Making AI Do Numbers to Predict Stock Returns

Can large language models like ChatGPT work with numbers to support technical analysis of stock returns rather than just words to support sentiment analysis? In his April 2024 paper entitled “StockGPT: A GenAI Model for Stock Prediction and Trading”, Dat Mai introduces StockGPT, an autoregressive model trained and tested on stock returns rather than firm news. He segments 1926 through 2000 daily U.S. stock returns into intervals (tokens) and then trains StockGPT to identify predictive return patterns. He then tests the model, using daily returns over the past 256 trading days to predict next-day returns during 2001 through 2023. He assesses accuracy of return predictions by:

1. Running cross sectional regressions of actual versus predicted daily returns.
2. Each day at the market close: (1) removing stocks in the lowest tenth of market values; and, (2) reforming an equal-weighted or value-weighted hedge portfolio that is long (short) stocks within the highest (lowest) tenth, or decile, of predicted returns.

He performs sensitivity tests that account for portfolio costs, portfolio construction delay and elimination of low-priced stocks. Using daily returns for all common stocks traded on NYSE, AMEX or NASDAQ during 1926 through 2023, he finds that: (more…)