Persuading Investors: A Video-Based Study
Persuasive communication functions not only through content but also delivery, e.g., facial expression, tone of voice, and diction. This paper examines the persuasiveness of delivery in start-up pitches. Using machine learning (ML) algorithms to process full pitch videos, we quantify persuasion in visual, vocal, and verbal dimensions. Positive (i.e., passionate, warm) pitches increase funding probability. Yet conditional on funding, high-positivity startups underperform. Women are more heavily judged on delivery when evaluating single-gender teams, but they are neglected when co-pitching with men in mixed-gender teams. Using an experiment, we show persuasion delivery works mainly through leading investors to form inaccurate beliefs.
The paper was previously circulated under the title "Human Interactions and Financial Investment: A Video-Based Approach." We thank seminar participants at ASSA, Boston College, Chicago, Colorado, EFA, Finance in the Cloud, Georgia State, IESEG, Indiana, MFA, NBER Behavioral Finance Meeting, NBER Summer Institute (Corporate Finance; Entrepreneurship), RFS/GSU FinTech Conference, SFS Cavalcade, Washington St. Louis, WFA, Yale, and numerous colleagues for helpful comments. Ran You provided excellent research assistance. The project received IRB approval at Yale University. All errors are our own. For code examples and instructions to implement the method, please email the authors. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.