Stocks
This paper shows that anomalies tend to lose profitability in the US after publication, likely due to arbitrage trading. However, the same does not hold for international markets, where anomalies remain strong and persistent—even post-publication.
This paper finds that stocks underreact to firm-specific news—prices continue to drift in the news direction for days. A strategy based on high-frequency news returns earns over 3% per month and remains profitable after trading costs.
This paper shows that many return anomalies—especially those tied to earnings, profitability, and momentum—perform much better when media coverage is low. Limited investor attention and short-sale constraints create predictable mispricing in these overlooked stocks.
This paper shows that so-called low-risk anomalies—like betting against beta or volatility—aren’t puzzles at all. They're compensation for skewness risk, especially default risk. High beta and high volatility stocks tend to be more negatively skewed, and markets misprice them if they ignore this.
Stocks tend to perform better in the same calendar months they did well in the past. This paper shows that historical same-month returns are strong predictors of future performance—across stocks, anomalies, industries, countries, and even commodities.
Not all momentum winners are created equal. This paper shows that a subset of high-return stocks—those with low institutional ownership and rising short interest—are overpriced. These “overpriced winners” crash in the future, offering a profitable short signal.
Analysts often issue optimistic return forecasts and recommendations that directly contradict well-documented anomaly signals. This paper shows that anomaly-longs are underappreciated and anomaly-shorts are overhyped—despite the anomalies' proven predictive power.
This paper shows that when an anomaly becomes widely known (i.e., "discovered"), arbitrageurs begin exploiting it—reducing its future returns, changing correlations, and indirectly benefiting passive investors through diversification.
This paper finds that stock return anomalies—long known to be profitable—perform much better on days with firm-specific news. Anomalies earn 50% higher returns on news days and over 6× more on earnings days, suggesting these effects are driven by investor misbeliefs corrected when news arrives.
This paper uses a unique intraday dataset to track insider trades and shows that insiders don’t try to hide their activity. They prioritize trading on returns, not timing liquidity—making their actions visible and their impact immediate.
This paper develops a machine learning method that builds its own sentiment dictionary from scratch to predict stock returns from news articles. The resulting strategy delivers much higher Sharpe ratios than those based on commercial scores like RavenPack or dictionary-based methods.
This paper shows that simple year-over-year changes in a firm’s 10-K predict stock returns, earnings, and even bankruptcies. Investors fail to notice or react to these changes at the time of filing—creating profitable return predictability.