Long-term strategies with monthly to multi-year holding periods, often fundamentals-driven.
Machine learning (ML) signals look great in backtests—but they often break when exposed to economic restrictions. This paper shows that ML-based strategies lose 50–80% of their alpha when excluding microcaps, distressed stocks, or accounting for transaction costs.
This paper develops the Policy Change Index (PCI) for China, a machine learning-based, language-free signal that predicts upcoming policy shifts by analyzing the page placement of articles in the People’s Daily. The PCI successfully anticipates major Chinese policy events.
This paper uses deep learning to estimate the stochastic discount factor (SDF) that prices all U.S. stocks. The authors combine a feedforward network, LSTM, and a generative adversarial network (GAN) to build a non-linear, no-arbitrage-compliant model.
This paper runs over 2 million trading strategies using CRSP and Compustat data to expose how widespread p-hacking is in finance. After accounting for multiple testing and requiring economic significance, fewer than 20 strategies remain—and none have theoretical justification.
Multinational firms' returns are predictably linked to foreign industry news from their sales regions. This paper shows that investors underreact to economically relevant information abroad.
This paper explains why return predictability—and momentum—concentrates in bad times. Investors rely on different forecasting models, which causes disagreement to spike in recessions. These disagreement shocks create time-series momentum, especially when uncertainty is high and beliefs polarize.
Unusual negative news—news that combines rare language with negative sentiment—predicts sharp increases in market volatility. This paper shows that both firm-specific and market-wide volatility rise significantly after such news, with effects that persist for months.
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 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.