Strategies focused on US-listed assets.
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.
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.
This paper shows that timing factor exposures over time can improve performance significantly. By forecasting returns on key equity anomalies using valuation signals, the authors build a dynamic portfolio that earns higher Sharpe ratios than static factor investing or market timing alone.
This paper finds that stocks more sensitive to investor mood swings—called "high mood beta" stocks—earn higher returns in good-mood periods (like Fridays and January) and lower returns in bad-mood periods (like Mondays and October). These effects are systematic, persistent, and tradable.
This paper studies how High-Frequency Traders (HFTs) and Market Makers behaved during the Flash Crash of May 6, 2010. It shows that HFTs didn’t cause the crash—but also didn’t help stabilize it. They traded fast and in the same direction as prices, while traditional market makers pulled back.