging trade. That is, we expect 𝐶2 to be positive (or negative with low magnitude) and statistically significant for firms with high level of information asymmetry, where speculative trading is more likely to occur. In contrast, 𝐶2 is expected to be negative (or positive with low magnitude) for firms with low level of information asymmetry, where speculative trading is less likely to occur. Accordingly, since our analysis of the extent of informed trading is positioned around the market labialization, we expect 𝐶2 to become negative (or remain positive but with lower magnitude than before) after market liberalization. In the second stage regression, using Fama and MacBeth’s (1973) approach17, we employ a cross-sectional regression to regress 𝐶2𝑖 coefficients of each firm-quarter collected from the first stage time-series regressions, on different measures of information asymmetry. This allows us to better understand the extent of informed versus uninformed trades to the degree of information asymmetry as follows: 𝐶2𝑖 = 𝑎 + 𝑏 ∙ 𝐴𝑖 + 𝑒𝑖

(3)

Here, 𝐴𝑖 is the degree of information asymmetry that we use two proxies for.18 Following Llorente et al. (2002), we use market capitalization and bid-ask spreads as proxies for

17

In the Fama and MacBeth (1973) approach, we first run time-series regressions and then use the coefficients obtained from the time-series regressions in the second stage cross-sectional regression, equation (3), by quarter across firms. Coefficients obtained from the cross-sectional regressions are then averaged for each quarter. To generate the standard errors for these estimates, the standard deviations of cross-sectional regression estimates are used (Gagnon and Karolyi, 2009). One advantage of this procedure is that it corrects for the cross-sectional correlation that might be ignored when panel regression is used. For more details about the two-stage Fama and MacBeth (1973) approach, please refer to Cochrane, 2001, p. 245-250. 18 In Section 7, we introduce a third proxy of information asymmetry. 13

information asymmetry. Market informational efficiency implies that the extent of informed trades should be positively correlated with the degree of information asymmetry, Blume et al. (1994). That is, 𝐶2 should exhibit a monotonically increasing relationship with the degree of information asymmetry. For informationally efficient markets, we expect 𝐶2 to be higher for small firms or firms with high bid-ask spreads, where more speculative (informative) trades are likely to dominate. Alternatively, we expect 𝐶2 to be lower for firms with large market capitalization or firms with low bid-ask spreads, where more hedging (non-informative) trades are likely to dominate.19 In China, where market liberalization is expected to improve the market informational efficiency, we should observe a monotonically increasing relationship between 𝐶2 and the degree of information asymmetry during post-liberalization period. 5. DATA Daily stock data from SHSE and SZSE are obtained from CSMAR (China Stock Market & Accounting Research) Database for the period between 1992 and 2012. Data obtained includes: stocks’ adjusted closing prices, trading volume, full number of shares, number of outstanding shares, full number of shares, and the market index adjusted closing prices. Figure (1) shows market index cumulative returns of SHSE and SZSE. Figure (2), on the other hand, shows daily returns of SHSE and SZSE market index. Returns’ volatility in both markets is very high prior 1996 but becomes more stable afterward. To avoid the period of time when both markets were experiencing a considerably high level of returns volatility, we eliminate the period between 1992 and 1995.20

[Insert Figure 1] [Insert Figure 2]

We are most interested in the impact of Chinese market liberalization on the dynamics of volume-return interactions and how they correlate with the degree of information asymmetry. Starting on November 2002, qualified foreign institutional investors (QFII) can access the local

19

To use the two information asymmetry proxies in the second stage cross-sectional analysis in a unified framework, we follow the ordinal transformation method used by Llorente et al. (2002). For more information about their transformation method, refer to Llorente et al. (2002) pp. 1021. 20 As we will see next, in order to have a balanced pre- and post-liberalization period, we included the 4th quarter of 1995 (1995q4). 14

Chinese market, i.e. the A-market. However, it was not until May 2003 when the first two QFII licenses were issued to UBS AG and Nomura Securities. By the end of the 2nd quarter of 2003, five financial institutions were QFII approved: UBS, Nomura Securities, Morgan Stanley, Citigroup Global, and Goldman Sachs. Therefore, we consider the second quarter of 2003 (2003q2) to be the liberalization window, and we design our analysis symmetrically around it.21 While the full period spans over 61 quarters (10/1/1995-12/31/2010), each one of our pre- and post-liberalization periods span over 30 quarters. The pre-liberalization period covers the period between 1995q4 and 2003q1 (10/1/1995-3/31/2003), while the post-liberalization period covers the period between 2003q3-2010q4 (7/1/2003-12/31/2010) for both markets, SHSE and SZSE. Table 1 provides descriptive statistics of all publicly traded firms in SHSE A-market for the full period as well as the pre- and post-liberalization periods. The table includes the average market capitalization (AvgCap), the average daily number of traded shares (AvgVol), the average daily turnover (AvgTurn), and the average share price (AvgPrc). The market capitalization is calculated daily by multiplying the number of outstanding shares for each firm times the adjusted closing prices. The average daily turnover is the number of traded shares daily divided by the number of outstanding shares. All share prices used are denominated in Chinese Yuan Renminbi (CNY). The entire sample is divided into three equal size groups based on firms’ quarterly market capitalization and bid-ask spreads. That is, for each quarter, firms are recognized as small, medium, and large based on their quarterly average market capitalization or bid-ask spreads, and ranked accordingly. Descriptive statistics of all sub-groups are presented in table 1 as well. Finally, similar to table 1, descriptive statistics for all publicly traded firms in SZSE and all sub-groups are presented in table 2 over the entire period as well as pre- and postliberalization periods. Our entire sample consists of 38,769 and 31,051 firm-quarters from SHSE and SZSE, respectively. In SHSE, average market capitalization (AvgCap) for firms in our sample ranges between CNY23 million and CNY7.1 trillion22, while in SZSE it ranges between CNY35 million and CNY239 billion. The average market capitalization for small, medium, and large firms are 442, 1,500, and 25,400 in SHSE and 532, 1,640, and 8,990 in SZSE, respectively. The average

21

In Section 7, we conduct further robustness tests by considering different liberalization windows, as well as preand post-liberalization periods. 22 PetroChina Co. (601857.SS) is the largest publicly held firm in China. Established in November 5, 1999, but very quickly reached its highest market capitalization of CNY7.12 trillion in November 5, 2007. 15

daily turnovers (AvgTurn), which is expected to increase with the firm size, in SHSE is 1.78% for the entire sample, and 1.73% for the small size group and 1.83% for the large size group. In SZSE, (AvgTurn) is 1.87% for the entire sample, and 1.81% for the small size group and 1.95% for the large size group. On the other hand, the average daily turnovers (AvgTurn) are 0.88%, 1.52%, and 2.64% in SHSE, and 1.03%, 1.85%, and, 2.78% in SZSE for firms with low, medium, and high bid-ask groups, respectively.

[Insert Table 1] [Insert Table 2] 6. EMPIRICAL RESULTS In table 3, we present results from equations 1 and 2. Our coefficient of interest is 𝐶2 which represents the volume-return interactions. For each firm-quarter, time-series equations 1 and 2 are estimated for individual stocks and the volume-return interaction coefficients, 𝐶2 , are then averaged for each quarter.23 As mentioned earlier, the sign and magnitude of 𝐶2 can be used to distinguish between informed and uninformed trades in SHSE and SZSE. If speculative or informational trades dominate the market, then returns associated with positive trading volume shocks are more likely to continue in the future. This means that returns associated with trading volume innovations are positively auto-correlated and therefore volume-return interaction coefficient, 𝐶2𝑖 , is positive on average. On the other hand, if hedging or non-informational trades dominate, then returns associated with high trading volume are more likely to reverse in the future. This means that returns associated with trading volume innovations are negatively autocorrelated and therefore volume-return interaction coefficient, 𝐶2𝑖 , is

negative on average.

Furthermore, if speculative trades dominate the Chinese market (𝐶2 is positive) prior the opening of the local market, market liberalization should mitigate the severity of these speculative activities (Ben Rejeb & Ben Salha, 2013; Chi, 2013; Kim & Singal, 2000a, 2000b; Nguyen, 2010). That is, we expect 𝐶2 to become negative, or remain positive but with lower magnitude than before, during post-liberalization period.

