AI Hallucination in Financial Research

Abstract

This article examines the risk of hallucination when using large language models (LLMs) for financial research and demonstrates the unreliability of off-the-shelf tools such as ChatGPT for tasks requiring precise financial reasoning. A simple, replicable experiment was conducted using the free online version of ChatGPT-4o. The same prompt concerning capital budgeting studies advocating dual discount rates for cash inflows and outflows was submitted on three consecutive days to assess the consistency and accuracy of the model’s responses. The results reveal extensive fabrication of academic references and significant logical inconsistency. Approximately half of the cited studies were fictitious and could not be verified. In addition, the model produced contradictory responses regarding the appropriate discount rates for different cash flows, with its recommendations reversing across days. These findings provide further evidence that off-the-shelf LLMs remain unreliable for precise financial applications and highlight the need for careful use and independent verification by researchers.