Dr. Russ Goyenko, Associate Professor of Finance at McGill University discusses with Dr. Hossein Kazemi how large language models can, and soon they will produce more accurate analyst forecasts.
First, the length of MD&A section on its own is negatively associated with future earnings surprises and firm returns in the cross-section.
Second, neither sentiment-based nor classic NLPs approaches are able to “learn” from the past managerial discussions to forecast future earnings.
Third, only “finance-trained” LLMs have the capacity to “understand’’ the contexts of previous discussions to predict both positive and negative earnings surprises, and future firm returns.
Our evidence indicates significant, and somewhat hidden in the complexity of presentations, informational content of publicly disclosed corporate filings, and superior (to human) abilities of more recent AI models to identify it.
The Financial Data Professional Institute (FDPI), established by CAIA Association, has designed a self-study program to provide financial professionals with an efficient path to learn the essential aspects of financial data science.
We are proud to have FIAM as an Association Partner sponsoring today’s event. FIAM’s focus of its activities is on issues, challenges & opportunities resulting from the rapid deployment of disruptive technologies in Finance, particularly in the use of AI/ML in asset management. FIAM has chosen to promote the FDP designation as a pillar of its activities.
Presentation will last approximately 45 minutes followed by 15 minutes for Q&A. Session will be recorded, and the recording link will be sent to those who have registered for the webinar.