Long-term strategies with monthly to multi-year holding periods, often fundamentals-driven.
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 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 shows that investors with limited ability to borrow tend to chase high-risk assets. As a result, low-risk (low-beta) assets offer better risk-adjusted returns. A strategy that goes long low-beta assets and shorts high-beta ones—called “Betting Against Beta”—consistently beats the market.
Macroeconomic uncertainty isn’t just noise—it reshapes bond risk premia and pricing.
This paper shows that the part of the variance risk premium tied to downside tail risk—jumps in asset prices—is what really predicts market returns. It's a cleaner measure of investor fear than the VIX.
Bitcoin’s extreme volatility comes with an upside: low correlation with other assets. This paper shows that even a small allocation to Bitcoin can significantly boost the Sharpe ratio of a diversified portfolio.
This paper shows that Initial Coin Offerings (ICOs) can help startups grow by giving them access to funding and liquidity. Projects that make good disclosures, use tokens with real utility, and get listed on exchanges are more likely to survive and hire more employees later.