InvestLogicBench: New Benchmark for Evaluating Financial LLMs

A new evaluation standard named InvestLogicBench has been launched to assess large language models (LLMs) specifically in the realm of financial investments. This benchmark is outlined in a paper available on arXiv (2608.06108) and critiques existing evaluation techniques for financial LLMs as insufficient. It highlights that conventional static question answering overlooks the nuances of investment decision-making, while profit and loss metrics fail to indicate if a successful action was based on sound reasoning, aligned with the investor's profile, or simply fortuitous. InvestLogicBench offers a process-oriented evaluation framework, featuring 201,247 recorded decisions from 151 actual investors. Each benchmark episode captures a P→E→R→D→O trace, detailing the investor's Profile, relevant market Events, investment Reasoning, actionable Decision, and delayed Outcome. The benchmark's release includes data on profile construction and event timing. The paper raises a pivotal question about whether the community is measuring consequential agents correctly. Ultimately, this benchmark seeks to deliver a more nuanced and individualized assessment of financial LLMs, acknowledging that investment proficiency is inherently unique, as identical market data can lead to varying actions based on individual investor goals, timelines, portfolios, and risk thresholds.