GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models
This provides a simplified framework for stock investors to analyze annual reports efficiently, though it is incremental as it builds on existing LLM and machine learning techniques.
The paper tackles the cumbersome analysis of lengthy annual reports for stock investment by using Large Language Models to extract insights and augment them with historical price data, achieving promising outperformance compared to S&P500 returns in walkforward tests.
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.