Empirical Asset Pricing via Machine Learning
This paper compares machine learning methods to predict stock returns and measure equity risk premia. The best-performing model (a 3-layer neural net) earns out-of-sample Sharpe ratios as high as 2.45.
Long-Short
Taking both long and short positions to exploit relative mispricings.
Latest posts — page 2
Autoencoder Asset Pricing: Nonlinear Factors with No-Arbitrage Discipline
This paper introduces a new class of asset pricing models using autoencoders. By embedding firm characteristics into a neural network architecture, the authors construct a nonlinear conditional factor model that enforces no-arbitrage. It dramatically outperforms linear benchmarks.
Machine Learning vs. Economic Reality: The Limits of Deep Alpha
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.
Reading China: Predicting Policy Change with Machine Learning
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.
Microstructure in the Machine Age: Frictions Still Matter
This paper shows that classic market microstructure measures like VPIN, Amihud, and Roll still have predictive power—even in modern, high-frequency, machine-traded markets.
Two Million Strategies Later: How p-Hacking Distorts Financial Research
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.
Foreign Information Alpha: How Overseas Signals Predict Multinationals' Returns
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.
Return Predictability Spikes in Bad Times: Disagreement Drives Momentum
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.
Trading Costs Demystified: Real Costs for Institutional Traders
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.
Anomalies Across the Globe: Once Public, No Longer Exist?
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.
Pervasive Underreaction: Trading Firm News with High-Frequency Edge
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.
Attention and Underreaction: Why Some Anomalies Work Better in the Dark
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.