Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models (SEP)

SEP pairs a self-reflective agent with Proximal Policy Optimization so that an LLM teaches itself to generate explainable stock predictions, fine-tuning on its own self-generated responses instead of human annotations. The same loop extends to portfolio construction, where no binary correct answer exists: each iteration shows the reflective model the profits implied by its current weights and asks it to revise them, then hands both weight sets to the PPO trainer with the higher-profit one treated as the better response. The data is price and tweet data for 2020-2022 covering 55 stocks, the top five in each of 11 industries, collected in the StockNet dataset format.

On the portfolio task the authors report an annualized Sharpe of 1.150 for SEP against 0.123 for the market index and -0.225 for an equal-weight 1/N portfolio — a single evaluation period reported by the authors, with no independent replication. The portfolio experiment runs on a basket of 11 stocks, a much smaller universe than the 55-stock prediction dataset, so the two sets of results should not be read as covering the same ground.

Neither repository carries a license. The code (koa-fin/sep) and the full dataset (koa-fin/sn2) are both public with no LICENSE file, which leaves them all-rights-reserved and legally unreusable, and both have been dormant since May 2024. Only sample price and tweet data ships with the code, and its README states outright that rerunning the full results may not be feasible for individual developers because of OpenAI API charges.