Phd Seminar Series: Using Machine Learning to Estimate the Effect of General Partners on Venture Capital Performance

Using Machine Learning to Estimate the Effect of General Partners on Venture Capital Performance

Speaker: Pietro Morino (Bocconi University)

Abstract: Despite extensive research on performance persistence in the venture capital (VC) industry, evidence supporting the common assumption that general partners drive such persistence remains limited. In this paper, we employ machine learning methods to quantify the effect (if any) of general partners on performance persistence across multiple VC funds. Analyzing 29,021 quarterly observations from 722 funds managed by 811 general partners between 1997 and 2022, we document statistically significant, albeit modest effects of general partners on performance persistence. These magnitudes are substantially smaller than those found in the literature, highlighting the limited external validity of traditional methods. We also find that general partner effects consistently exceed VC firm effects, suggesting that individual-level analyses provide greater insights than firm-level ones. Broadly, our results suggest that most of the variation in VC performance is not attributable to the organizational characteristics of VC firms and can be explained only partially by the individuals managing them.