Abstract
This paper uses a two-stage regression approach and tick data from 2012 to investigate the factors that affect short-horizon market efficiency in the Chinese stock market. The findings show that market efficiency is significantly related to certain variables for individual stocks, such as return volatility, trading volume, closing price, and trading costs. Furthermore, one specific characteristic of the Chinese stock market, prevalent speculative trading, causes these relations to differ from those in the US stock market. The stocks with high return volatility and high price level are more efficiently priced in short horizons because they have an elevated level of speculative trading, which gradually loses its effect on market efficiency in the Chinese stock market after 15–20 min.
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Notes
The Chinese stock market was formed in 1991. By the end of 2014, the Shanghai Stock Exchange ranked fourth in terms of global market capitalization, surpassing Euronext and the Hong Kong Stock Exchange. The Shanghai Stock Exchange and the Shenzhen Stock Exchange rank third and fourth, respectively, in terms of annual trading volume (World Federation of Exchanges monthly report).
Speculative trading can be defined as “the purchase (or sale) of goods with a view to resell (repurchase) at a later date” (Kaldor 1939, p. 1).
In the context of this study, market efficiency refers to weak-form market efficiency (Fama 1970).
A-shares are shares denominated in renminbi that are purchased and traded on the Shanghai Stock Exchange and Shenzhen Stock Exchange.
The “Share Investment Through-Train Scheme to Hong Kong” was announced on August 20, 2007. Under the scheme, mainland authorities initially allowed individual mainland investors to purchase Hong Kong stocks directly. However, in a speech on November 3, 2007, the Chinese prime minister stated that the scheme would have to be reassessed because of concerns that excess fund flows could affect market stability. The scheme was not suspended but has made no progress since then.
Chordia and Subrahmanyam (2004) argue that “the concept of order imbalance over an interval makes sense only in a paradigm of an intermediated market, wherein market makers accommodate buying and selling pressures from the general public” (p. 486). The Chinese stock market is an electronic continuous auction market without market makers. Thus, the order imbalance method may not be applicable to the Chinese stock market; however, Chordia and Subrahmanyam (2004) also indicate that “order imbalances can signal excessive investor interest in a stock, and if this interest is autocorrelated, then order imbalances could be related to future returns” (p. 486). Thus, the use of the order imbalance method is justified by this statement. Empirical findings verify the correlation between order imbalance and future stock returns (Narayan et al. 2015).
A detailed description of our measure for short-horizon market efficiency is provided in the “Appendix”.
Some of the sample stocks are for state-owned enterprises (SOEs), in which a large proportion of nontradable shares are retained by the enterprises themselves or other SOEs and government institutions. In our sample period, some of these shares became tradable, which entails a sudden increase in the proportion of institutional holdings. However, these shares, which ensure that the majority of holdings are by the state (because these firms are “state owned”), are not actively traded and distort our measure of institutional trading. Chen et al. (2007) balance the costs and benefits of institutional ownership and indicate that long-term investments, such as corporate holdings of SOEs and government institutions in our case, specialize in monitoring rather than trading. Their holdings enhance the quality of company management instead of influencing price efficiency by trading actively on information. Therefore, we omit these shares from total institutional trading. Insholding represents all institutional holdings—including mutual funds, securities firms, qualified foreign institutional investors (QFII), insurance companies, social security funds, trust companies, banks, and private funds—except general corporate holdings.
Following Visaltanachoti and Yang (2010), we also calculate \( \textit{Return}_{t} \) and \( \textit{OIBNUM}_{t} \) at 1-, 2-, 3-, 10-, 15-, 20-, 30-, 40- and 60-min intervals in order to obtain the market efficiency measure, \( \textit{Efficiency }\), over different time intervals that are used in our short-horizon versus long-horizon efficiency analysis.
\( \textit{Efficiency }\) calculated at three-minute intervals is used to check the robustness of the multivariate regression results in Eq. (2). The results are qualitatively the same as the ones obtained from \( \textit{Efficiency }\) calculated at five-minute intervals in Eq. (2). Therefore, the results in Eq. (2) obtained from \( \textit{Efficiency} \) calculated at three-minute intervals are not reported to conserve space and are available upon request to the author.
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Acknowledgements
The author thanks Jos van Bommel, Jörg Prokop, and participants at the 34th International Conference of French Finance Association for helpful discussions and suggestions. The author is particularly grateful to the anonymous referees for their insightful comments.
