PPD-SOTA: Recasting State-of-the-Art Evaluation for AI-Driven Financial Forecasting

AI-driven financial forecasting has advanced rapidly, but state-of-the-art evaluation still relies heavily on metric wins over selected baselines. In financial forecasting, however, predictive performance must pass through portfolio construction and deployment conditions before it translates into practical value, making a single leaderboard rank difficult to interpret. We introduce PPD-SOTA, an evaluation framework that separates forward model selection from the scope of a SOTA claim. For deployment, PPD-SOTA evaluates forecasting models through Prediction, Portfolio, and Deployability (PPD). For comparison, it tests a comparator set using practical margins and paired simultaneous inference. On a unified benchmark of recent stock-forecasting methods, PPD achieves lower mean regret than conventional metrics-win selection and remains effective across changing conditions, while superiority support narrows as the comparator set expands and no model clears the full set. These results motivate evaluating financial forecasting models by both forward deployment performance and the scope of superiority supported by the evidence, rather than by isolated metric wins alone.