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A deep dive into Diag Davenport's ‘Predictably Bad Investments'
· 08.03.2024 · 13:30:35 ··· Freitag ⭐ 1 🎬 104
📺 Giacomo Mollo
A critical dive into Diag Davenport’s 2022 Paper ‘Predictably Bad Investments: Evidence from Venture Capitalists'
This is an exploration of s a 2022 study by Dr. Davenport from Princeton on the topic of "Predictably Bad Investments" in the venture capital world, highlighting the potential of machine learning in predicting investment outcomes. Dr. Davenport asserts that up to half of VC investments are predictably bad, drawing attention to the gap between current investment practices and the possibilities offered by advanced analytics. The discussion covers the methodology, data analysis, and key findings from the study, including the significant predictors of startup success or failure, such as the educational background of founders and their prior investment history. Additionally, it addresses the biases in decision-making processes within the VC industry and contrasts the investment strategies of top-performing firms with those performing poorly. The video also delves into the limitations of current predictive models and the role of machine learning in refining investment decisions, suggesting a shift towards more data-driven approaches in the VC space.
Link to the paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4135861
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References
P A Gompers , W Gornall , S N Kaplan , I A Strebulaev, How do venture capitalists make decisions, Journal of Financial Economics , volume 135 , issue 1 , p. 169 - 190 Posted: 2020 [https://econpapers.repec.org/article/eeejfinec/v_3a135_3ay_3a2020_3ai_3a1_3ap_3a169-190.htm]
Kleinberg, J., H. Lakkaraju, J. Leskovec, J. Ludwig, and S. Mullainathan (2018a). Human decisions and machine predictions. The Quarterly Journal of Economics 133 (1), 237–293.[https://www.cs.cornell.edu/home/kleinber/w23180.pdf]
Lyonnet, V. and L. H. Stern (2022). Venture capital (mis) allocation in the age of ai. Fisher College of Business Working Paper (2022-03) [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4035930]
Listen to all episodes on audio:
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#venturecapital #startup #innovation
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00:00 Introduction to Dr. Davenport's study on predictably bad investments
00:37 Discussion on the methodology and structure of the study
01:07 Machine learning's role in predicting VC investment outcomes
02:07 Overview of the investment prediction problem and startup life cycle
03:16 Definitions of exit strategies and their probabilities
04:32 Expected return and the influence of bias in investment decisions
06:18 Analyzing predictably bad investments through machine learning
08:06 Data set and predictors used in the study
09:46 Sample construction and division for machine learning
10:58 Insights from applying machine learning to VC investment prediction
13:20 Evaluating the machine learning model's predictive power
15:48 Identifying early-stage predictors of startup success
18:14 Contrasting investment decision criteria of top vs. bottom VC firms
20:06 The persistence of decision-making biases in the VC space
21:14 Limitations of the study and areas for future research
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Get in touch:
Instagram: https://www.instagram.com/giacxxx
Twitter: https://twitter.com/giacomomollo
Email: giacomo@in3.ventures
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Venture capital, Behavioral finance, Machine learning, Artificial intelligence, startup investments, predictably bad investments, VC decision-making, investment predictions, behavioral decision making, Dr. Davenport, early-stage startups, investment success predictors
· 08.03.2024 · 13:30:35 ··· Freitag
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