23

We estimated a total of 38,769 and 31,051 regressions for SHSE and SZSE, respectively. We use xtfmb command in STATA to perform Fama and MacBeth (1973) procedure. The code was developed by Hoechle (2011). For more details on the program, type “net search xtfmb” in STATA to install the ado file and then type “help xtfmb”. 16

We present results from SHSE in panel A and results from SZSE in panel B of the same table. For the full period in SHSE, 𝐶2 is negative on average (-0.01815) which implies that returns associated with high-volume shocks reverse themselves during the following period. That is, returns associated with positive trading shocks are motivated by hedging considerations, on average, during the whole period, 1995q4 to 2010q4. However, analysis of the pre- and postliberalization periods shows that the magnitude of informed trades diminishes significantly as a result of market-liberalization. The coefficient 𝐶2 is positive (0.00362) prior market liberalization, but negative (-0.04011) in the post-liberalization period. At the same time, only 51% of 𝐶2 estimated during the pre-liberalization period are negative. However, it increases to 65% during the post-liberalization period. In addition, the number of significant t-values remind relatively the same in both periods, 73% in per-liberalization period and 69% in postliberalization period.24 In panel B of the same table, we present results from SZSE. These results exhibit similar patterns to those from SHSE. The coefficient 𝐶2 is negative on average for the full period. Yet, while it is positive during the pre-liberalization period (0.00338), it becomes negative during the post-liberalization period (-0.03531). The percentage of negative 𝐶2 coefficients increased from 50% during the pre-liberalization period to 62% in the post-liberalization period. Results from the first stage regression show that whereas speculative trades dominate both markets during preliberalization period, trades motivated by hedging considerations dominate the post-liberalization period.

[Insert Table 3]

Next, we investigate the extent of speculative and hedging trades under different levels of information asymmetry. This should provide us with valuable information about potential improvement in informational efficiency as a result of market liberalization in China. As mentioned earlier, we use two measures of information asymmetry: market capitalization and bid-ask spreads. In table 4, results from the SHSE entire sample and the three size groups -small, medium, and large firms- are presented. Results are provided for the full period as well as the Further investigation of these results suggests that the number of negative and statistically significant 𝐶2 does increase significantly in post- liberalization period. 24

17

pre- and post-liberalization sub-periods. In this table we only include results of two coefficients; 𝐶1 and 𝐶2 . For the full period 𝐶2 is negative, on average. Volume-return interactions are not positively correlated with the degree of information asymmetry. The coefficient 𝐶2 for firms with small, medium, and large market capitalization are -0.00128, -0.02158, -0.01902, respectively. However, results from the pre- and post-liberalization periods reveal interesting results. While 𝐶2 is not monotonically increasing with the degree of information asymmetry during the pre-liberalization period, it is during the post-liberalization period. This implies that stock returns are more reflective of market fundamentals post market liberalization. We interpret this as an improvement in the informational efficiency of SHSE. The same findings are evident in results we obtain from the second stage cross-sectional regression, equation (3). Panel B in table 4 shows results attained from equation (3). Whereas b is not different from zero during preliberalization period, it is negative and statistically significant during the post-liberalization period. This means that the joint volume-return relation is positively correlated with the information asymmetry measure during the post-liberalization period but not in the preliberalization period.

[Insert Table 4] Results from SZSE are presented in table 5. These results show that whereas 𝐶2 monotonically increases with the degree of information asymmetry during the post-liberalization period, we do not observe the same relationship in the pre-liberalization period. Results from equation (3), presented in panel B of the same table, support this finding. They show that 𝐶2 is positively correlated with the degree of information asymmetry during the post-liberalization period but not in the pre-liberalization period. Similar to SHSE, these results imply an improvement in the overall market informational efficiency of SZSE as a result of market liberalization.

[Insert Table 5] In figure 3, we develop a quarterly-based “Trade Informativeness” index that shows the extent of information-based trading to the degree of information asymmetry. To do so, for each 18

quarter, we collect the average of 𝐶2 coefficients estimated from our model for firms with small, medium, and large market capitalization. We then track the movement of these coefficients over time to learn more about the evolution of the magnitude of informed trades and how they correlate with the degree of information asymmetry. The visual representation of this relationship can help identify periods of time in which more informed or uninformed trading is more dominant. In addition, plotting the magnitudes of volume-return interactions according to the degree of information asymmetry can help detect if stock returns reflect market fundamentals or not. According to Blume et al. (1994), informational efficiency of a market implies that the magnitude of information-based trades should be positively correlated with the degree of information asymmetry. That is, smaller and less widely followed stocks in SHSE and SZSE have greater uncertainty, and are more affected by private than public information. Therefore, they are more likely to exhibit return continuations, positive 𝐶2 , on average. On the contrary, larger and more followed stocks have lower uncertainty, and are less affected by private than public information. Therefore, they are more likely to exhibit return reversals, negative 𝐶2 , on average. Prior to 2003q2, figure 3 shows that a monotonic relationship between 𝐶2 and market capitalization did not exist. However, a positive and monotonic relationship between the two variables is evident, on average25, during the post-liberalization period.26

[Insert Figure 3] Similar to other indexes, we acknowledge some of the limits of our “Trade Informativeness” index. First, while this index can be applicable for the whole market, using it for one firm or a small group of firms may well be meaningless. A single firm returns are prone to many variables. 25

As mentioned earlier, we rank firms quarterly based on their average market capitalization. Thus, fluctuations in stock prices may result in ranking biasness, especially for firms at the top or lower end of each size group. For example, large-size firms, but at the lower end of their size group, may be categorized as mid-size firms the next quarter due to a drop in their stock prices. This miss-classification may result in a biased index. To draw further distinction between all size groups, we divide our sample into 5 size groups -not only 3 as we did initially- according to their quarterly market capitalization. Then, we drop the 2nd and 4th quantiles, calculate the quarterly means of C2 coefficients for the 1st, 3rd, and 5th quantiles, and plot the “trade informativeness” index for these groups. The postliberalization monotonic relationship between the extent of informed trading, C2 , and information asymmetry is unambiguously clear in the new figures. All results and figures are available upon request. 26 While this conclusion was made earlier based on results presented in tables 4 and 5, we find it very difficult to know how this relationship vary from one period to another without calculating the quarterly average of C2 for each size group and viewing it over time. 19

Therefore, volume-return relationship for a single firm is likely not to reflect the actual magnitude of informed trading for its stock. Likewise, for a small group of firms, a high magnitude of informed trading for one or two biased firms might impede the index from reflecting the actual overall magnitude of informed trading. However, when a large number of firms are pooled together, the index seems to perform well. Second, the index developed in this study is a quarterly-based index, and therefore it might be more useful for market observers than market participants. As mentioned earlier, the index shows the extent of informed trading relative to uninformed trading for each quarter using daily stock returns and trading volume. Quarterly observations of informed trading might not be very useful for market participants who require data with higher frequency.27 Similar to table 4 and 5, we present results from SHSE and SZSE in tables 6 and 7, using bid-ask spreads as a proxy for information asymmetry. In table 6, 𝐶2 is monotonically increasing with the degree of information asymmetry. The same pattern seems to hold in pre- and postliberalization periods. Results in panel B support those presented in panel A. The coefficient 𝐶2 is positively correlated with the degree of information asymmetry during pre- and postliberalization periods. Similar to table 6, results from SZSE are presented in table 7. In panel A, we observe a monotonically increasing relationship between 𝐶2 and the degree of information asymmetry during pre- and post-liberalization periods, and the same conclusion can be drawn from panel B.28 These results are inconsistent with those from table 4 and 5, where we show that 𝐶2 is monotonically increasing with information asymmetry, using market capitalization as a proxy, during the post-liberalization period but not in pre-liberalization period. While we fail to provide an explanation for this conflicting results, in the following section we consider a third measure of information asymmetry, i.e. State-Owned Enterprises (SOEs) Ratio.29 Results from

27

One way of alleviating this barrier is by estimating our models on a monthly basis rather than a quarterly basis. However, by doing so we may lose the meaningfulness of our 𝐶2 coefficients, since we only have around 21 trading days a month versus 63 trading days a quarter, on average. 28 Although the correlation between the extent of informed trading and information asymmetry (measured by bid-ask spreads) is positive and statistically significant during the pre- and post-liberalization periods, the magnitude of this correlation as well as the significance level are much higher in post-liberalization period. This can interpreted as an improvement in monotonicity of this relationship as a result of market liberalization. 29 Despite the intuitive relationship between bid-ask spreads and the degree of information asymmetry, many empirical studies have found that this relationship does not always persist (Liu and Wang, 2016). For example, several studies have found that bid-ask spreads are indeed decreasing with the degree of information asymmetry (Brooks, 1996; Huang and Stoll, 1997; Acharya and Johnson, 2007); the so-called bid-ask spreads puzzle. While this is not the case in our study, it may suggest that bid-ask spreads is not a superior proxy for information asymmetry. 20

the third proxy of information asymmetry should provide us with a conclusive evidence on whether this monotonically positive relationship exists in either pre- and post-liberalization periods or only post-liberalization period.

[Insert Table 6] [Insert Table 7]

Using bid-ask spreads as a proxy for information asymmetry, we depict the dynamics of volume-return interactions and their extent to the degree of information asymmetry in figure 4. We graph quarterly 𝐶2 collected from equation (1) and (2) for firms with low, medium, and large bid-ask spreads. Consistent with our findings in tables 6 and 7, a positive monotonic correlation between 𝐶2 and the degree of information asymmetry exists during both pre- and postliberalization periods, but with higher degree of uniformity in the post-liberalization period. Furthermore, it shows decline in speculative trades, on average, for all three groups in both markets as a result of market liberalization.

