FinAbstain: Uncertainty-Calibrated Multimodal RAG for Selective Financial Forecasting

• URL: http://arxiv.org/abs/2607.24875v1
• Date: Mon, 27 Jul 2026 07:05:22 GMT
• Title: FinAbstain: Uncertainty-Calibrated Multimodal RAG for Selective Financial Forecasting
• Abstract summary: FinAbstain is a framework for uncertainty-calibrated multimodal retrieval-augmented generation with selective prediction.<n>A point-in-time retriever admits only information public at the forecast timestamp and supplies modality-specific evidence to fundamental, news, technical, risk, and verification agents.<n>A controller predicts bullish, bearish, or neutral outcomes only when uncertainty is below a validated threshold.
• Score: 8.891247829087936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) can synthesize financial narratives but may express high confidence when evidence is sparse, stale, or contradictory. This failure is especially consequential in forecasting, where filings, news, prices, volume, and technical signals can disagree. We present FinAbstain, a research framework for uncertainty-calibrated multimodal retrieval-augmented generation (RAG) with selective prediction. A point-in-time retriever admits only information public at the forecast timestamp and supplies modality-specific evidence to fundamental, news, technical, risk, and verification agents. Their probabilistic assessments are aggregated with retrieval relevance, evidence contradiction, repeated-sample consistency, and historical calibration statistics. Temperature scaling, isotonic regression, conformal prediction, and a proposed hybrid uncertainty score are evaluated under a common chronological protocol. A controller predicts bullish, bearish, or neutral outcomes only when uncertainty is below a validated threshold; otherwise it abstains, requests evidence, reduces exposure, or routes the case to human review. The evaluation covers one- and five-day abnormal-return direction, twenty-day volatility intervals, and abstention decisions, using accuracy, calibration, risk--coverage, citation, trading, latency, and cost metrics. To make the design auditable before a full data collection is complete, we report explicitly labeled simulated results rather than empirical claims. These results illustrate the intended hypothesis: calibrated abstention may trade coverage for lower selective error and drawdown. The contribution is a time-safe architecture, a composite uncertainty formulation, and a reproducible evaluation blueprint for evidence-grounded selective financial forecasting.

Related papers

• FinBench: Time-Gated Calibration and Uncertainty Benchmarking for Agentic Financial Forecasting [0.0]
  FinBench is a benchmark designed to evaluate calibration and uncertainty quality for financial forecasting.<n>This paper describes the benchmark specification and reports a small pilot run as a sanity check of the pipeline.
  arXiv  Detail & Related papers  (2026-06-24T01:27:30Z)
• Conformal Reliability: A New Evaluation Metric for Conditional Generation [62.761166941396844]
  Conditional generative models have recently remarkable success in various applications.<n>We propose a novel evaluation metric called reliability based on prediction set at a pre-specified confidence level.<n>We introduce Conformal ReLiability (CReL), a framework that can (i) construct a prediction set and (ii) accurately optimize the reliability score within the constructed prediction set.
  arXiv  Detail & Related papers  (2026-05-29T03:52:44Z)
• Machine Learning for Stress Testing: Uncertainty Decomposition in Causal Panel Prediction [2.289837306672451]
  Regulatory stress testing requires projecting credit losses under hypothetical macroeconomic scenarios.<n>We propose a framework for policy-path counterfactual inference in panels.<n>We validate all results through simulation and semi-synthetic experiments with real unemployment data.
  arXiv  Detail & Related papers  (2026-03-08T03:22:38Z)
• Statistical Inference for Score Decompositions [0.0]
  We introduce methods for score decompositions, which partition scoring functions for predictive assessment into three interpretable components.<n>We find that for survey inflation forecasts, discrimination abilities can differ significantly even when overall predictive ability does not.<n>By disentangling forecast accuracy from backtest performance, the method exposes critical shortcomings in current banking regulation.
  arXiv  Detail & Related papers  (2026-03-04T16:57:08Z)
• Distribution-informed Online Conformal Prediction [53.674678995825666]
  We propose Conformal Optimistic Prediction (COP), an online conformal prediction algorithm incorporating underlying data pattern into the update rule.<n>COP produces tighter prediction sets when predictable pattern exists, while retaining valid coverage guarantees even when estimates are inaccurate.<n>We prove that COP can achieve valid coverage and construct shorter prediction intervals than other baselines.
  arXiv  Detail & Related papers  (2025-12-08T17:51:49Z)
• Bayesian Modeling for Uncertainty Management in Financial Risk Forecasting and Compliance [0.0]
  We develop an integrated approach that consistently enhances the handling of risk in market volatility forecasting, fraud detection, and compliance monitoring.<n>We evaluate the performance of one-day-ahead 95% Value-at-Risk (VaR) forecasts on daily S&P 500 returns, with a training period from 2000 to 2019 and an out-of-sample test period spanning 2020 to 2024.<n>Our proposed discount-factor DLM model produces a slightly liberal VaR estimate, with evidence of clustered violations.
  arXiv  Detail & Related papers  (2025-12-06T23:00:19Z)
• 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)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)
• Coverage-Guaranteed Speech Emotion Recognition via Calibrated Uncertainty-Adaptive Prediction Sets [0.0]
  Road rage, often triggered by emotional suppression and sudden outbursts, significantly threatens road safety by causing collisions and aggressive behavior.<n>Speech emotion recognition technologies can mitigate this risk by identifying negative emotions early and issuing timely alerts.<n>We propose a novel risk-controlled prediction framework providing statistically rigorous guarantees on prediction accuracy.
  arXiv  Detail & Related papers  (2025-03-24T12:26:28Z)
• Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors [1.3812010983144802]
  This study shows that the desirable properties of Adaptive Conformal Inference (ACI) do not require the use of Conformal Predictors (CP)<n>We empirically investigate the performance of Non-Conformal Confidence Predictors (NCCP) against CP when used with ACI on non-exchangeable data.
  arXiv  Detail & Related papers  (2024-09-23T21:02:33Z)
• Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
  We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
  arXiv  Detail & Related papers  (2023-10-25T02:37:51Z)
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model [68.34559610536614]
  We argue that the aleatoric uncertainty is an inherent attribute of the data and can only be correctly estimated with an unbiased oracle model. We propose a new sampling and selection strategy at train time to approximate the oracle model for aleatoric uncertainty estimation. Our results show that our solution achieves both accurate deterministic results and reliable uncertainty estimation.
  arXiv  Detail & Related papers  (2021-11-22T08:54:10Z)

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.