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 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.