[Insert Figure 4] Finally, when examining the U.S. equity markets, Llorente et al. (2002) estimated 𝐶2 to be 0.03027, 0.00485, and 0.000719 for firms with small, medium, and large market capitalization, respectively. Also, using bid-ask spreads as a proxy for information asymmetry, their estimates for 𝐶2 are -0.00281, 0.00317, and 0.03549 for stocks with low, medium, and high bid-ask spreads, respectively. Comparing their findings with those obtained from our post-liberalization period, we find that, similar to the U.S. equity market, a positive and monotonic relationship between the extent of informed trading and the degree of information asymmetry is present in the Chinese equity markets.30 We also find that whereas informed trading, on average, dominates the U.S. equity market, uninformed trading dominates the Chinese equity market during postliberalization period. 30

We recognize that such comparison is inequitable since it covers different periods of time, and therefore we do not draw any conclusions from it. However, comparing the magnitudes of these coefficients provides the reader with a good insight on how effective liberalization of the Chinese equity market in alleviating the intensity of informed trading. 21

In China, retail investors control the vast majority of equity markets, while institutional investors control a small portion of it.31 Most of those retail investors are novice traders with very limited investment opportunities, and therefore are continuously speculating in the market (Mei et al., 2003). For the most part, their speculations are based on rumors rather than fundamentals (Chan et al., 2008). At the same time, institutional investors in China seem to possess private information which allow them to outperform the market, but recent market reforms and regulations on private trading have led to a significant decay in their performance (Chi 2013, 2014). All this have given rise to uninformed trades by retail investors who seem to be still trading on rumors according to our results.32 Furthermore, it is worth noting that our conclusion about the improved informational efficiency in the Chinese stock markets is based on the main two findings of this study, i.e. the erosion of informed trading and the presence of a positive and monotonic relationship between the extent of informed trading and the degree of information asymmetry during the postliberalization period. Yet, the dominance of uninformed or noise trading during postliberalization period may suggest otherwise.33 The net impact of adding noise traders to a stock market is ambiguous (Tetlock, 2007). Adding uninformed traders to a stock market is likely to improve informational efficiency, as uninformed traders motivate informed traders to trade more aggressively on their information and therefore continue acquiring better information. Therefore, theoretical frameworks like the ones developed by Grossman and Stiglitz (1980) and Kyle (1985) suggest that adding more noise traders is likely to enhance the informational efficiency of the stock market. On the other hand, trades by informed investors may not offset those of noise traders in the market due to their limited ability to arbitrage. That is, informed traders may reinforce demand shocks caused by noise traders knowing that stocks’ mispricing is most likely to continue in the short run (DeLong et al., 1990). This prediction suggests that adding more noise traders is likely to harm the informational efficiency of the stock market. Findings from our study support the

31

As of 2003, individual investors own more than 87% of total stock market capitalization (Chi, 2014). We do not rule out other interpretations to the dominance of uninformed trades in the Chinese market, but this interpretation seem to be logical given what other studies have founded. 33 The term “Noise Trading” is used in this study to refer to investors who are not trading on market fundamentals. Instead, they follow trends “rumor-based trading”. Noise traders also are known for over-reacting to good and bad news. 32

22

theoretical prediction of Kyle (1985)’s model, but not that of Grossman and Stiglitz (1980) and Kyle (1985). It seems that although the Chinese market is dominated by uninformed or noise trading during post-liberalization period, stock returns are more reflective of market fundamentals relative to the pre-liberalization period. While it is beyond the scope of this study, the impact of noise traders on market informational efficiency is a complex and controversial topic that requires further research. 7. ROBUSTNESS TESTING In this section we perform a series of robustness tests to confirm our findings concerning the magnitude of informed trades and how they correlate with information asymmetry measures in SHSE and SZSE. We start by adjusting pre- and post-liberalization periods. First, we consider an extended liberalization window. That is, in our initial analysis we study the period from 1995q4 to 2010q4, while considering 2003q2 to be the quarter in which market liberalization took place. We did so because by the end of June 2003 five foreign financial institutions were granted the QFII status and had an access to the A-market. In our first robustness test, we extend the liberalization window by including the 3rd quarter of 2003, which allows for more openness of the local market. That is, our full period covers the period between 1995q4 and 2011q1, and the pre- and post-liberalization periods are 1995q4-2003q1 and 2003q4-2011q1, respectively.34 Secondly, to avoid any potential influence of the B-market liberalization that started in February 2001, we further extend our liberalization window to cover the period between 2001q1 and 2003q2.35 Pre- and post-liberalization periods of 21 quarters are considered. So, under the second robustness test, the pre- and post-liberalization periods span over the periods 1995q42000q4 and 2003q3-2008q3, respectively. Thirdly, to avoid any potential influence of the 2007 subprime financial crisis that started in 2007q4, we re-estimate our models over the period from 1999q1 to 2007q3.36 The pre- and post-liberalization periods span over a period of 17 quarters, instead of 30 quarters in the initial test. The pre- and post-liberalization periods of our third

34

Similarly, we further extend the labialization window to include the period between 2002q3 and 2003q2, which covers all the reforms related to the opening of the A-market. The results are available upon request. 35 It is very well expected that with the starting of market liberalization that local investors are still confused about its impact. 36 The full period is shortened in order to maintain a balanced pre- and post-liberalization periods. 23

robustness test are 1999q1-2003q1 and 2003q3-2007q3, respectively. Results obtained from these three robustness tests are very similar to those obtained from the initial test.37 Lastly, we use an additional proxy for information asymmetry to provide a conclusive evidence on the relationship between 𝐶2 and the degree of information asymmetry around market liberalization. In the previous section, using market capitalization as a proxy for information asymmetry, our results suggest that the extent of informed trading is positively correlated with the degree of information asymmetry only during the post-liberalization period, but not during the pre-liberalization period. However, when using bid-ask spreads as a proxy for information asymmetry, we find that the same relationship is present in both pre- and post-market liberalization periods. Therefore, we could not reach a conclusive result concerning the relationship between the extent of informed trading and the degree of information asymmetry around market liberalization. To reconcile these conflicting results and provide a conclusive evidence, we use an additional proxy for information asymmetry, i.e. State-Owned Enterprises (SOEs). In China, firms can be wholly or partially owned by the Chinese government. Firms with high SOE ratios are shown to be operationally inefficient, suggesting that they have higher degree of information asymmetry as oppose to firms with low SOE ratios (Fisman & Wang, 2011; Malkiel, 2007). Chi (2014) finds that more speculative trades are likely to exist in Chinese firms with high SOE ratio. Accordingly, we use SOE as a third proxy for information asymmetry. We calculate SOEs’ ratios by dividing the number of outstanding shares over the full number of shares using daily data. We then calculate the quarterly averages for each firm and use them to divide the entire sample into three equal sub-groups based on their SOEs’ ratios: small, medium, and large. Descriptive statistics for the SOEs from SHSE and SZSE are presented in table 8. We start this robustness test by performing similar empirical analysis to the initial one discussed in Section 4. Tables 9 and 10 provide summary statistics for equations 1-3 from SHSE and SZSE, respectively. While 𝐶2 is not monotonically increasing with degree of information asymmetry during the pre-liberalization period in both markets, it is during the post-liberalization period. Panel B in tables 9 and 10 support this finding by showing an insignificant correlation between 𝐶2 and SOEs ratios during the pre-liberalization period, but significantly positive correlation during the post-liberalization period. These results provide a conclusive evidence in favor of a 37

Results are available upon request. 24

monotonic and positive relationship between 𝐶2 and information asymmetry only during the post-liberalization period, suggesting that stock returns are more reflective of market fundamentals during that period.

[Insert Table 8] [Insert Table 9] [Insert Table 10]

Finally, Individual Chinese stock returns are found to be highly correlated with the market returns rather than firm-specific information (Mei et al., 2003). Therefore, we include market index daily returns as control variables in our time-series regressions in order to eliminate the impact of high synchronicity between individual stock returns and market-wide returns on our results as follows. 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑅𝑖,𝑡 = 𝐶0𝑖 + 𝐶1𝑖 ∙ 𝑅𝑖,𝑡−1 + 𝐶2𝑖 ∙ 𝑉𝑖,𝑡−1 ∙ 𝑅𝑖,𝑡−1 + 𝛽𝑖,𝑆𝐻𝑆𝐸 𝑅M,𝑡 + 𝑒𝑖,𝑡

(1)′

𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑅𝑖,𝑡 = 𝐶0𝑖 + 𝐶1𝑖 ∙ 𝑅𝑖,𝑡−1 + 𝐶2𝑖 ∙ 𝑉𝑖,𝑡−1 ∙ 𝑅𝑖,𝑡−1 + 𝛽𝑖,𝑆𝑍𝑆𝐸 𝑅M,𝑡 + 𝑒𝑖,𝑡

(2)′

𝑆𝐻𝑆𝐸 𝑆𝑍𝑆𝐸 where 𝑅M,𝑡 and 𝑅M,𝑡 are SHSE and SZSE market index returns at time t.

