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

2024/07/05 by Ekin Ozince, Ozince, Ekin, Yiğit Ihlamur +1 · 2 citations
Business, Management and Accounting · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Private Equity and Venture Capital

paper · pdf · doi:10.48550/arxiv.2407.04885

openalex publication_date 2024/07/05 · openalex created_date 2024/07/11 · openalex updated_date 2026/07/28

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.

Cited by

• LLM Agents as VC investors: Predicting Startup Success via RolePlay-Based Collective Simulation
• From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital

Related

• SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performance
• From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
• Using Deep Learning to Find the Next Unicorn: A Practical Synthesis
• LLM-AR: LLM-powered Automated Reasoning Framework
• CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction