Fostering the future of finance
through conversations and collaborations
between academics and practitioners.
Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
|
Session Overview |
| Session | ||
Session 2.01: What Do Early Stage Investors Ask? An LLM Analysis of Expert Calls
| ||
    | ||
What Do Early Stage Investors Ask? An LLM Analysis of Expert Calls 1University of Michigan, United States of America; 2Ohio State University, United States of America We analyze how early-stage investors evaluate potential investments by using ChatGPT to analyze 5,143 expert consultation calls. Companies discussed in these calls are 15 percentage points more likely to receive financing in the following quarter. Positive signals about technology integration and customer acquisition increase deal likelihood by 14% and 10.5%, respectively, with their predictive power declining by over 75% for mature companies. Market analysis and business strategy discussions – comprising over 40% of call content—show minimal predictive power for investment outcomes. Our findings document both how investors overcome information asymmetries in early-stage investing and a misalignment between the information they seek and the information that predicts investment outcomes. Methodologically, we demonstrate the potential of LLMs to extract nuanced insights from complex qualitative data. | ||