Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques

TL;DR

This paper presents a novel framework using large language models and machine learning to predict startup success based on founder features, aiming to enhance venture capital decision-making.

Contribution

It introduces a new approach combining LLM prompting, feature engineering, and automated labeling for founder assessment in VC.

Findings

Identified relationships between founder traits and startup success

Demonstrated the effectiveness of LLM-driven feature extraction

Proposed a scalable framework for VC decision support

Abstract

This study explores the application of large language models (LLMs) in venture capital (VC) decision-making, focusing on predicting startup success based on founder characteristics. We utilize LLM prompting techniques, like chain-of-thought, to generate features from limited data, then extract insights through statistics and machine learning. Our results reveal potential relationships between certain founder characteristics and success, as well as demonstrate the effectiveness of these characteristics in prediction. This framework for integrating ML techniques and LLMs has vast potential for improving startup success prediction, with important implications for VC firms seeking to optimize their investment strategies.