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