Why is price momentum absent in China? Since momentum is commonly considered arising from investors’ under-reaction to fundamental news, we decompose monthly stock returns into news- and non-news-driven components and document a news day return continuation along with an offsetting non-news day reversal in China. The non-news day reversal is particularly strong for stocks with high retail ownership, relatively less recent positive news articles, and limits to arbitrage. Evidence on order imbalance suggests that stock returns overshoot on news days due to retail investors' excessive attention-driven buying demands, and mispricing gets corrected by institutional investors on subsequent non-news days. To avoid this tug-of-war in stock price, we use a signal that directly captures the recent news performance and re-document a momentum-like underreaction to fundamental news in China.

We introduce a novel perspective on private information, specifically microstructure-based private information, to unravel how institutional investors predict stock returns. Using tick-by-tick transaction data from the Chinese stock market, we find that in retail-dominated markets, institutional investors positively predict stock returns, consistent with findings from institution-dominated markets. However, in contrast to the traditional view that institutional investors primarily rely on value-based private information, our results indicate that microstructure-based private information contributes almost as much to their predictive power as value-based private information does, with both components jointly accounting for approximately two-thirds of the total predictive power of institutional order flow. This finding reveals that retail investors’ trading activities significantly impact institutional investors, naturally forcing them to balance firm value information with microstructure information, thus profoundly influencing the price discovery process in the stock market.

This paper provides new evidence on the predictive power of retail trading for future stock returns using tick data from the Chinese stock market. We explore sources of the predictive power from the novel perspective that sophisticated retail investors may exploit predictive information by observing limit order book and inferring institutional trading intentions. Employing a two-stage decomposition approach, we decompose the retail order imbalance into four components and find that the component related to retail investors’ perception of institutional trading intentions significantly contributes to the predictive power of the retail order imbalance for future returns, accounting for more than 15%.

One commonly adopted practice in classifying retail and institutional orders is based on order size. Due to the increasing use of small orders by institutional investors, size-based classification can lead to an error rate over 20%. To improve the accuracy of the order size algorithm, we study the order patterns and uncover a higher tendency of retail investors trading in multiples of 500 shares. We modify the original order size algorithm by incorporating the feature of share roundedness. The modified algorithm substantially improves the accuracy of identifying retail and institutional investors in China. Order imbalances derived from the modified algorithm better predict future stock returns.

We provide an easy method to identify purchases and sales initiated by retail investors using recent, widely available U.S. equity transactions data. Individual stocks with net buying by retail investors outperform stocks with negative imbalances by approximately 10 basis points over the following week. Less than half of the predictive power of marketable retail order imbalances is attributable to order flow persistence; contrarian trading (a proxy for liquidity provision) and public news sentiment explain little of the remaining predictability. There is suggestive (but only suggestive) evidence that retail marketable orders contain firm-level information that is not yet incorporated into prices.

This paper uses high frequency option data to investigate the information content of option trading of AH cross listed stocks (A-shares traded in mainland China and H-shares traded in Hong Kong) and the role of the Shanghai-Hong Kong Connect in this issue. Measuring the informed trading with order imbalance, we find that the order imbalance of stock options traded in Hong Kong contains incremental information that predicts the return of corresponding A-shares traded in Shanghai after controlling for the cross-market return and volume factors proposed by Gagnon and Karolyi (2009). More important, this predictive power strengthens after the Shanghai-Hong Kong Connect, which is also supported by the evidence of comparison between the two stock crashes exactly before and after the connection. During the 2015 stock crash, the spillover effect of the two markets is significantly stronger than that during the 2008 financial crisis.

This paper studies the presence of informed trading in Taiwan stock index options (TXO) and analyzes the informational role of foreign institutions in incorporating information into Taiwan stock index futures (TX). We have found that only the option-induced part (OOI) of the total TX order imbalance can predict future TX prices, and the OOI calculated from open-buy TXO, defined by Ni et al. (2008), provides incremental predictability. This finding shows that the price predictability stems from the information flow resulting from option transactions rather than from liquidity pressure. We conclude further that option transactions from foreign institutions provide the most significant predictability, out-of-the-money option transactions in particular. These empirical results show that option transactions conducted by foreign institutions have played the primary role in conveying the information inherent in the TXO market to the TX market, foreign institutions being delta-informed traders. Retail investors, the major players in both the TXO and TX markets, have done almost nothing of significance with regard to TXO information transmission into the TX market, with the exception of some near-the-money and out-of-the-money options.

This paper studies trading and prices of dual/cross-listed stocks (i.e., equities from a single company that trade in more than one country). We focus on PRC rms with shares listed in Shanghai and Hong Kong. well-publicized index tracks the average price disparity across the two exchanges and shows signicant variation over time. We show that dierences in order imbalances (in Shanghai vs Hong Kong) explain contemporaneous changes in relative prices at daily and weekly frequencies. Our results help clarify liquidity-driven explanations from sentiment-based ones.

This paper considers informed traders’ trading strategies in a bear market. Known as stealth trading, one strategy of informed traders’ is to use medium-size trades, which tend to contain more information than small- and large-size ones and thus to have stronger impact on stock price movement. Using the tick-by-tick data of Shanghai 180 Index Component Stocks, we document the strong pattern of stealth trading in Chinese stock market during the period of June 1, 2004 to May 31, 2005, which is: (1) an order-driven market; (2) a market that has limit orders only; (3) a bear market; (4) a market with no corresponding derivative market; (5) a market with short-selling constraints; (6) an emerging market. The results extend the empirical evidence on the stealth trading by documenting the fact that price movements are mainly due to the medium-size trades. We find that the pattern in a bear market is highly consistent with that in a bull market. First, we observe that the per-transaction stock price changes in different trade-size categories exhibit a clear U-shape and only the price changes induced by medium-size trades are consistent with the market movement direction. We formally test the stealth trading as well as four alternative hypotheses, and conclude that stealth trading hypothesis can correctly explain this phenomenon. Second, the evidence shows that the medium- size trades have stronger impacts on price change s in the stocks whose price movements are highly consistent with the market (in our study, it refers to those stocks with severely low cumulative return in the sample period). Third, we further document that there is strong interaction between stealth trading hypothesis and order imbalance hypothesis. However, after controlling the effect of order imbalance, the stealth trading hypothesis still holds, but the magnitude is much lower. It is suggested that the follow-up researchers take into consideration the effect of order imbalance, when confirming the existence or the magnitude of the stealth trading.