History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting

• URL: http://arxiv.org/abs/2511.09754v2
• Date: Sun, 16 Nov 2025 16:06:38 GMT
• Title: History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting
• Abstract summary: We introduce macro-contextual retrieval, a retrieval-augmented forecasting framework that grounds each prediction in historically analogous macroeconomic regimes.<n>We trained on seventeen years of S&P 500 data (2007-2023) and evaluated OOD on AAPL (2024) and XOM (2024), and the framework consistently narrows the CV to OOD performance gap.<n>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.
• Score: 1.8911049192403768
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• MacroLens: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios [44.59910717749994]
  MacroLens covers 4,416 U.S. small-cap splits and micro-cap gradient splits.<n>Seven tasks share one point-in-time panel of prices, 46.8M equities over accounting facts, 53 macroeconomic series, 295,860 SEC filings, and 215,882 news articles.<n>Tasks span contextual forecasting, public and private valuation, statement generation from fundamentals and descriptions, scenario-conditioned returns, and real-estate valuation.
  arXiv  Detail & Related papers  (2026-06-23T07:52:44Z)
• RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting [83.06074370551887]
  A fixed context window is mismatched to the time-varying optimal look-back of non-stationary price processes.<n>We propose a Mixture-of-Experts framework designed to adaptively determine the temporal context for each input sample.<n> Experiments on cumulative log-return prediction (HS300, S&P500) and fund sales forecasting demonstrate that RAVEN achieves SOTA performances, improves Pearson correlation by 9.2% on HS300 and 20.2% on S&P500, and reduces MSE by 18.2% on fund sales forecasting.
  arXiv  Detail & Related papers  (2026-06-23T02:11:04Z)
• Ratio-Variance Regularized Policy Optimization [64.95520246570446]
  We show that explicitly constraining the policy ratio variance provides a principled local approximation to trust-region constraints.<n>We introduce $bf R2bf VPO$ (Ratio-Variance Regularized Policy Optimization), which implements this constraint via a primal-dual optimization framework.
  arXiv  Detail & Related papers  (2026-05-26T09:53:42Z)
• Is Capability a Liability? More Capable Language Models Make Worse Forecasts When It Matters Most [11.4807141145402]
  In inverse scaling, more capable models produce worse distributional forecasts on forecasting problems with superlinear growth and tail risk of regime change.<n>The pattern appears on ForecastBench-Sim, in forecasting synthetic SIR epidemics with a matched linear control, and replicates in real-world datasets on COVID-19, measles, housing markets, and hyperinflation.<n>This inverse scaling does not appear on single-threshold metrics common in LLM forecasting benchmarks, reversing the sign of the capability--accuracy relationship on identical outputs.
  arXiv  Detail & Related papers  (2026-05-21T16:14:33Z)
• Nexus : An Agentic Framework for Time Series Forecasting [72.73790673303154]
  Time series forecasting requires reasoning with unstructured contextual data such as news or events.<n>We introduce Nexus, a multi-agent forecasting framework that decomposes prediction into specialized stages.<n>We show that current-generation LLMs possess substantially stronger intrinsic forecasting ability than previously recognized.
  arXiv  Detail & Related papers  (2026-05-14T05:12:13Z)
• Statistical Model Checking of the Keynes+Schumpeter Model: A Transient Sensitivity Analysis of a Macroeconomic ABM [0.3210887831772571]
  We show how statistical model checking can provide a principled analysis layer for a realistic macroeconomic simulator.<n>The paper shows that SMC can support reproducible and informative quantitative analysis of substantively rich economic ABMs.
  arXiv  Detail & Related papers  (2026-05-11T12:19:16Z)
• Codified Finite-state Machines for Role-playing [70.86310301713068]
  We introduce Codified Finite-State Machines (CFSMs), a framework that automatically codifies textual character profiles into FSMs.<n>CFSMs extract key states and transitions directly from the profile, producing interpretable structures that enforce character consistency.<n>We extend CFSMs into Codified Probabilistic Finite-State Machines (CPFSMs), where transitions are modeled as probability distributions over states.