The same initial analysis- presented in Section 4- is then conducted using the modified models. Results obtained from the modified models are very similar to those obtained from the initial models.38 Furthermore, using the modified models we perform the same robustness tests presented in this section and fail to find any significant changes in our results.39 8. CONCLUSION In this study we examine whether and to what extent has Chinese market liberalization mitigated the severity of speculative (informed) trading that previously dominated the market. Our analysis is based on the dynamic relationship between trading volume and stock returns’ autocorrelation proposed by Llorente et al. (2002). They argue that returns are more likely to 38 39

Results are available upon request. Minor changes are observed in the results, but will not change the main conclusion of our study. 25

continue themselves if motivated by speculative (informational) trades, but reverse themselves if motivated by hedging (non-informational) trades. Furthermore, the extent of return continuations or reversals depends on the degree of information asymmetry. Using a comprehensive sample of all publicly traded firms in SHSE and SZSE between 1996 and 2010, we find that market liberalization has mitigated the severity of speculative trading. We also find some evidence that a positive and monotonic relationship between the extent of informed trading and the degree of information asymmetry measures now exists after market liberalization. Both findings suggest an improvement in the informational efficiency of the Chinese equity market. Therefore, it does not deserve its reputation as a “Casino” anymore. Furthermore, rather than merely aggregating the model coefficients before and after market liberalization, we develop a “Trade Informativeness” index that can more effectively tracks quarterly variations in the magnitude of informed trades. This index demonstrates the relative significance of informational trades versus non-informational trades. It can also be used as a reference for market informational efficiency; when the magnitude of informed trading is presented according to its degree of information asymmetry. The main policy implication from our results is related to the Chinese authorities’ efforts in liberalizing the equity market. It seems that these efforts have paid off, and now stock returns are more reflective of market fundamentals and the extent of informed trading has eroded. However, as discussed earlier, the net impact of the post-liberalization prevalence of noise trading on market efficiency is unclear, especially on the long-run. Thus, the need for more sophisticated investors is essential in an emerging market like China. The presence of such traders provides the necessary balance to offset noise trades and further enhance information gathering, processing, and sharing, which in turn will further enhance the market informational efficiency. This can be achieved by promoting mutual fund ownership who are shown to be more sophisticated and influential than local traders in the China (Ding et al., 2013). The Chinese authority can also promote the presence of sophisticated traders in the market by allowing qualified foreign institutional investors (QFIIs) to freely access the local Chinese equity market.40 This is likely to result in higher integration with the world market, which will lower stock price synchronicity, and therefore alleviate the extent of noise trades in the local market

40

QFII are facing many investment constraints imposed by quotas that limit their ability to manage or diversify their investments (Korkeamäki et al., 2015). 26

(Hsin and Tseng, 2012). Finally, although not an objective of this paper, but our findings provide supporting evidence to studies concerned with short-horizon return autocorrelations in capital markets, especially emerging markets like China.

ACKNOWLEDGEMENTS The authors would like to thank the editor and the anonymous reviewers’ suggestions for further completing the paper. And the authors also express appreciation to Fundamental Research Funds for the Central Universities (No: 106112016CDJXY020012).

27

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Figure 1: Market Cumulative Returns for Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE)

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

0

1

2

3

4

5

6

SHSE Market Index Cumulative Returns

Date

Date

31

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

-1

0

1

2

3

SZSE Market Index Cumulative Returns

Figure 2: Market Daily Returns for Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE)

.2 0

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

-.4

-.2

Market Returns

.4

SHSE: Daily Market Returns

Date

.2 0

Date

32

2012

2010

2008

02006

2004

2002

2000

1998

1996

1994

1992

-.2

Market Returns

.4

SZSE: Daily Market Returns

Figure 3: Trade Informativeness Index for Small, Medium, and Large Market Capitalization in SHSE and SZSE

-.1

0

.1

.2

SHSE: Dynamics of Volume-Return Interactions with Market Capitalization

2010q3

2009q1

2007q3

2006q1

2004q3

2003q1

2001q3

2000q1

1998q3

1997q1

1995q3

-.2

Market Liberalization 2003q2

Date Small Large

Medium

Date Small Large

Medium

33

2010q3

2009q1

2007q3

2006q1

2004q3

2003q1

2001q3

2000q1

1998q3

1997q1

Market Liberalization 2003q2

1995q3

-.15

-.1

-.05

0

.05

.1

SZSE: Dynamics of Volume-Return Interactions with Market Capitalization

Figure 4: Trade Informativeness Index for Firms with Low, Medium, and High Bid-Ask Spreads in SHSE and SZSE

-.2

0

.2

SHSE: Dynamics of Volume-Return Interactions with Bid-Ask Spreads

2010q3

2009q1

2007q3

2006q1

2004q3

2003q1

2001q3

2000q1

1998q3

1997q1

1995q3

-.4

Market Liberalization 2003q2

Date Low High

Medium

-.2

0

.2

SZSE: Dynamics of Volume-Return Interactions with Bid-Ask Spreads

Date Low High

Medium

34

2010q3

2009q1

2007q3

2006q1

2004q3

2003q1

2001q3

2000q1

1998q3

1997q1

1995q3

-.4

Market Liberalization 2003q2

Table 1: Shanghai Stock Exchange (SHSE) Descriptive Statistics The table provides summary statistics for all publicly traded stocks in Shanghai Stock Exchange (SHSE) over the full period (10/1/1995-12/31/2010) as well as the pre- and post-liberalization periods (10/1/1995-3/31/2003 and 7/1/2003-12/31/2010, respectively). AvgCap is the average daily market capitalization (the number of outstanding shares multiplied by the daily adjusted closing price). AvgVol is the average daily number of traded shares. AvgTurn is the average daily turnover (number of traded shares daily divided by the number of outstanding shares). AvgPrc is the average daily stock price denominated in Chinese Yuan. Firm-Quarters represents the total number of firm-quarters data for the entire sample as well as each one of the sub-samples. To divide the full sample into three size groups, the average quarterly market capitalization for each firm is calculated using daily data and used to divided the full sample evenly into three groups; small, medium, and large. Similarly, to divide the sample into three groups based on their bid-ask spreads, for each quarter firms are ranked based on their daily average bid-ask spread into three equal groups; low, medium, and high. All prices are denominated in Chinese Yuan. Daily closing prices, trading volume, number of outstanding shares are obtained from China Stock Market & Accounting Research Database (CSMAR). SHSE

Entire Sample

Small Firms

Medium Firms

Large Firms

Low Bid-Ask

Medium Bid-Ask

High Bid-Ask

Mean Std. Dev. Min Median Max Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters

Full Period AvgCap (CNY Millions) 7,500 65,800 23 1,170 7,120,000 38,769 442 190 438 12,923 1,500 538 1,380 12,923 25,400 126,000 6,080 12,923 4,790 53,500 738 12,923 4,300 47,800 934 12,923 10,300 79,300 1,850 12,923

AvgVol

AvgTurn

AvgPrc

(‘000)

(%)

(CNY)

5,744 17,900 0 1,540 4,170,000 38,769 1,066 1,712 524 12,923 3,394 5,093 1,670 12,923 15,400 32,100 7,610 12,923 3,212 15,100 753 12,923 4,084 14,300 1,088 12,923 6,640 17,000 2,424 12,923

1.78% 2.43% 0.00% 1.05% 91.44% 38,769 1.73% 2.83% 0.97% 12,923 1.76% 2.36% 1.05% 12,923 1.83% 1.87% 1.15% 12,923 0.88% 1.02% 0.56% 12,923 1.52% 1.72% 1.00% 12,923 2.64% 3.47% 1.62% 12,923

11.05 8.66 0.9 9.08 294.17 38,769 8.64 4.71 7.72 12,923 10.41 5.99 9.04 12,923 15.28 13.22 11.82 12,923 6.32 3.33 5.62 12,923 9.68 4.22 8.91 12,923 16.71 11.68 13.98 12,923

Pre-Liberalization AvgCap AvgVol (CNY (‘000) Millions) 1,020 1,353 1,070 4,113 40 0 754 566 20,000 1,300,000 13,329 13,329 452 874 194 1,526 458 419 4,443 4,443 1,350 1,602 467 3,746 1,220 712 4,443 4,443 4,600 4,591 2,390 14,300 3,760 1,792 4,443 4,443 1,110 929 1,210 4,091 812 364 4,443 4,443 959 1,086 950 3,953 723 507 4,443 4,443 1,040 1,940 1,110 4,240 759 878 4,443 4,443

35

AvgTurn

AvgPrc

(%)

(CNY)

1.79% 3.07% 0.00% 0.86% 91.44% 13,329 1.23% 2.12% 0.57% 4,443 1.45% 2.70% 0.70% 4,443 2.12% 3.37% 1.06% 4,443 0.63% 0.82% 0.39% 4,443 1.29% 1.65% 0.79% 4,443 3.13% 4.46% 1.75% 4,443

12.51 5.94 1.89 11.4 92 13,329 11.17 4.57 10.38 4,443 13.79 6.53 12.55 4,443 15.94 9.18 13.7 4,443 8.89 3.62 8.26 4,443 11.62 4.31 10.96 4,443 15.92 6.93 14.5 4,443

