A Familiar Face: Measuring Visual Similarity in Venture Capital
本文提出“面部距离”这一基于面部识别模型的新指标，发现创业者和投资者的面部相似性显著预测投资决策，尤其在不确定性高时更明显，且此类投资成功率较低，表明视觉同质性是一种成本高昂的启发式偏差。
Receiving venture capital can dramatically shape the trajectory and ultimate success of startup firms. Because these investments occur under significant uncertainty, investors rely heavily on subjective assessments, often backing entrepreneurs who are similar to themselves. Yet, research on this tendency has largely relied on coarse demographic categories, potentially obscuring more granular visually assessed forms of similarity that influence investment decisions. This paper introduces “face distance,” a novel measure of visual similarity derived from facial recognition models, to explore this subtle channel. Using a mixed-methods approach that combines observational data from a top startup accelerator with an online controlled experiment, I find that facial similarity between an entrepreneur and an investor is a powerful predictor of investment. This relationship is more pronounced when there is relatively higher uncertainty, suggesting that facial similarity is a heuristic investors tend to rely on when concrete “hard” information is scarce. In addition, investments between visually similar entrepreneurs and investors underperform in terms of successful exits, which is consistent with a costly distortion. These findings highlight that homophily operates at a highly granular, visual level in early-stage investment and that this is not only inequitable but also a potentially inefficient heuristic. Funding: The author gratefully acknowledges funding from the UCLA Behavioral Lab. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2025.0490 .