Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4

This research dissects financial equity research reports (ERRs) by systematically mapping their content into categories. There is insufficient empirical analysis of the questions answered in ERRs. In particular, it is not understood how frequently certain information appears, what information is considered essential, and what information requires human judgment to distill into an ERR. The study analyzes 72 ERRs sentence-by-sentence, classifying their 4964 sentences into 169 unique question archetypes. We did not predefine the questions but derived them solely from the statements in the ERRs. This approach provides an unbiased view of the content of the observed ERRs. Subsequently, we used public corporate reports to classify the questions' potential for automation. Answers were labeled “text-extractable” if the answers to the question were accessible in corporate reports. 75.15% of the questions in ERRs can be automated using text extraction from text sources. Those automatable questions consist of 51.91% text-extractable (suited to processing by large language models, LLMs) and 24.24 % database-extractable questions. Only 24.85% of questions require human judgment to answer. We empirically validate, using Llama-3-70B and GPT-4-turbo-2024-04-09, that recent advances in language generation and information extraction enable the automation of approximately 80 % of the statements in ERRs. Surprisingly, the models complement each other's strengths and weaknesses well, indicating strong ensemble potential. The research confirms that the current writing process of ERRs can likely benefit from additional automation, improving quality and efficiency. The research thus allows us to quantify the potential impacts of introducing large language models in the ERR writing process. The full question list, including the archetypes and their frequency, are available online (janspoerer.github.io/pop-spoerer-2025-financial-report-data).