SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities
SECQUE benchmark evaluates LLM performance on complex SEC filings analysis tasks, demonstrating nuances across comparison, ratio, risk, and insight categories.
What it examines
This paper introduces SECQUE, a benchmark for assessing LLMs on real-world financial analysis tasks using SEC filings. It presents domain-specific questions, LLM-based judging methods, and ablation studies to evaluate financial reasoning, numerical computation, and integration of textual and tabular data.
What it concludes
Results reveal varied model performance, with promise in tasks like ratio analysis and risk assessment. The research suggests future improvements by expanding document types and handling multiple valid answers. Applications include enhancing financial AI tools, supporting risk management, and aiding data-driven investment decisions.
Evidence objects
SECQUE introduces a benchmark using real SEC filings with 565 expert-written questions across four categories, showing even GPT-4o struggles with complex reasoning and analyst insights, spotlighting evaluation challenges in finance.
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A notable breakthrough is SECQUE-judge, an evaluation system that uses LLM judges, aligning closely with human evaluations and outperforming simpler methods when aggregating advanced LLM judgments in specialized finance tasks.
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Extensive analysis with text and table parsing, varied prompt configurations, and ablation studies revealed performance metrics, potential LLM bias, and challenges in capturing nuanced financial analysis, providing notable balanced insights.
key_findings bullet 3 · key_findings · validation V0
The paper introduces SECQUE, an inventive benchmark evaluating LLMs on real-world SEC filings. Its novel approach uses SECQUE-Judge to assess complex long-context financial data, blending ratio calculations, risk, and comparison analysis. This tailored and detailed design makes the work uniquely engaging, providing valuable insights and applicability to financial quantitative analysis.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
Abstract: We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leverag… ▽ More We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. Additionally, we provide an extensive analysis of various models' performance on our benchmark. By making SECQUE publicly available, we aim to facilitate further research and advancements in financial AI. △ Less
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