A fused large language model for predicting startup success

Maarouf, Abdurahman; Feuerriegel, Stefan und Pröllochs, Nicolas (2025): A fused large language model for predicting startup success. In: European Journal of Operational Research, Bd. 322, Nr. 1: S. 198-214

Abstract

Investors are continuously seeking profitable investment opportunities in startups and, hence, for effective decision-making, need to predict a startup’s probability of success. Nowadays, investors can use not only various fundamental information about a startup (e.g., the age of the startup, the number of founders, and the business sector) but also textual description of a startup’s innovation and business model, which is widely available through online venture capital (VC) platforms such as Crunchbase. To support the decision-making of investors, we develop a machine learning approach with the aim of locating successful startups on VC platforms. Specifically, we develop, train, and evaluate a tailored, fused large language model to predict startup success. Thereby, we assess to what extent self-descriptions on VC platforms are predictive of startup success. Using 20,172 online profiles from Crunchbase, we find that our fused large language model can predict startup success, with textual self-descriptions being responsible for a significant part of the predictive power. Our work provides a decision support tool for investors to find profitable investment opportunities.

• Dokumententyp: Zeitschriftenartikel
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
• ISSN: 03772217
• Sprache: Englisch