Post- Liberalization AvgCap AvgVol (CNY (‘000) Millions) 10,100 6,840 79,400 19,400 23 0 1,580 2,461 7,120,000 4,040,000 24,729 24,729 425 1,253 186 1,848 410 660 8,243 8,243 1,590 4,149 558 5,079 1,500 2,583 8,243 8,243 26,600 14,400 132,000 30,700 6,360 7,147 8,243 8,243 6,410 4,184 64,100 17,600 699 1,115 8,243 8,243 7,250 6,687 65,400 18,800 1,430 2,614 8,243 8,243 16,700 9,864 103,000 21,200 4,080 4,744 8,243 8,243

AvgTurn

AvgPrc

(%)

(CNY)

1.66% 1.96% 0.00% 1.05% 76.06% 24,729 1.56% 2.17% 0.90% 8,243 1.63% 1.77% 1.07% 8,243 1.78% 1.91% 1.19% 8,243 0.99% 1.09% 0.66% 8,243 1.73% 1.75% 1.22% 8,243 2.31% 2.54% 1.55% 8,243

10.09 9.91 0.9 7.47 294.17 24,729 6.04 3.25 5.31 8,243 7.99 4.29 7.09 8,243 15.72 14.15 11.9 8,243 5.2 2.52 4.73 8,243 8.03 3.4 7.44 8,243 17.3 14.03 13.53 8,243

Table 2: Shenzhen Stock Exchange (SZSE) Descriptive Statistics The table provides summary statistics for all publicly traded stocks in Shenzhen Stock Exchange (SZSE) over the full period (10/1/1995-12/31/2010) as well as the pre- and post-liberalization periods (10/1/1995-3/31/2003 and 7/1/2003-12/31/2010, respectively). AvgCap is the average daily market capitalization (the number of outstanding shares multiplied by the daily adjusted closing price). AvgVol is the average daily number of traded shares. AvgTurn is the average daily turnover (number of traded shares daily divided by the number of outstanding shares). AvgPrc is the average daily stock price denominated in Chinese Yuan. Firm-Quarters represents the total number of firm-quarters data for the entire sample as well as each one of the sub-samples. To divide the full sample into three size groups, the average quarterly market capitalization for each firm is calculated using daily data and used to divided the full sample evenly into three groups; small, medium, and large. Similarly, to divide the sample into three groups based on their bid-ask spreads, for each quarter firms are ranked based on their daily average bid-ask spread into three equal groups; low, medium, and high. All prices are denominated in Chinese Yuan. Daily closing prices, trading volume, number of outstanding shares are obtained from China Stock Market & Accounting Research Database (CSMAR). SZSE

Entire Sample

Small Firms

Medium Firms

Large Firms

Low Bid-Ask

Medium Bid-Ask

High Bid-Ask

Mean Std. Dev. Min Median Max Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters

Full Period AvgCap (CNY Millions) 3,040 7,350 35 1,190 239,000 31,051 532 235 523 10,350 1,640 481 1,550 10,350 8,990 12,800 4,930 10,350 1,470 3,070 802 10,350 2,270 4,870 1,110 10,350 5,580 11,200 2,490 10,350

AvgVol

AvgTurn

AvgPrc

(‘000)

(%)

(CNY)

40,88 8,684 0.1 1,467 970,000 31,051 1,327 2,112 647 10,350 3,435 4,786 1,821 10,350 9,526 14,700 5,038 10,350 2,653 6,405 890 10,350 4,114 8,671 1,482 10,350 5,603 10,400 2,320 10,350

1.87% 2.56% 0.00% 1.11% 94.10% 31,051 1.81% 3.10% 1.00% 10,350 1.80% 2.18% 1.14% 10,350 1.95% 1.91% 1.24% 10,350 1.03% 1.19% 0.65% 10,350 1.85% 2.03% 1.24% 10,350 2.78% 3.61% 1.70% 10,350

12.42 11 0.68 9.46 279.51 31,051 8.71 5.12 7.68 10,350 11.44 7.16 9.51 10,350 19.87 16.94 14.83 10,350 6.42 3.51 5.73 10,350 10.44 4.63 9.52 10,350 21.05 15.13 16.8 10,350

Pre-Liberalization AvgCap AvgVol (CNY (‘000) Millions) 1,020 1,429 1,260 2,887 62 0 764 640 34,900 215,000 11,134 11,134 554 1,101 229 1,937 555 518 3,712 3,712 1,460 1,799 411 3,317 1,350 850 3,711 3,711 4,900 3,691 4,060 7,167 3,590 1,567 3,711 3,711 1,020 835 994 1,469 810 428 3,712 3,712 969 1,310 1,220 2,454 736 636 3,711 3,711 1,100 2,281 1,550 4,215 763 1,065 3,711 3,711

36

AvgTurn

AvgPrc

(%)

(CNY)

1.94% 3.29% 0.00% 0.90% 94.10% 11,134 1.20% 1.89% 0.60% 3,712 1.47% 2.46% 0.73% 3,711 2.22% 3.66% 1.05% 3,711 0.71% 0.94% 0.43% 3,712 1.64% 2.16% 0.97% 3,711 3.79% 5.14% 2.16% 3,711

12.23 6.49 1.95 10.89 116.12 11,134 11.19 5.13 10.26 3,712 13.46 7.16 12.01 3,711 19.04 11.92 16.28 3,711 6.89 3.89 7.99 3,712 11.78 4.59 11.04 3,711 16.95 8.28 15.06 3,711

Post- Liberalization AvgCap AvgVol (CNY (‘000) Millions) 4,270 5,703 9,060 10,500 35 0 1,950 2,544 239,000 970,000 19,441 19,441 502 1,637 241 2,306 471 883 6,480 6,480 1,750 4,407 487 5,231 1,710 2,762 6,481 6,481 9,260 9,900 13,100 15,000 5,100 5,377 6,480 6,480 1,700 3,606 3,710 7,676 798 1,450 6,480 6,480 3,420 6,570 6,360 11,100 1,910 3,378 6,481 6,481 7,630 7,120 13,000 11,900 3,760 3,296 6,480 6,480

AvgTurn

AvgPrc

(%)

(CNY)

1.84% 2.03% 0.00% 1.24% 81.28% 19,441 1.65% 2.22% 0.96% 6,480 1.85% 1.90% 1.29% 6,481 2.00% 1.97% 1.43% 6,480 1.21% 1.28% 0.82% 6,480 2.03% 1.92% 1.50% 6,481 2.32% 2.51% 1.56% 6,480

12.62 13.02 0.68 8.41 279.51 19,441 5.58 3.05 4.90 6,480 10.31 6.95 8.31 6,481 19.94 17.22 14.75 6,480 5.27 2.69 4.78 6,480 9.31 4.38 8.37 6,481 22.96 17.07 18.08 6,480

Table 3: The Dynamics of Volume-Return Interactions for SHSE and SZSE This table summarizes the coefficients 𝐶0 , 𝐶1 and 𝐶2, t-statistics, 𝑅2 , and F-test results of equations (1) and (2) 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑆𝐻𝑆𝐸 𝑅𝑖,𝑡 = 𝐶0𝑖 + 𝐶1𝑖 ∙ 𝑅𝑖,𝑡−1 + 𝐶2𝑖 ∙ 𝑉𝑖,𝑡−1 ∙ 𝑅𝑖,𝑡−1 + 𝑒𝑖,𝑡

(1)

𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑆𝑍𝑆𝐸 𝑅𝑖,𝑡 = 𝐶0𝑖 + 𝐶1𝑖 ∙ 𝑅𝑖,𝑡−1 + 𝐶2𝑖 ∙ 𝑉𝑖,𝑡−1 ∙ 𝑅𝑖,𝑡−1 + 𝑒𝑖,𝑡

(2)

The equations examine returns’ auto-correlation (𝐶1 ) and volume-return interactions (𝐶2 ) for all locally traded stocks in SHSE and SZSE. Results are provided for the full period as well as two sub-periods. The full period spans over 61 quarters, between 1995q4 and 2010q4. While the pre- and post-liberalization periods span over the periods 1995q4-2003q1 and 2003q3 and 2010q4, respectively. We use daily returns and estimate the time-series regression for each firm-quarter. In Panel A, we present results from SHSE and results from SZSE are presented in Panel B. The table also includes the number of quarters for which the coefficients are negative, N, the percentage of negative coefficients relative to the total number of coefficients, %N, and the total number of quarters for which we ran regressions over. In addition, we list the mean of t-statistics for each of the coefficients along with number of tstatistics that are statistically significant at the 10% level. Panel A: SHSE Statistic All 1995:q42010:q4 Pre1995:q42003:q1 Post2003:q32010:q4

Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters

𝑪𝟎 #<0 -0.06294 18,997 49% 38,769 -0.03537 6,531 49% 13,329 -0.09225 11,870 48% 24,729

𝑪𝟏 #<0 0.04704 15,508 40% 38,769 0.02031 6,265 47% 13,329 0.07388 7,913 32% 24,729

𝑪𝟐 #<0 -0.01815 22,486 58% 38,769 0.00362 6,798 51% 13,329 -0.04011 16,074 65% 24,729

𝒕𝑪𝟎 |#|>1.64 -1.55 26,751 69% 38,769 -1.50 8,531 64% 13,329 -1.17 17,805 72% 24,729

𝒕𝑪𝟏 |#|>1.64 16.56 18,221 47% 38,769 4.73 6,931 52% 13,329 19.93 11,128 45% 24,729

𝒕𝑪𝟐 |#|>1.64 -10.26 25,200 65% 38,769 1.43 9,730 73% 13,329 -16.6 17,063 69% 24,729

𝑹𝟐

F-stat.

