Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models

TL;DR

This paper investigates whether large language models exhibit company-specific biases in financial sentiment analysis, assesses how such biases influence investor behavior and stock prices, and provides empirical evidence using Japanese financial texts.

Contribution

It introduces a method to quantify company-specific bias in LLM-based sentiment analysis and models its potential impact on stock prices and investor decisions.

Findings

LLMs show measurable company-specific sentiment biases.

Biases can significantly influence stock price movements.

Empirical analysis confirms the presence of biases in Japanese financial texts.

Abstract

This study aims to evaluate the sentiment of financial texts using large language models~(LLMs) and to empirically determine whether LLMs exhibit company-specific biases in sentiment analysis. Specifically, we examine the impact of general knowledge about firms on the sentiment measurement of texts by LLMs. Firstly, we compare the sentiment scores of financial texts by LLMs when the company name is explicitly included in the prompt versus when it is not. We define and quantify company-specific bias as the difference between these scores. Next, we construct an economic model to theoretically evaluate the impact of sentiment bias on investor behavior. This model helps us understand how biased LLM investments, when widespread, can distort stock prices. This implies the potential impact on stock prices if investments driven by biased LLMs become dominant in the future. Finally, we conduct an empirical analysis using Japanese financial text data to examine the relationship between firm-specific sentiment bias, corporate characteristics, and stock performance.