Predictability of Stock Return Anomaly Signals

Can investors reasonably anticipate the signals (stock rankings) for stock anomalies that are based on firm financial information. In their August 2024 paper entitled “Predicting Anomalies”, Boone Bowles, Adam Reed, Matthew Ringgenberg and Jake Thornock investigate whether: (1) stock returns follow predictable patterns before availability of anomaly trading signals; and, (2) anomaly trading signals are themselves predictable. They focus on a set 28 published anomalies that are entirely based on publicly available information in quarterly financial statements. They each quarter for each anomaly reform a hedge portfolio that is long (short) the tenth of stocks with the highest (lowest) expected returns. They consider four models to predict stock rankings for each anomaly: (1) a first-order autoregression that projects strength of signals; (2) a first-order autoregression that projects stock rankings; (3) a machine learning model that uses past anomaly signals and rankings; and, (4) a (martingale) model that assumes anomaly portfolio rankings for next quarter will be the same as current rankings. Using as-published specifications for each of the 28 anomalies plus daily returns and quarterly/annual financial reports for a broad sample of U.S. stocks during January 1990 through December 2019, they find that: (more…)