0.0454

99.12

0.0360

9.89

0.0553

158.42

𝑹𝟐

F-stat.

0.0270

120.31

0.0315

28.12

0.0228

147.16

Panel B: SZSE Statistic All 1995:q42010:q4 Pre1995:q42003:q1 Post2003:q32010:q4

Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters

𝑪𝟎 #<0 0.00054 14,345 46% 31,051 0.00076 5,241 47% 11,134 0.00039 8,757 45% 19,441

𝑪𝟏 #<0 0.04887 12,479 40% 31,051 0.03403 5,038 45% 11,134 0.06354 6,842 35% 19,441

𝑪𝟐 #<0 -0.01593 17,463 56% 31,051 0.003381 5,561 50% 11,134 -0.03531 12,141 62% 19,441

37

𝒕𝑪𝟎 |#|>1.64 1.58 23,909 77% 31,051 1.57 7,571 68% 11,134 0.78 15,747 81% 19,441

𝒕𝑪𝟏 |#|>1.64 15.37 11,799 38% 31,051 6.69 5,122 46% 11,134 16.21 8,554 44% 19,441

𝒕𝑪𝟐 |#|>1.64 -7.44 20,494 66% 31,051 1.13 7,571 68% 11,134 -11.62 11,470 59% 19,441

Table 4: The Extent of Volume-Return Interactions to Information Asymmetry measured by Market Capitalization in SHSE This table summarizes the dynamic relationship between volume-return interactions and the level of information asymmetry in SHSE. Firms’ market capitalization is used as a proxy for information symmetry. In panel A, we only present the returns’ auto-correlation (𝐶1 ) and volume-return interactions coefficients (𝐶2 ), and 𝑅2 and F-test results from equations (1) and (2). Results are provided for the full period and pre- and post-liberalization periods. The table also includes the number of quarters for which the coefficients are negative, N, the percentage of negative coefficients relative to the total number of coefficients, %N, and the total number of quarters for which we ran regressions over. According to their average quarterly market capitalization, firms in SHSE are divided into three size groups; small, medium, and large. Results from the full period and the two sub-periods are provided for the entire sample as well as the three size groups. In panel B, we present results from equation (3); 𝐶2𝑖 = 𝑎 + 𝑏 ∙ 𝐴𝑖 + 𝑒𝑖 . Estimates of the coefficient b from the full period are presented as well as the preand post-liberalization periods. We also provide t-statistics and 𝑅2 for the same periods. Panel A SHSE Mkt Cap Entire Sample

Small

Medium

Large

Statistic Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters

Full Period 𝑪𝟏 #<0 0.04704 15,508 40% 38,769 0.04105 5,557 43% 12,923 0.04033 5,557 43% 12,923 0.03937 5,557 43% 12,923

𝑪𝟐 #<0 -0.01815 22,486 58% 38,769 -0.00128 6,978 54% 12,923 -0.02158 7,108 55% 12,923 -0.01902 7,108 55% 12,923

𝑹𝟐

F-stat.

0.0454

99.12

0.0334

73.81

0.0312

69.62

0.0373

55.08

Pre-liberalization 𝑪𝟏 𝑪𝟐 #<0 #<0 0.02032 0.00362 6,265 6,798 47% 51% 13,329 13,329 0.01655 0.00028 2,177 2,088 49% 47% 4,443 4,443 0.02036 -0.00118 2,133 2,222 48% 50% 4,443 4,443 0.00826 0.00888 2,222 2,310 50% 52% 4,443 4,443

𝑹𝟐

F-stat.

0.0360

9.89

0.0369

5.36

0.0357

9.95

0.0442

0.75

Post-liberalization 𝑪𝟏 𝑪𝟐 #<0 #<0 0.07388 -0.04011 7,913 16,074 32% 65% 24,729 24,729 0.06459 -0.00233 3,132 4,451 38% 54% 8,243 8,243 0.06114 -0.04293 3,132 4,863 38% 59% 8,243 8,243 0.06992 -0.04653 2,967 4,863 36% 59% 8,243 8,243

Panel B Full Period -0.0028 (2.49)** 𝑹𝟐 0.1046 *,**,*** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.

Pre-liberalization 0.0067 (1.21) 0.0415

b

38

Post-liberalization -0.0130 (3.67)*** 0.1457

𝑹𝟐

F-stat.

0.0553

158.42

0.0302

106.38

0.0271

109.29

0.0308

126.91

Table 5: The Extent of Volume-Return Interactions to Information Asymmetry measured by Market Capitalization in SZSE This table summarizes the dynamic relationship between volume-return interactions and the level of information asymmetry in SZSE. Firms’ market capitalization is used as a proxy for information symmetry. In panel A, we only present the returns’ auto-correlation (𝐶1 ) and volume-return interactions coefficients (𝐶2 ), and 𝑅2 and F-test results from equations (1) and (2). Results are provided for the full period and pre- and post-liberalization periods. The table also includes the number of quarters for which the coefficients are negative, N, the percentage of negative coefficients relative to the total number of coefficients, %N, and the total number of quarters for which we ran regressions over. According to their average quarterly market capitalization, firms in SZSE are divided into three size groups; small, medium, and large. Results from the full period and the two sub-periods are provided for the entire sample as well as the three size groups. In panel B, we present results from equation (3); 𝐶2𝑖 = 𝑎 + 𝑏 ∙ 𝐴𝑖 + 𝑒𝑖 . Estimates of the coefficient b from the full period are presented as well as the preand post-liberalization periods. We also provide t-statistics and 𝑅2 for the same periods. Panel A SZSE Mkt Cap Full

Small

Medium

Large

Statistic Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters

Full Period 𝑪𝟏 #<0 0.04887 12,479 40% 31,051 0.03396 4,658 45% 10,350 0.04000 4,761 46% 10,350 0.04544 4,451 43% 10,350

𝑪𝟐 #<0 -0.01593 17,463 56% 31,051 -0.01461 5,589 54% 10,350 -0.01561 5,693 55% 10,350 -0.01710 5,796 56% 10,350

𝑹𝟐

F-stat.

0.0270

120.31

0.0473

24.05

0.0405

41.21

0.0331

53.26

Pre-liberalization 𝑪𝟏 𝑪𝟐 #<0 #<0 0.03403 0.00338 5,038 5,561 45% 50% 11,134 11,134 0.01577 0.01073 1,819 1,967 49% 53% 3,712 3,712 0.02841 -0.01258 1,856 1,856 50% 50% 3,711 3,711 0.02656 0.00211 1,818 1,856 49% 50% 3,711 3,711

𝑹𝟐

F-stat.

0.0315

28.12

0.0567

1.19

0.0480

6.50

0.039

7.77

Post-liberalization 𝑪𝟏 𝑪𝟐 #<0 #<0 0.06354 -0.03531 6,842 12,141 35% 62% 19,441 19,441 0.05079 -0.02348 1,154 1,538 42% 56% 6,480 6,480 0.05196 -0.03897 1,154 1,594 42% 58% 6,481 6,481 0.06316 -0.03966 1,044 1,621 38% 59% 6,480 6,480

Panel B Full Period -0.0088 (3.67)*** 0.1525 𝑹𝟐 *,**,*** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.

Pre-liberalization -0.0007 (0.02) 0.0371

b

39

Post-liberalization -0.0108 (4.01)*** 0.1954

𝑹𝟐

F-stat.

0.0228

147.16

0.0382

60.77

0.0333

66.21

0.0273

72.84

Table 6: The Extent of Volume-Return Interactions to Information Asymmetry measured by Bid-Ask Spread in SHSE This table summarizes the dynamic relationship between volume-return interactions and the level of information asymmetry in SHSE. Firms’ bid-ask spreads are used as a proxy for information symmetry. In panel A, we only present the returns’ auto-correlation (𝐶1 ) and volume-return interactions coefficients (𝐶2 ), and 𝑅2 and F-test results from equations (1) and (2). Results are provided for the full period and pre- and post-liberalization periods. The table also includes the number of quarters for which the coefficients are negative, N, the percentage of negative coefficients relative to the total number of coefficients, %N, and the total number of quarters for which we ran regressions over. According to their average quarterly bid-ask spreads, firms in SHSE are divided into three groups; low, medium, and high. Results from the full period and the two sub-periods are provided for the entire sample as well as the three size groups. In panel B, we present results from equation (3); 𝐶2𝑖 = 𝑎 + 𝑏 ∙ 𝐴𝑖 + 𝑒𝑖 . Estimates of the coefficient b from the full period are presented as well as the pre- and post-liberalization periods. We also provide t-statistics and 𝑅2 for the same periods. Panel A SHSE Bid-Ask Full

Low

Medium

High

Statistic Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters

Full Period 𝑪𝟏 𝑪𝟐 #<0 #<0 0.04704 -0.01815 15,508 22,486 40% 58% 38,769 38,769 0.04959 -0.06426 6,462 8,012 50% 62% 12,923 12,923 0.00782 -0.01779 6,849 7,366 53% 57% 12,923 12,923 0.07537 -0.00935 5,169 7,108 40% 55% 12,923 12,923

𝑹𝟐

F-stat.

