Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs

• URL: http://arxiv.org/abs/2605.21975v1
• Date: Thu, 21 May 2026 04:09:16 GMT
• Title: Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
• Abstract summary: We introduce StockR1, a time-series-enhanced LLM that unifies stock forecasting and financial reasoning through a verifiable forecast action.<n>Based on a tool-call design, the model first emits a forecast action, which is a structured and interpretable representation of its qualitative market outlook.<n>We optimize the full pipeline with reinforcement learning, where rewards jointly reflect answer validity, forecast accuracy, and consistency between generated actions and observed time-series dynamics.
• Score: 34.87727677131385
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Financial markets are characterized by extreme non-stationarity, low signal-to-noise ratios, and strong dependence on external information such as news, company fundamentals, and macroeconomic signals. Yet, existing approaches either abstract time-series into text or decouple forecasting from language-based reasoning, leading to a fundamental mismatch between qualitative reasoning and quantitative outcomes. To address this, we introduce StockR1, a time-series-enhanced LLM that unifies stock forecasting and financial reasoning through a verifiable forecast action. Based on a tool-call design, the model first emits a forecast action, which is a structured and interpretable representation of its qualitative market outlook. It then invokes a time-series decoder conditioned on this action to generate distributional future trajectories, leading to more informed question answering and financial reasoning. We optimize the full pipeline with reinforcement learning, where rewards jointly reflect answer validity, forecast accuracy, and consistency between generated actions and observed time-series dynamics. In addition, rewards are reweighted by a sample-level uncertainty scalar, encouraging the model to accommodate varying uncertainty in market dynamics. We evaluate StockR1 on financial question answering and stock forecasting over a large-scale 10-year benchmark. Our method consistently outperforms time-series baselines and general-purpose LLMs, improving reasoning accuracy by 17.7% (4B) and 25.9% (8B). These findings demonstrate that structuring the forecast actions establishes a powerful synergy between language reasoning and temporal prediction, enabling LLMs to reason through verifiable, interpretable, and numerically grounded decisions.

Related papers

• PRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting [0.40105987447353786]
  This study introduces PRICE, a structured approach for adapting Large Language Models to short-term Bitcoin price forecasting.<n>Built on a 4-bit quantized LLaMA-3 8B model, PRICE investigates how fine-tuning, numerical representation, prompting, inference, and decoding jointly influence performance.<n>Price achieves the lowest forecasting errors on both validation and test sets while maintaining robust performance across evaluation periods.
  arXiv  Detail & Related papers  (2026-09-04T15:01:38Z)
• 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)
• LEAF: A Living Benchmark for Event-Augmented Forecasting [78.94019460534442]
  LEAF is the first living benchmark for event-augmented forecasting tasks.<n>We evaluate state-of-the-art proprietary and open-weight LLMs.<n>We find that LLMs achieve better performance on equities they confidently identify as more predictable.
  arXiv  Detail & Related papers  (2026-05-09T03:17:59Z)
• Context-Integrated Adversarial Learning for Predictive Modelling of Stock Price Dynamics [0.0]
  This paper introduces a context-sensitive adversarial learning model to predict equity prices.<n>The architecture uses adversarial training to model future price movements and incorporates contextual sentiment features derived from financial textual data.<n> Empirical evaluation of a sample of U.S. equities testifies that the presented approach outperforms the traditional ARIMA and LSTM baselines in a range of measures of error.
  arXiv  Detail & Related papers  (2026-04-13T18:51:01Z)
• The Forecast Critic: Leveraging Large Language Models for Poor Forecast Identification [74.64864354503204]
  We propose The Forecast Critic, a system that leverages Large Language Models (LLMs) for automated forecast monitoring.<n>We evaluate the ability of LLMs to assess time series forecast quality.<n>We present three experiments, including on both synthetic and real-world forecasting data.
  arXiv  Detail & Related papers  (2025-12-12T21:59:53Z)
• FinZero: Launching Multi-modal Financial Time Series Forecast with Large Reasoning Model [27.20045729222667]
  FinZero is a multimodal pre-trained model finetuned by UARPO to perform reasoning, prediction, and analytical understanding on the FVLDB financial time series.<n>After fine-tuning with UARPO, FinZero achieves an approximate 13.48% improvement in prediction accuracy over GPT-4o in the high-confidence group.
  arXiv  Detail & Related papers  (2025-09-10T16:32:41Z)
• FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making [58.04602111184477]
  FinHEAR is a framework for Human Expertise and Adaptive Risk-aware reasoning.<n>It orchestrates specialized agents to analyze historical trends, interpret current events, and retrieve expert-informed precedents.<n> Empirical results on financial datasets show that FinHEAR consistently outperforms strong baselines across trend prediction and trading tasks.
  arXiv  Detail & Related papers  (2025-06-10T04:06:51Z)
• 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)
• Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach [2.0213537170294793]
  This paper propose a comparative study on hybrid machine learning algorithms and leverage on enhancing model interpretability. For the interpretability, we carry out a systematic overview on the preprocessing techniques of time-series statistics, including decomposition, auto-correlational function, exponential triple forecasting, which aim to excavate latent relations and complex patterns appeared in the financial time-series forecasting.
  arXiv  Detail & Related papers  (2023-12-31T16:38:32Z)
• Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
  Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
  arXiv  Detail & Related papers  (2023-08-18T16:21:15Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [48.87381259980254]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training.<n>Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network [2.1163070161951865]
  We propose IndexGAN, which includes deliberate designs for the inherent characteristics of the stock market. We also utilize the critic to approximate the Wasserstein distance between actual and predicted sequences.
  arXiv  Detail & Related papers  (2023-02-27T21:56:56Z)

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.