Journal article

Authors list: Maarouf, Abdurahman; Feuerriegel, Stefan; Pröllochs, Nicolas

Publication year: 2025

Pages: 198-214

Journal: European Journal of Operational Research

Volume number: 322

Issue number: 1

ISSN: 0377-2217

eISSN: 1872-6860

Open access status: Hybrid

DOI Link: https://doi.org/10.1016/j.ejor.2024.09.011

Publisher: Elsevier

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.

Harvard Citation style: Maarouf, A., Feuerriegel, S. and Pröllochs, N. (2025) A fused large language model for predicting startup success, European Journal of Operational Research, 322(1), pp. 198-214. https://doi.org/10.1016/j.ejor.2024.09.011

APA Citation style: Maarouf, A., Feuerriegel, S., & Pröllochs, N. (2025). A fused large language model for predicting startup success. European Journal of Operational Research. 322(1), 198-214. https://doi.org/10.1016/j.ejor.2024.09.011