Evaluating Large Language Models for Financial Reasoning: A CFA-Based Benchmark Study
It addresses the need for systematic evaluation of LLMs in specialized financial contexts, offering practitioners evidence-based guidance for model selection and optimization, though it is incremental in benchmarking existing methods on new data.
This study tackled the problem of evaluating large language models for financial reasoning by testing them on 1,560 CFA exam questions, finding that reasoning-oriented models performed best in zero-shot settings and a novel RAG pipeline significantly improved accuracy for complex scenarios.
The rapid advancement of large language models presents significant opportunities for financial applications, yet systematic evaluation in specialized financial contexts remains limited. This study presents the first comprehensive evaluation of state-of-the-art LLMs using 1,560 multiple-choice questions from official mock exams across Levels I-III of CFA, most rigorous professional certifications globally that mirror real-world financial analysis complexity. We compare models distinguished by core design priorities: multi-modal and computationally powerful, reasoning-specialized and highly accurate, and lightweight efficiency-optimized. We assess models under zero-shot prompting and through a novel Retrieval-Augmented Generation pipeline that integrates official CFA curriculum content. The RAG system achieves precise domain-specific knowledge retrieval through hierarchical knowledge organization and structured query generation, significantly enhancing reasoning accuracy in professional financial certification evaluation. Results reveal that reasoning-oriented models consistently outperform others in zero-shot settings, while the RAG pipeline provides substantial improvements particularly for complex scenarios. Comprehensive error analysis identifies knowledge gaps as the primary failure mode, with minimal impact from text readability. These findings provide actionable insights for LLM deployment in finance, offering practitioners evidence-based guidance for model selection and cost-performance optimization.