Large Language Models as Financial Analysts: Sector-Aware Reasoning for Investment Decisions

Abstract

While large language models (LLMs) have shown promise for financial analysis and asset selection, existing approaches tend to rely on a uniform analytical framework across industries. Such approaches fail to integrate industry context into their investment reasoning, unlike financial analysts who leverage sector expertise. We propose a sector-aware analytical framework that conditions LLM financial analysis on industry-specific knowledge through the Global Industry Classification Standard. Our approach assigns the LLM sector-specific analyst roles through tailored instructions that integrate industry context directly into the reasoning process. The model predicts monthly return direction probabilities for individual assets by analyzing financial characteristics through sector-specific perspectives, then selects the assets predicted to be top performers for portfolio construction. We validate our framework using S&P 500 constituents from 2012 to 2021, demonstrating superior risk-adjusted returns that significantly outperform comparative strategies. These findings demonstrate that incorporating sector expertise enables LLMs to generate substantial economic value in asset selection while providing human-understandable, industry-contextualized investment insights. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.