EPSBench: Benchmarking LLM Agents for Earnings Forecasting
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabEPSBench is a benchmark for testing whether LLM agents can forecast quarterly earnings by reading filings, earnings-call transcripts, and news, then estimating income-statement drivers with cited evidence. A shared accounting calculator converts these drivers into GAAP diluted EPS. Across 149 firm-quarters from 86 US companies, six models were compared against date-matched analyst consensus, and their prediction intervals were checked for stated coverage.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
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EPSBench is a benchmark for testing whether LLM agents can forecast quarterly earnings by reading filings, earnings-call transcripts, and news, then estimating income-statement drivers with cited evidence. A shared accounting calculator converts these drivers into GAAP diluted EPS. Across 149 firm-quarters from 86 US companies, six models were compared against date-matched analyst consensus, and their prediction intervals were checked for stated coverage.
A READER'S EVIDENCE CHECKHow to read this signalSix questions about claims, sources, risks, and open checks
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- 01Who is making each claim?
The claimant and whether each claim was measured or asserted are not recorded in this public brief.
- 02What was actually tested?
Not recorded per claim. Check the source for sample size, tasks, model and benchmark versions, and reproducibility.
- 03Where are the sources?
Original link: huggingface.co. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
The benchmark covers 149 firm-quarters from 86 US companies, and source claims not independently checked.
- 05What is at risk if wrong?
Not assessed per claim. Consider safety, privacy, cost, and adoption decisions before relying on the result.
- 06What would change the view?
No claim-specific check is recorded. An inspectable primary method or independent replication could change the assessment.
Key findings from the stored brief
- Three of six evaluated models show no detectable paired difference in point forecast error from analyst consensus.
- Nominal 80% prediction intervals contain reported EPS only 37–70% of the time across the six models.
- Most interval misses fall above the upper bound because the forecasts run low.
- Most of the error gap between models traces to operating-income estimates.
The benchmark covers 149 firm-quarters from 86 US companies, and source claims not independently checked.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
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Why this may matter
The benchmark targets evidence-grounded financial forecasting agents and shows that point accuracy can match consensus while uncertainty estimates remain poorly calibrated, a gap relevant to anyone relying on agent-produced forecast intervals.
Limits and open questions
The benchmark covers 149 firm-quarters from 86 US companies, and source claims not independently checked.
Organization-specific financial, security, and operational effects require the organization's own context and review.
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