Long-term investment strategies suited for large institutional investors.
This paper introduces HAID—a novel measure that captures new information in earnings call Q&As by comparing executive answers to ChatGPT’s responses. A higher HAID predicts more trading, stronger price reactions, improved analyst forecasts, and greater liquidity.
ChatGPT-based sentiment from full earnings call transcripts predicts stock returns up to six months ahead. After its democratization, retail investors’ trading aligns more closely with AI insights—narrowing the gap with informed traders.
This paper shows that hedge funds adopting ChatGPT saw a 3–5% boost in annualized returns post-2022.
This paper introduces asset embeddings—vector representations of stocks learned from investor portfolios using techniques like BERT and Word2Vec. These embeddings outperform traditional characteristics in explaining return comovement, offering a new framework for understanding investor behavior.
ChatGPT can classify Federal Reserve policy tone and identify monetary shocks with high accuracy—matching or outperforming traditional methods. The paper shows GPT-4 can replicate expert-level reasoning, offering a powerful tool for analyzing central bank communication.
ChatGPT can extract forward-looking investment signals from earnings calls. The paper shows that its investment score predicts firm capex up to 9 quarters ahead and negatively forecasts future stock returns—revealing information not yet priced by markets.
ChatGPT can predict future UK interest rate decisions by analyzing central bank speeches—turning policy tone into a reliable forecasting signal.
This paper shows that ChatGPT-3.5 can predict aggregate U.S. stock returns using WSJ headlines. DeepSeek and smaller LLMs (like BERT) do not match this predictive performance.
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 shows that a simple, model-free valuation approach using all available accounting data can identify stock mispricing. A strategy that buys underpriced and sells overpriced firms earns **4–10% alpha per year**, outperforming many known anomalies—even without complex valuation models.
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.