Strategies that only take long positions in assets.
This paper introduces a ChatGPT-based Commodity News Ratio Index (CNRI) to forecast excess returns on commodity futures. Using over 2.5 million articles across 18 commodities and 80 years, the CNRI significantly outperforms traditional predictors both in- and out-of-sample.
When the euro gets stronger, European investors tend to sell government bonds from emerging countries if those bonds are in the local currencies of those countries. They do this because a stronger euro means those investments are now worth less to them, even if countries themselves aren’t riskier.
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.
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 (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 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.
This paper shows that the **entire equity premium since 1994** is earned in just four specific weeks of the FOMC cycle: weeks 0, 2, 4, and 6. Stock returns spike after informal Fed communications and Board of Governors meetings—revealing a hidden pattern driven by central bank behavior.
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 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.