We study the trading of individual investors using transaction data and identifying buyer- or seller-initiated trades. We document four results: (1) Small trade order imbalance correlates well with order imbalance based on trades from retail brokers. (2) Individual investors herd. (3) When measured annually, small trade order imbalance forecasts future returns; stocks heavily bought underperform stocks heavily sold by 4.4 percentage points the following year. (4) Over a weekly horizon, small trade order imbalance reliably predicts returns, but in the opposite direction; stocks heavily bought one week earn strong returns the subsequent week, while stocks heavily sold earn poor returns.

Abstract Rational models claim “trading to learn” explains widespread excessive speculative trading and challenge behavioral explanations of excessive trading. We argue rational learning models do not explain speculative trading by studying day traders in Taiwan. Consistent with previous studies of learning, unprofitable day traders are more likely than profitable traders to quit. Consistent with models of overconfidence and biased learning (but not with rational learning), the aggregate performance of day traders is negative; 74% of day trading volume is generated by traders with a history of losses; and 97% of day traders are likely to lose money in future day trading. Received: March 4, 2019; Editorial decision: May 16, 2019 by Editor: Jeffrey Pontiff. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

ABSTRACT A risk‐averse manager's overconfidence makes him less conservative. As a result, it is cheaper for firms to motivate him to pursue valuable risky projects. When compensation endogenously adjusts to reflect outside opportunities, moderate levels of overconfidence lead firms to offer the manager flatter compensation contracts that make him better off. Overconfident managers are also more attractive to firms than their rational counterparts because overconfidence commits them to exert effort to learn about projects. Still, too much overconfidence is detrimental to the manager since it leads him to accept highly convex compensation contracts that expose him to excessive risk.

Individual investor trading results in systematic and economically large losses. Using a complete trading history of all investors in Taiwan, we document that the aggregate portfolio of individuals suffers an annual performance penalty of 3.8 percentage points. Individual investor losses are equivalent to 2.2% of Taiwan's gross domestic product or 2.8% of the total personal income. Virtually all individual trading losses can be traced to their aggressive orders. In contrast, institutions enjoy an annual performance boost of 1.5 percentage points, and both the aggressive and passive trades of institutions are profitable. Foreign institutions garner nearly half of institutional profits.

ABSTRACT We study the influence of financial innovation by fintech brokerages on individual investors’ trading and stock prices. Using data from Robinhood, we find that Robinhood investors engage in more attention‐induced trading than other retail investors. For example, Robinhood outages disproportionately reduce trading in high‐attention stocks. While this evidence is consistent with Robinhood attracting relatively inexperienced investors, we show that it is also driven in part by the app's unique features. Consistent with models of attention‐induced trading, intense buying by Robinhood users forecasts negative returns. Average 20‐day abnormal returns are −4.7% for the top stocks purchased each day.

ABSTRACT We compare execution quality of six brokerage accounts across five brokers by generating a sample of 85,000 simultaneous market orders. Commission levels and payment for order flow (PFOF) differ across our accounts. We find that execution prices vary significantly across brokers: the mean account‐level round‐trip cost ranges from 0.07% to 0.46%, excluding any commissions. The dispersion is due to off‐exchange wholesalers systematically giving different execution prices for the same trades to different brokers. Across brokers, variation in PFOF does not explain the large variation in price execution. We provide several suggestions for more informative disclosures on execution quality.

ABSTRACT We placed 85,000 retail trades in six retail brokerage accounts from December 2021 to June 2022 to validate the Boehmer et al. algorithm, which uses subpenny trade prices to identify and sign retail trades. The algorithm identifies 35% of our trades as retail, incorrectly signs 28% of identified trades, and yields uninformative order imbalance measures for 30% of stocks. We modify the algorithm by signing trades using the quoted spread midpoints. The quote midpoint method does not affect identification rates but reduces the signing error rates to 5% and provides informative order imbalance measures for all stocks.