  arXiv  Detail & Related papers  (2026-02-05T17:19:18Z)
• Bayesian Robust Financial Trading with Adversarial Synthetic Market Data [15.993346478707686]
  Algorithmic trading relies on machine learning models to make trading decisions.<n>Despite strong in-sample performance, these models often degrade when confronted with evolving real-world market regimes.<n>We propose a Bayesian Robust Framework that integrates a macro-conditioned generative model with robust policy learning.
  arXiv  Detail & Related papers  (2026-01-14T13:15:46Z)
• All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection [67.89888669159899]
  RFC Bench is a benchmark for evaluating large language models on financial misinformation under realistic news.<n>The benchmark defines two complementary tasks: reference free misinformation detection and comparison based diagnosis.
  arXiv  Detail & Related papers  (2026-01-07T18:18:28Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction [4.838838129678638]
  We propose a novel framework for multi-class bond default prediction.<n>LOT integrates numerical time-series (financial/macroeconomic indicators) and unstructured data (bondes)<n>It uses Time-Aware LSTM to handle irregular sequences, and adopts soft clustering and multi-level attention to boost interpretability.
  arXiv  Detail & Related papers  (2025-09-13T03:42:34Z)
• Methodological Insights into Structural Causal Modelling and Uncertainty-Aware Forecasting for Economic Indicators [4.9930207509018425]
  We focus on four key U.S. macroeconomic indicators -- GDP, economic growth, inflation, and unemployment.<n>Our results reveal a robust unidirectional causal link from economic growth to GDP.<n>We use a large language model trained for time series to perform zero-shot predictions on unemployment.
  arXiv  Detail & Related papers  (2025-09-08T04:52:12Z)
• Can We Reliably Predict the Fed's Next Move? A Multi-Modal Approach to U.S. Monetary Policy Forecasting [2.6396287656676733]
  This study examines whether predictive accuracy can be enhanced by integrating structured data with unstructured textual signals from Federal Reserve communications.<n>Our results show that hybrid models consistently outperform unimodal baselines.<n>For monetary policy forecasting, simpler hybrid models can offer both accuracy and interpretability, delivering actionable insights for researchers and decision-makers.
  arXiv  Detail & Related papers  (2025-06-28T05:54:58Z)
• Consistency Checks for Language Model Forecasters [54.62507816753479]
  We measure the performance of forecasters in terms of the consistency of their predictions on different logically-related questions.<n>We build an automated evaluation system that generates a set of base questions, instantiates consistency checks from these questions, elicits predictions of the forecaster, and measures the consistency of the predictions.
  arXiv  Detail & Related papers  (2024-12-24T16:51:35Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Evaluating Probabilistic Classifiers: The Triptych [62.997667081978825]
  We propose and study a triptych of diagnostic graphics that focus on distinct and complementary aspects of forecast performance. The reliability diagram addresses calibration, the receiver operating characteristic (ROC) curve diagnoses discrimination ability, and the Murphy diagram visualizes overall predictive performance and value.
  arXiv  Detail & Related papers  (2023-01-25T19:35:23Z)
• The Macroeconomy as a Random Forest [0.0]
  I develop an algorithm adapting the canonical Machine Learning (ML) tool to flexibly model evolving parameters in a linear macro equation. The approach delivers clear forecasting gains over numerous alternatives, predicts the 2008 drastic rise in unemployment, and performs well for inflation.
  arXiv  Detail & Related papers  (2020-06-23T03:44:15Z)
• Distributional Robustness and Regularization in Reinforcement Learning [62.23012916708608]
  We introduce a new regularizer for empirical value functions and show that it lower bounds the Wasserstein distributionally robust value function. It suggests using regularization as a practical tool for dealing with $textitexternal uncertainty$ in reinforcement learning.
  arXiv  Detail & Related papers  (2020-03-05T19:56:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.