Human Interactions and Financial Investment: A Video-Based Approach

93 Pages Posted: 4 Jun 2020 Last revised: 25 Jun 2020

See all articles by Allen Hu

Allen Hu

Yale School of Management

Song Ma

Yale School of Management

Date Written: June 15, 2020

Abstract

We quantify human interactions in three-V dimensions---visual, vocal, and verbal, using machine learning (ML)-based algorithms with videos as data input. We apply the method to videos of startups pitching investors for funding. We find that venture investors are more likely to invest in startups showing more positivity (i.e., happy, warm, passionate), even though those startups underperform conditional on funding. Investors do not seem to correctly predict startup quality using interactions. Instead, we document interaction-induced biases---using video analysis and an experiment, we show that interaction-induced biases can be explained by a taste-based channel (18 percent) and inaccurate beliefs (82 percent).

Keywords: Behavioral Economics, Social Interactions, Video Data, Machine Learning, Entrepreneurship

JEL Classification: D91, G41, C55, G24

Suggested Citation

Hu, Allen and Ma, Song, Human Interactions and Financial Investment: A Video-Based Approach (June 15, 2020). Available at SSRN: https://ssrn.com/abstract=3583898 or http://dx.doi.org/10.2139/ssrn.3583898

Allen Hu

Yale School of Management ( email )

165 Whitney Ave
New Haven, CT 06511

HOME PAGE: http://www.anallenhu.com

Song Ma (Contact Author)

Yale School of Management ( email )

165 Whitney Ave
P.O. Box 208200
New Haven, CT 06511
United States

HOME PAGE: http://faculty.som.yale.edu/songma/

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