GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models

TL;DR

This paper presents GPT-InvestAR, a framework that uses large language models to analyze annual reports, generate features, and improve stock investment strategies, demonstrating promising results compared to the S&P 500.

Contribution

It introduces a novel approach combining LLMs and machine learning for automated financial report analysis and stock prediction, with open-source code for reproducibility.

Findings

Outperforms S&P 500 in walkforward tests

Creates a dataset of LLM-extracted features and stock data

Provides a scalable framework for future financial analysis

Abstract

Annual Reports of publicly listed companies contain vital information about their financial health which can help assess the potential impact on Stock price of the firm. These reports are comprehensive in nature, going up to, and sometimes exceeding, 100 pages. Analysing these reports is cumbersome even for a single firm, let alone the whole universe of firms that exist. Over the years, financial experts have become proficient in extracting valuable information from these documents relatively quickly. However, this requires years of practice and experience. This paper aims to simplify the process of assessing Annual Reports of all the firms by leveraging the capabilities of Large Language Models (LLMs). The insights generated by the LLM are compiled in a Quant styled dataset and augmented by historical stock price data. A Machine Learning model is then trained with LLM outputs as features. The walkforward test results show promising outperformance wrt S&P500 returns. This paper intends to provide a framework for future work in this direction. To facilitate this, the code has been released as open source.