History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting

Financial markets are inherently non-stationary: structural breaks and macroeconomic regime shifts often cause forecasting models to fail when deployed out of distribution ({OOD}). Conventional multimodal approaches that simply fuse numerical indicators and textual sentiment rarely adapt to such shifts. We introduce macro-contextual retrieval, a retrieval-augmented forecasting framework that grounds each prediction in historically analogous macroeconomic regimes. The method jointly embeds macro indicators (e.g., {CPI}, unemployment, yield spread, {GDP} growth) and financial news sentiment in a shared similarity space, enabling causal retrieval of precedent periods during inference without retraining. Trained on seventeen years of S\&P 500 data (2007-2023) and evaluated {OOD} on {AAPL} (2024) and {XOM} (2024), the framework consistently narrows the {CV} to {OOD} performance gap. Macro-conditioned retrieval achieves the only positive out-of-sample trading outcomes ({AAPL}: {PF}=1.18, Sharpe=0.95; {XOM}: {PF}=1.16, Sharpe=0.61), while static numeric, text-only, and naive multimodal baselines collapse under regime shifts. Beyond metric gains, retrieved neighbors form interpretable evidence chains that correspond to recognizable macro contexts, such as inflationary or yield-curve inversion phases, supporting causal interpretability and transparency. By operationalizing the principle that “financial history may not repeat, but it often rhymes,” this work demonstrates that macro-aware retrieval yields robust, explainable forecasts under distributional change. All datasets, models, and source code are publicly available.