Fama and French (1992) document a significant relation between firm size, book-to-market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. The authors document that the relation between firm size, book-to-market ratios, and security returns is similar for financial and nonfinancial firms. In addition, they present evidence that survivorship bias does not significantly affect the estimated size or book-to-market premiums in returns. The authors' results indicate data-snooping and selection biases do not explain the size and book-to-market patterns in returns.

Fama and French (1992) document a significant relation between firm size, book-to-market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. We document that the relation between firm size, book-to-market ratios, and security returns is similar for financial and nonfinancial firms. In addition, we present evidence that survivorship bias does not significantly affect the estimated size or book-to-market premiums in returns. Our results indicate data-snooping and selection biases do not explain the size and book-to-market patterns in returns.

Individual investors who hold common stocks directly pay a tremendous performance penalty for active trading. Of 66,465 households with accounts at a large discount broker during 1991 to 1996, those that trade most earn an annual return of 11.4 percent, while the market returns 17.9 percent. The average household earns an annual return of 16.4 percent, tilts its common stock investment toward high‐beta, small, value stocks, and turns over 75 percent of its portfolio annually. Overconfidence can explain high trading levels and the resulting poor performance of individual investors. Our central message is that trading is hazardous to your wealth.

We analyze tests for long‐run abnormal returns and document that two approaches yield well‐specified test statistics in random samples. The first uses a traditional event study framework and buy‐and‐hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewness‐adjusted t ‐statistic or the empirically generated distribution of long‐run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar‐time portfolios and a time‐series t ‐statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long‐run abnormal returns is treacherous.

We analyze tests for long‐run abnormal returns and document that two approaches yield well‐specified test statistics in random samples. The first uses a traditional event study framework and buy‐and‐hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewness‐adjusted t‐statistic or the empirically generated distribution of long‐run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar‐time portfolios and a time‐series t‐statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long‐run abnormal returns is treacherous.

Based on a survey of American Finance Association members, we analyze how demographics, time allocation, production mechanisms, and institutional factors affect research production during the pandemic. Consistent with the literature, research productivity falls more for women and faculty with young children. Independently, and novel, extra time spent on teaching (much more likely for women) negatively affects research productivity. Also novel, concerns about feedback, isolation, and health have large negative research effects, which disproportionately affect junior faculty and PhD students. Finally, faculty who express greater concerns about employers’ finances report larger negative research effects and more concerns about feedback, isolation, and health.

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.

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.