0.0454

99.12

0.1167

11.67

0.0393

19.21

0.0328

14.38

Pre-liberalization 𝑪𝟏 𝑪𝟐 #<0 #<0 0.02031 0.00362 6,265 6,798 47% 51% 13,329 13,329 0.00048 -0.00578 2,533 2,310 57% 52% 4,443 4,443 -0.02663 -0.00231 2,088 2,577 47% 58% 4,443 4,443 0.06048 0.00638 1,866 2,355 42% 53% 4,443 4,443

𝑹𝟐

F-stat.

0.0360

9.89

0.1011

1.01

0.0420

11.51

0.0360

2.13

Post-liberalization 𝑪𝟏 𝑪𝟐 #<0 #<0 0.07388 -0.04011 7,913 16,074 32% 65% 24,729 24,729 0.09606 -0.07314 3,544 5,935 43% 72% 8,243 8,243 0.04054 -0.03262 4,616 5,028 56% 61% 8,243 8,243 0.09010 -0.01993 3,132 4,781 38% 58% 8,243 8,243

Panel B Full Period 0.0281 (6.65)*** 0.1249 𝑹𝟐 *,**,*** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.

Pre-liberalization 0.0467 (3.03)*** 0.1082

b

40

Post-liberalization 0.0568 (9.23)*** 0.1287

𝑹𝟐

F-stat.

0.0553

158.42

0.1325

142.11

0.0371

53.96

0.0301

74.00

Table 7: The Extent of Volume-Return Interactions to Information Asymmetry measured by Bid-Ask Spread in SZSE This table summarizes the dynamic relationship between volume-return interactions and the level of information asymmetry in SZSE. Firms’ bid-ask spreads are used as a proxy for information symmetry. In panel A, we only present the returns’ auto-correlation (𝐶1 ) and volume-return interactions coefficients (𝐶2 ), and 𝑅2 and F-test results from equations (1) and (2). Results are provided for the full period and pre- and post-liberalization periods. The table also includes the number of quarters for which the coefficients are negative, N, the percentage of negative coefficients relative to the total number of coefficients, %N, and the total number of quarters for which we ran regressions over. According to their average quarterly bid-ask spreads, firms in SZSE are divided into three groups; low, medium, and high. Results from the full period and the two sub-periods are provided for the entire sample as well as the three size groups. In panel B, we present results from equation (3); 𝐶2𝑖 = 𝑎 + 𝑏 ∙ 𝐴𝑖 + 𝑒𝑖 . Estimates of the coefficient b from the full period are presented as well as the pre- and post-liberalization periods. We also provide t-statistics and 𝑅2 for the same periods. Panel A SZSE Bid-Ask Full

Low

Medium

High

Statistic Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters Mean N N% Firm-Quarters

Full Period 𝑪𝟏 𝑪𝟐 #<0 #<0 0.04887 -0.01593 12,479 17,463 40% 56% 31,051 31,051 0.03860 -0.05395 5,175 6,521 50% 63% 10,350 10,350 0.03501 -0.02184 4,865 6,003 47% 58% 10,350 10,350 0.06352 0.02449 4,244 5,900 41% 57% 10,350 10,350

𝑹𝟐

F-stat.

0.0270

120.31

0.1048

29.93

0.0410

9.37

0.0381

6.16

Pre-liberalization 𝑪𝟏 𝑪𝟐 #<0 #<0 0.03403 0.003381 5,038 5,561 45% 50% 11,134 11,134 -0.00461 -0.02905 1,782 2,042 48% 55% 3,712 3,712 0.04243 -0.01081 1,596 2,004 43% 54% 3,711 3,711 0.06461 0.03181 1,373 1,893 37% 51% 3,711 3,711

𝑹𝟐

F-stat.

0.0315

28.12

0.0948

1.45

0.0482

1.26

0.0377

2.59

Post-liberalization 𝑪𝟏 𝑪𝟐 #<0 #<0 0.06354 -0.03531 6,842 12,141 35% 62% 19,441 19,441 0.07014 -0.07941 1,456 1,868 53% 68% 6,480 6,480 0.02787 -0.03284 1,484 1,814 54% 66% 6,481 6,481 0.06061 -0.00181 1,264 1,703 46% 62% 6,480 6,480

Panel B Full Period 0.0858 (8.41)*** 0.1201 𝑹𝟐 *,**,*** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.

Pre-liberalization 0.0670 (4.78)*** 0.1014

b

41

Post-liberalization 0.0961 (6.14)*** 0.1196

𝑹𝟐

F-stat.

0.0228

147.16

0.1139

59.80

0.0355

30.74

0.0384

4.21

Table 8: Descriptive Statistics of State-Owned Enterprises (SOEs) in SHSE and SZSE The table provides summary statistics for all locally traded stocks in Shanghai Stock Exchange (SHSE) and Shenzhen Stock Exchange (SZSE). Similar to table (1) and (2), we present summary statistics for AvgCap, AvgVol, AvgTurn, and AvgPrc. The entire sample is divided into three sub-groups based on their StateOwned Enterprises (SOEs) ratios. SOEs are Chinese firms owned, fully or partially, by the Chinese government. We calculate SOEs ratio by dividing outstanding shares over the full number of shares for each firm using daily data. Quarterly averages for each firm are then calculated and used to divide the entire samples evenly into three sub-groups; low, medium, and high SOEs. SHSE

Entire Sample

Low SOEs

Medium SOEs

High SOEs

SZSE Entire Sample

Low SOEs

Medium SOEs

High SOEs

Mean Std. Dev. Min Median Max Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Min Median Max Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters Mean Std. Dev. Median Firm-Quarters

Full Period AvgCap (CNY Millions) 7,500 65,800 23 1,170 7,120,000 38,769 924 1,420 589 12,923 1,060 1,010 782 12,923 17,100 13,000 3,500 12,923 Full Period 3,040 7,350 35 1,190 239,000 31,051 825 740 622 10,350 1,410 2,110 946 10,350 5,790 10,500 2,900 10,350

AvgVol

AvgTurn

AvgPrc

(‘000)

(%)

(CNY)

5,744 17,900 0 1,540 4,170,000 38,769 1,627 6,058 565 12,923 1,619 2,871 783 12,923 11,800 26,300 5,677 12,923

1.78% 2.43% 0.00% 1.05% 91.44% 38,769 1.77% 2.92% 0.90% 12,923 1.47% 2.28% 0.77% 12,923 1.95% 1.95% 1.36% 12,923

11.05 8.66 0.9 9.08 294.17 38,769 10.84 5.99 9.66 12,923 9.16 5.62 7.94 12,923 12.25 11.45 9.18 12,923

40,88 8,684 0.1 1,467 970,000 31,051 1,399 2,654 643 10,350 2,343 4,626 1,013 10,350 7,351 11,900 3,801 10,350

1.87% 2.56% 0.00% 1.11% 94.10% 31,051 1.88% 3.09% 0.93% 10,350 1.53% 2.38% 0.75% 10,350 1.96% 1.92% 1.39% 10,350

12.42 11 0.68 9.46 279.51 31,051 10.64 6.52 9.27 10,350 8.48 5.38 7.42 10,350 15.43 14.55 10.88 10,350

Pre-Liberalization AvgCap AvgVol (CNY (‘000) Millions) 1,020 1,353 1,070 4,113 40 0 754 566 20,000 1,300,000 13,329 13,329 905 1,327 1,060 4,663 659 516 4,443 4,443 1,220 1,339 986 2,444 971 655 4,443 4,443 2,710 3,277 1,810 4,846 2,460 1,666 4,443 4,443 Pre-Liberalization 1,020 1,429 1,260 2,887 62 0 764 640 34,900 215,000 11,134 11,134 868 1,270 687 2,411 698 591 3,712 3,712 1,640 2,081 2,380 4,258 1,150 880 3,711 3,711 2,254 3,071 1,017 3,978 2,179 1,401 3,711 3,711

42

AvgTurn

AvgPrc

(%)

(CNY)

1.79% 3.07% 0.00% 0.86% 91.44% 13,329 1.92% 3.27% 0.93% 4,443 1.51% 2.55% 0.72% 4,443 1.89% 2.71% 0.87% 4,443

