FinZero: Launching Multi-modal Financial Time Series Forecast with Large Reasoning Model
FinZero recasts forecasting by turning time series into images and training a 3B vision language model with an uncertainty aware RL method called UARPO. After fine tuning on a 10k plus FVLDB dataset, it beats GPT-4o by 13.48 percent in high confidence calls and posts average accuracy on price 58.90 percent and volatility 58.33 percent, surpassing Qwen-7B and GPT-4o. Confidence scores track accuracy. Limits include modest scale, missing trading metrics and calibration, and binary tasks.
What it examines
The paper tackles limits of standard time‑series models—information loss from normalization, fixed windows/variables, weak interpretability and uncertainty. It creates FVLDB, a 10k+ image‑text financial dataset, encodes series as images, and proposes FinZero, a 3B multimodal model fine‑tuned with UARPO RL to perform reasoning, trend prediction, and calibrated confidence.
What it concludes
FinZero outperforms baselines, including GPT‑4o, and is 13.5% more accurate on high‑confidence cases, while its confidence scores track reliability. Applications include trading signals, risk control, portfolio rebalancing, and macro/FX monitoring. Limits: modest dataset, image conversion bias, small 3B scale. Future work: larger data, richer modalities, better calibration.
Evidence objects
FinZero turns time series into images and trains a 3B-parameter vision-language model with uncertainty-aware RL (UARPO) to reason, predict rises/falls, and assess confidence using Qwen2.5-VL-3B; inputs avoid normalization, window constraints.
key_findings bullet 1 · key_findings · validation V0
Method uses multi-part rewards (accuracy, length, format) on two A100 GPUs; confidence-based grouping boosts usable accuracy, with UARPO fine-tuning on 10k+ FVLDB outperforming GPT-4o by 13.48% in high-confidence group evaluations.
key_findings bullet 2 · key_findings · validation V0
Results: average accuracy in price 58.90% and volatility 58.33%, beating Qwen-7B and GPT-4o; contributions: diverse FVLDB plus UARPO blending IGRA, CGRA, UARA; limits: modest scale, missing metrics, calibration, binary tasks.
key_findings bullet 3 · key_findings · validation V0
FinZero targets risk-aware financial forecasting by uniting chart-based multimodal LLMs with uncertainty-quantified, interpretable reasoning. Its UARPO fine-tuning blends in-group and cross-group relative advantages with uncertainty adjustment, yielding high-confidence gains over GPT-4o. A new FVLDB image-text dataset supports evaluation. Despite leveraging known GRPO/PPO ideas, integration and empirical improvements are practically compelling.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
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 scalabil… ▽ More 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. △ Less
Source row: 879 · abstract type: unknown