History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting (arxiv.org)

A new framework called macro-contextual retrieval has been introduced to enhance financial forecasting, addressing the challenges posed by non-stationary markets and macroeconomic shifts. Traditional multimodal models, which combine numerical data and textual sentiment, often struggle during regime changes. This innovative approach allows for the retrieval of historically analogous macroeconomic regimes and integrates macro indicators like CPI and unemployment with financial news sentiment, enabling real-time predictions without the need for retraining. Significantly, this method was validated using 17 years of S&P 500 data and demonstrated its effectiveness through successful out-of-distribution evaluations on AAPL and XOM stocks for 2024. The macro-conditioned retrieval not only outperformed static and naive models but also provided interpretable and transparent results, supporting the premise that “financial history may not repeat, but it often rhymes.” The framework’s ability to produce robust, explainable forecasts during distributional changes marks a critical advancement for the AI/ML community, particularly in finance, where understanding market movements is essential. All related datasets, models, and code are available for public use, fostering a collaborative environment for future research.