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New benchmark evaluates LLM agents' earnings forecasting accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced EPSBench, a new benchmark designed to evaluate the capabilities of Large Language Model (LLM) agents in forecasting company earnings. The benchmark involves agents analyzing financial documents, earnings call transcripts, and news to estimate income statement drivers, which are then converted into GAAP diluted earnings per share (EPS). Six models were tested, with three showing no significant difference in point forecast error compared to analyst consensus, while the other three performed worse. Additionally, prediction intervals for most models failed to achieve their stated coverage, often forecasting too low. AI

IMPACT This benchmark could drive improvements in LLM agents' financial analysis capabilities, potentially impacting investment strategies and financial reporting.

RANK_REASON The cluster contains an academic paper introducing a new benchmark for evaluating LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark evaluates LLM agents' earnings forecasting accuracy

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The cluster contains an academic paper introducing a new benchmark for evaluating LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yanbin Yin, Chuyu Xu, Liqin Ye, Jing Peng, Chao Zhang ·

    EPSBench: Benchmarking LLM Agents for Earnings Forecasting

    arXiv:2610.38293v1 Announce Type: cross Abstract: Quarterly earnings forecasting requires an agent to connect financial evidence to the income-statement line items that produce a company's reported earnings. We introduce EPSBench, a benchmark for studying this ability across 149 …

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