| Abstract: |
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 improves short-term
performance, substituting detailed news summaries with sentiment scores
reduces token use without loss of performance. In many cases, omitting news
data entirely enhances performance by reducing bias. Our research shows that
LLMs can be leveraged to effectively utilize large amounts of multimodal
financial data, as showcased by their effectiveness at the stock rating
prediction task. Our work provides a reproducible and efficient framework for
generating accurate stock ratings, serving as a cost-effective alternative to
traditional methods. Future work will extend to longer timeframes, incorporate
diverse data, and utilize newer models for enhanced insights. |