pith:3HJXFK2T
FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models
A 2x2 taxonomy of time series capabilities with tailored chain-of-thought strategies enables 78.9 percent accuracy on financial reasoning tasks from S&P stocks.
arxiv:2605.03460 v2 · 2026-05-05 · cs.AI · cs.LG
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Claims
The proposed method achieves 78.9% average accuracy on FinTSR-Bench, substantially outperforming LLM and TSRM baselines. Furthermore, we show that the four capability categories are complementary and mutually reinforcing through joint training, and that Scenario-Aware CoT consistently improves prediction accuracy over standard CoT.
That the ten tasks constructed from S&P stocks adequately capture the distinctive challenges of financial reasoning and that the deterministic-versus-stochastic distinction is the primary reason current models underperform.
FinSTaR reaches 78.9% accuracy on a new financial time series reasoning benchmark by applying Compute-in-CoT for deterministic assessments and Scenario-Aware CoT for stochastic predictions.
Receipt and verification
| First computed | 2026-05-26T01:03:32.028733Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/3HJXFK2TZWZXYCIKDKAOADFO2L \
| jq -c '.canonical_record' \
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# expect: d9d372ab53cdb37c090a1a80e00caed2c4123d041f6c0a62f0c4f883caefe00b
Canonical record JSON
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