FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Finance & Economics — FinTech & Digital Financial Services, Capital Markets & Investment Management, Artificial Intelligence & Machine Learning · Depth: Advanced, extended

Summary

FinSTaR (Financial Time Series Thinking and Reasoning) is a novel model designed to address the consistent failure of general time series reasoning models (TSRMs) in the financial domain. It introduces a general $2\times 2$ capability taxonomy, crossing single-entity vs. multi-entity analysis with current state assessment vs. future behavior prediction. This taxonomy is instantiated as ten financial reasoning tasks, forming the FinTSR-Bench benchmark based on 250 S&P 500 stocks from 2010–2025, with approximately 35K training samples. FinSTaR employs distinct chain-of-thought (CoT) strategies: Compute-in-CoT for deterministic assessment and Scenario-Aware CoT for stochastic prediction. The model achieved 78.9% average accuracy on FinTSR-Bench, substantially outperforming 15+ LLM and TSRM baselines, with joint training proving complementary and mutually reinforcing.

Key takeaway

For AI Scientists developing financial models, FinSTaR demonstrates that distinguishing between deterministic assessment and stochastic prediction is crucial for robust performance. You should implement specialized chain-of-thought strategies, like Compute-in-CoT for calculable outcomes and Scenario-Aware CoT for uncertain forecasts, to improve accuracy and interpretability. This approach yields superior results compared to general LLMs and traditional TSRMs, especially when dealing with financial market volatility and unobservable factors.

Key insights

FinSTaR enhances financial time series reasoning by separating deterministic assessment from stochastic prediction using tailored CoT strategies.

Principles

• Financial reasoning requires distinct deterministic assessment and stochastic prediction modes.
• A $2\times 2$ taxonomy (entity scope, temporal scope) categorizes financial reasoning tasks.
• Joint training across diverse reasoning categories is mutually reinforcing.

Method

FinSTaR uses Compute-in-CoT for assessment (extract, compute, classify) and Scenario-Aware CoT for prediction (extract, compute, scenario analysis, assessment, judgment) on financial time series data.

In practice

• Apply Compute-in-CoT for tasks with computable answers from raw data.
• Use Scenario-Aware CoT to model uncertainty in future predictions.
• Fine-tune LLMs with LoRA on financial time series data.

Topics

• Financial Time Series
• LLM Reasoning
• Chain-of-Thought
• FinTSR-Bench
• S&P 500 Stocks
• Financial Modeling

Code references

• seunghan96/FinSTaR

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.