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Persuading Investors: A Video-Based Study

Author

Listed:
  • Allen Hu
  • Song Ma

Abstract

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.

Suggested Citation

  • Allen Hu & Song Ma, 2021. "Persuading Investors: A Video-Based Study," NBER Working Papers 29048, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29048
    Note: CF LS PR
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    Cited by:

    1. Alexopoulos, Michelle & Han, Xinfen & Kryvtsov, Oleksiy & Zhang, Xu, 2024. "More than words: Fed Chairs’ communication during congressional testimonies," Journal of Monetary Economics, Elsevier, vol. 142(C).
    2. Kolbe, Maura & Mansouri, Sasan & Momtaz, Paul P., 2022. "Why do video pitches matter in crowdfunding?," Journal of Economics and Business, Elsevier, vol. 122(C).
    3. Ramana Nanda, "undated". "Priors, Experiments, Learning and Persuasion in (Bayesian) Entrepreneurial Finance," Harvard Business School Working Papers 25-020, Harvard Business School.
    4. Curti, Filippo & Kazinnik, Sophia, 2023. "Let's face it: Quantifying the impact of nonverbal communication in FOMC press conferences," Journal of Monetary Economics, Elsevier, vol. 139(C), pages 110-126.
    5. Onur Bayar & Emre Kesici, 2024. "The impact of social media on venture capital financing: evidence from Twitter interactions," Review of Quantitative Finance and Accounting, Springer, vol. 62(1), pages 195-224, January.
    6. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    7. Alekseeva, Liudmila & Fontana, Silvia Dalla & Genc, Caroline & Ranjbar, Hedieh Rashidi, 2022. "From in-person to online: the new shape of the VC industry," SocArXiv 3pc4z, Center for Open Science.
    8. David Hardt & Lea Mayer & Johannes Rincke, 2023. "Who Does the Talking Here? The Impact of Gender Composition on Team Interactions," CESifo Working Paper Series 10550, CESifo.
    9. Dimitrios Kanelis & Pierre L. Siklos, 2022. "Emotion in Euro Area Monetary Policy Communication and Bond Yields: The Draghi Era," CQE Working Papers 10322, Center for Quantitative Economics (CQE), University of Muenster.
    10. Agam Shah & Arnav Hiray & Pratvi Shah & Arkaprabha Banerjee & Anushka Singh & Dheeraj Eidnani & Sahasra Chava & Bhaskar Chaudhury & Sudheer Chava, 2024. "Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis," Papers 2402.11728, arXiv.org, revised Oct 2024.
    11. Ivo Blohm & Moritz Schulz & Jan Marco Leimeister, 2024. "Hedonic Signals in Crowdfunding," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(6), pages 757-775, December.
    12. Huang, Xing & Ivković, Zoran & Jiang, John Xuefeng & Wang, Isabel Yanyan, 2023. "Angel investment and first impressions," Journal of Financial Economics, Elsevier, vol. 149(2), pages 161-178.
    13. Gonzalez-Uribe, Juanita & Hmaddi, Ouafaa, 2022. "The multi-dimensional impacts of business accelerators: what does the research tell us?," LSE Research Online Documents on Economics 115461, London School of Economics and Political Science, LSE Library.

    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G4 - Financial Economics - - Behavioral Finance
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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