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Appendix: Description of the measure of market efficiency
Appendix: Description of the measure of market efficiency
In this study, the notion of market efficiency refers to the weak-form market efficiency in which the information set is past price (or return) histories (Fama 1970). Chordia and Subrahmanyam (2004) construct a simple model to show that investors tend to split their orders in consecutive periods in order to reduce the impact on stock prices, which induces a significant correlation of order imbalance in consecutive periods (on average, we have a statistically significant first lag correlation of order imbalance of about .47 over 5-min intervals). As the order imbalance is strongly indicative of the contemporaneous return, the lagged order imbalance provides a certain predictability power for future stock returns. Therefore, the market efficiency measure in this study aims to quantify the degree of the predictability of past order imbalances that is closely related to past stock returns over current returns. The same (or similar) measure of market efficiency has been used in studies by Chordia et al. (2008), Chung and Hrazdil (2010a, 2012), Su et al. (2010) and Visaltanachoti and Yang (2010).
This study obtains tick data from the Resset Financial Database. The final sample includes 1693 stocks from both the SHSE and the SZSE. Every transaction is assigned using the Lee and Ready (1991) trade assignment algorithm to estimate whether it is initiated by the buyer or the seller. Any quote less than 5 s prior to the trade is ignored, and the first quote at least 5 s prior to the trade is retained. A trade is classified as initiated by the buyer (seller) if it is closer to the ask (bid) price of the prevailing quote. If the trade is exactly at the midpoint of the quote, a “tick test” is used in which the trade is classified as initiated by the buyer (seller) if the last price change prior to the trade is positive (negative).
To avoid contamination of the return serial correlations by bid–ask bounce, returns are computed from quote midpoints (Chordia et al. 2005). For each transaction during each day, the prevailing quote before the trade is used to compute a bid–ask midpoint. Returns are then computed from these midpoints.
Trades indicated as either buyer- or seller-initiated are used as indicators to calculate the imbalance measures in three ways. The first is based on the number of the trade (\( \textit{OIBNUM} \)), the second on the share of the trade (\( \textit{OIBSHR} \)), and the third based on the renminbi (RMB) value of the trade (\( \textit{OIBVAL} \)). Following the work of Chung and Hrazdil (2012) and Visaltanachoti and Yang (2010), we calculate \( \textit{OIBNUM} \) for each stock for 5-min intervals as:
This study computes \( \textit{OIBNUM} \) and stock return measures for all stocks. Following Chordia et al. (2008), we compute stock returns over 5-min intervals using the bid–ask midpoints quoted at the end of the intervals. To assess the degree of market efficiency, we use the following model:
where \( \textit{Return}_{t} \) is the stock midpoint return over the 5-min interval \( t \) and \( \textit{OIBNUM}_{t - 1} \) is order imbalance over the 5-min interval \( t - 1 \).Footnote 9 This study uses the \( \textit{adjusted} \;R^{2} \) as the measure of market efficiency throughout (Chordia et al. 2008; Chung and Hrazdil 2010a). Following Chordia et al. (2008), we include the interaction variable \( \textit{OIBNUM}_{t - 1} \) and \( \textit{ILD}_{t} \) to control for the effects of liquidity changes on market efficiency. We code the dummy variable \( \textit{ILD}_{t} \) with a value of one for all intervals on a day if the value-weighted average effective spread for the day is at least one standard deviation above the mean spread calculated for a surrounding period (− 60, + 60), and zero otherwise.
The \( \textit{adjusted} \;R^{2} \) is collected from the estimation in Eq. (A2) on a firm-by-firm basis over the sample period and used as the measure for short-horizon market efficiency. The logit transformation to the \( \textit{adjusted} \;R^{2} \) is applied because the \( \textit{adjusted} \;R^{2} \) measure is bounded by zero and one. The result of logit transformation is also multiplied by − 1 to obtain new variable \( \textit{Efficiency}, \) so, for ease of interpretation, higher values of \( \textit{Efficiency }\) represent higher degrees of market efficiency.Footnote 10
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Hu, Y. Short-horizon market efficiency, order imbalance, and speculative trading: evidence from the Chinese stock market. Ann Oper Res 281, 253–274 (2019). https://doi.org/10.1007/s10479-018-2849-4
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DOI: https://doi.org/10.1007/s10479-018-2849-4