12.51 5.94 1.89 11.4 92 13,329 12.77 5.99 11.76 4,443 11.94 5.77 10.68 4,443 12.86 6.20 11.41 4,443

1.94% 3.29% 0.00% 0.90% 94.10% 11,134 2.01% 3.40% 0.95% 3,712 1.64% 2.74% 0.72% 3,711 2.34% 2.91% 1.64% 3,711

12.23 6.49 1.95 10.89 116.12 11,134 12.57 6.63 11.23 3,712 10.11 5.69 9.62 3,711 17.12 6.22 12.57 3,711

Post- Liberalization AvgCap AvgVol (CNY (‘000) Millions) 10,100 6,840 79,400 19,400 23 0 1,580 2,461 7,120,000 4,040,000 24,729 24,729 925 2,071 1,850 7,130 465 667 8,243 8,243 882 1,903 1,020 3,196 605 955 8,243 8,243 17,000 10,500 15,000 24,600 3,440 4,964 8,243 8,243 Post- Liberalization 4,270 5,703 9,060 10,500 35 0 1,950 2,544 239,000 970,000 19,441 19,441 722 1,668 833 3,055 447 782 6,480 6,480 1,090 2,702 1,650 5,040 682 1,233 6,481 6,481 1,408 9,312 11,480 13,841 1,118 7,250 6,480 6,480

AvgTurn

AvgPrc

(%)

(CNY)

1.66% 1.96% 0.00% 1.05% 76.06% 24,729 1.54% 2.22% 0.86% 8,243 1.44% 1.98% 0.82% 8,243 1.78% 1.81% 1.21% 8,243

10.09 9.91 0.9 7.47 294.17 24,729 7.47 4.40 6.52 8,243 6.22 3.79 5.40 8,243 12.47 12.08 9.21 8,243

1.84% 2.03% 0.00% 1.24% 81.28% 19,441 1.64% 2.36% 0.90% 6,480 1.40% 1.85% 0.80% 6,481 1.71% 1.48% 1.13% 6,480

12.62 13.02 0.68 8.41 279.51 19,441 6.60 4.16 5.58 6,480 5.35 2.92 4.80 6,481 13.52 15.87 9.27 6,480

Table 9: The Relationship between Volume-Return Interactions and Information Asymmetry measured by State-Owned Enterprises (SOEs) in SHSE This table summarizes the dynamic relationship between volume-return interactions and the level of information asymmetry in SHSE. The ratio of State-Owned Enterprise (SOEs) are used as a proxy for information symmetry. In panel A, we only present the returns’ auto-correlation (𝐶1 ) and volume-return interactions coefficients (𝐶2 ), and 𝑅2 and F-test results from equations (1) and (2). Results are provided for the full period and pre- and postliberalization periods. The table also includes the number of quarters for which the coefficients are negative, N, the percentage of negative coefficients relative to the total number of coefficients, %N, and the total number of quarters for which we ran regressions over. According to their average quarterly SOEs ratios, firms in SHSE are divided into three groups; small, medium, and large. Results from the full period and the two sub-periods are provided for the entire sample as well as the three size groups. In panel B, we present results from equation (3); 𝐶2𝑖 = 𝑎 + 𝑏 ∙ 𝐴𝑖 + 𝑒𝑖 . Estimates of the coefficient b from the full period are presented as well as the pre- and post-liberalization periods. We also provide t-statistics and 𝑅2 for the same periods. Panel A SHSE SOE

Statisti c

Mean Full N N% FirmQuarte rs Mean Small N N% FirmQuarte rs Mediu m

Preliberalization

Full Period

Mean N N% FirmQuarte rs Mean

Large N N% FirmQuarte rs

𝑪𝟏 #<0

15,508 40%

𝑪𝟐 #<0 0.0181 5 22,486 58%

38,769

38,769

0.0336 7 5,557 43%

0.0287 5 7,366 57%

12,923

12,923

0.1541 5 5,557 43%

0.0221 4 7,108 55%

12,923

12,923

0.0470 4

5,557 43%

0.0044 6 7,108 55%

12,923

12,923

0.0871 0

𝑹𝟐

Fstat.

0.04 54

99.1 2

0.06 83

0.05 64

0.07 01

0.32

0.83

1.16

Postliberalization 𝑹𝟐

Fstat.

0.03 60

9.89

𝑪𝟏 #<0

𝑪𝟐 #<0

0.020 31

0.0036 2

6,265 47%

6,798 51%

7,913 32%

𝑪𝟐 #<0 0.0401 1 16,074 65%

13,32 9

13,329

24,729

24,729

0.017 09

0.0036 3

2,177 49%

2,266 51%

0.0785 4 3,132 38%

0.0671 1 5,111 62%

4,443

4,443

8,243

8,243

0.0593 7 3,132 38%

0.0418 5 4,863 59%

8,243

8,243

0.03 16

9.35

𝑪𝟏 #<0 0.0738 8

2,133 48%

0.0025 1 2,222 50%

4,443

4,443

0.113 14

0.0050 2

2,222 50%

2,310 52%

2,967 36%

0.0094 8 4,863 59%

4,443

4,443

8,243

8,243

0.009 31

0.03 74

0.10 51

1.02

0.35

0.0691 9

𝑹𝟐

Fstat.

0.05 53

158. 42

0.10 14

0.40

0.08 15

0.81

0.04 29

3.13

Panel B

b

𝑹𝟐

Full Period 0.0374 (2.69)*** 0.1056

Pre-liberalization 0.0065 (1.47) 0.0824

43

Post-liberalization 0.0724 (7.01)*** 0.1232

*,**,*** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.

44

Table 10: The Relationship between Volume-Return Interactions and Information Asymmetry measured by State-Owned Enterprises (SOEs) in SZSE This table summarizes the dynamic relationship between volume-return interactions and the level of information asymmetry in SZSE. The ratio of State-Owned Enterprises (SOEs) are used as a proxy for information symmetry. In panel A, we only present the returns’ auto-correlation (𝐶1 ) and volume-return interactions coefficients (𝐶2 ), and 𝑅2 and F-test results from equations (1) and (2). Results are provided for the full period and pre- and postliberalization periods. The table also includes the number of quarters for which the coefficients are negative, N, the percentage of negative coefficients relative to the total number of coefficients, %N, and the total number of quarters for which we ran regressions over. According to their average quarterly SOEs ratios, firms in SHSE are divided into three groups; small, medium, and large. Results from the full period and the two sub-periods are provided for the entire sample as well as the three size groups. In panel B, we present results from equation (3); 𝐶2𝑖 = 𝑎 + 𝑏 ∙ 𝐴𝑖 + 𝑒𝑖 . Estimates of the coefficient b from the full period are presented as well as the pre- and post-liberalization periods. We also provide t-statistics and 𝑅2 for the same periods. Panel A SZSE SOE

Statisti c

Mean Full N N% FirmQuarte rs Mean Small N N% FirmQuarte rs Mediu m

Preliberalization

Full Period

Mean N N% FirmQuarte rs Mean

Large N N% FirmQuarte rs

𝑪𝟏 #<0

12,479 40%

𝑪𝟐 #<0 0.0159 3 17,463 56%

31,051

31,051

0.0488 7

4,658 45%

0.0210 7 5,796 56%

10,350

10,350

0.4662 3 4,761 46%

0.0149 4 5,693 55%

10,350

10,350

0.1333 4

0.0227 3 4,451 43% 10,350

𝑹𝟐

Fstat.

0.02 7

120. 31

0.04 72

0.05 75

0.81

2.22

28.1 2

0.0033 8

5,038 45%

5,561 50%

6,842 35%

11,134

11,134

19,441

19,441

0.0331 7

0.0047 2

1,819 49%

1,967 53%

1,154 42%

0.0309 3 1,593 58%

3,712

3,712

6,480

6,480

0.1035 9 1,154 42%

0.0189 6 1,621 59%

6,480

6,480

1,856 50%

0.0045 3 1,044 38%

0.0042 7 1,593 58%

3,711

6,480

6,480

1,856 50%

0.0104 0 1,856 50%

3,711

3,711

0.0291 2

3,711

Panel B

𝑹𝟐

0.03 15

0.0340 3

𝑪𝟐 #<0 0.0353 1 12,141 62%

10,350

1.82

Fstat.

𝑪𝟐 #<0

5,589 54%

0.02 42

𝑹𝟐

𝑪𝟏 #<0

0.0358 7 1,818 49%

0.0031 2

Postliberalization

45

0.0059 8

0.03 32

0.05 77

0.04 13

25.6 5

2.75

1.03

𝑪𝟏 #<0 0.0635 4

0.1463 4

𝑹𝟐

Fstat.

0.02 28

147. 16

0.06 72

0.64

0.05 85

2.11

0.04 85

2.87

Full Period Pre-liberalization 0.0548 0.0058 (8.61)*** (0.63) 0.0973 0.0512 *,**,*** indicate statistical significance at the 10, 5, and 1 percent levels, respectively.

b

46

Post-liberalization 0.0893 (12.70)*** 0.1178