FinZero: Launching Multi-modal Financial Time Series Forecast with Large Reasoning Model

ArXiv ID: 2509.08742 “View on arXiv”

Authors: Yanlong Wang, Jian Xu, Fei Ma, Hongkang Zhang, Hang Yu, Tiantian Gao, Yu Wang, Haochen You, Shao-Lun Huang, Danny Dongning Sun, Xiao-Ping Zhang

Abstract

Financial time series forecasting is both highly significant and challenging. Previous approaches typically standardized time series data before feeding it into forecasting models, but this encoding process inherently leads to a loss of important information. Moreover, past time series models generally require fixed numbers of variables or lookback window lengths, which further limits the scalability of time series forecasting. Besides, the interpretability and the uncertainty in forecasting remain areas requiring further research, as these factors directly impact the reliability and practical value of predictions. To address these issues, we first construct a diverse financial image-text dataset (FVLDB) and develop the Uncertainty-adjusted Group Relative Policy Optimization (UARPO) method to enable the model not only output predictions but also analyze the uncertainty of those predictions. We then proposed FinZero, a multimodal pre-trained model finetuned by UARPO to perform reasoning, prediction, and analytical understanding on the FVLDB financial time series. Extensive experiments validate that FinZero exhibits strong adaptability and scalability. After fine-tuning with UARPO, FinZero achieves an approximate 13.48% improvement in prediction accuracy over GPT-4o in the high-confidence group, demonstrating the effectiveness of reinforcement learning fine-tuning in multimodal large model, including in financial time series forecasting tasks.

Keywords: Multimodal Pre-training, Reinforcement Learning (RL), Uncertainty Quantification, Time Series Forecasting, Transformers

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail
  • Why: The paper presents a novel, mathematically dense method (UARPO) with complex formulations, yet it is also highly data/implementation-heavy, featuring a custom dataset (FVLDB), specific evaluation metrics, and reported quantitative improvements over a benchmark model.
  flowchart TD
    A["Research Goal:<br>Improve Multimodal Financial TS Forecasting<br>with Uncertainty Quantification"] --> B["Key Methods:<br>1. UARPO (Reinforcement Learning)<br>2. Multimodal Pre-training"]
    B --> C{"Data/Inputs:<br>FVLDB Image-Text Financial Dataset"}
    C --> D["Computational Process:<br>1. Multimodal Pre-training<br>2. UARPO Fine-tuning"]
    D --> E["Key Findings/Outcomes:<br>1. FinZero Model<br>2. Improved Scalability & Adaptability<br>3. 13.48% Accuracy Gain vs GPT-4o"]