  This research dissects financial equity research reports (ERRs) by 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 4940 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. 78.7% of the questions in ERRs can be automated. Those automatable
question consist of 48.2% text-extractable (suited to processing by large
language models, LLMs) and 30.5% database-extractable questions. Only 21.3% 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. 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, will be made
available online after peer review.
