A fused large language model for predicting startup success

Abdurahman Maarouf, Stefan Feuerriegel and Nicolas Pröllochs

European Journal of Operational Research, 2025, vol. 322, issue 1, 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.

Keywords: Machine learning; Text mining; Large language models; Deep learning; Venture capital (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:322:y:2025:i:1:p:198-214

DOI: 10.1016/j.ejor.2024.09.011

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