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AI in Investment Analysis: LLMs for Equity Stock Ratings by Papasotiriou, Kassiani; Sood, Srijan; Reynolds, Shayleen; Balch, Tucker is a scholarly article available to read on EtoBox.

What is AI in Investment Analysis: LLMs for Equity Stock Ratings about?

Investment Analysis is a cornerstone of the Financial Services industry. The rapid integration of advanced machine learning techniques, particularly Large Language Models (LLMs), offers opportunities to enhance the equity rating process. This paper explores the application of LLMs to generate multi-horizon stock ratings by ingesting diverse datasets. Traditional stock rating methods rely heavily on the expertise of financial analysts, and face several challenges such as data overload, inconsistencies in filings, and delayed reactions to market events. Our study addresses these issues by leveraging LLMs to improve the accuracy and consistency of stock ratings. Additionally, we assess the efficacy of using different data modalities with LLMs for the financial domain. We utilize varied datasets comprising fundamental financial, market, and news data from January 2022 to June 2024, along with GPT-4-32k (v0613) (with a training cutoff in Sep. 2021 to prevent information leakage). Our results show that our benchmark method outperforms traditional stock rating methods when assessed by forward returns, specially when incorporating financial fundamentals. While integrating news data improve

Author

Papasotiriou, Kassiani; Sood, Srijan; Reynolds, Shayleen; Balch, Tucker

Published

2024

Language

EN