A fused large language model for predicting startup success
开发了一种融合大语言模型，利用初创企业在Crunchbase上的文本描述和基本信息预测其成功概率，为投资者提供决策支持工具。
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. • We develop a machine learning approach to detect successful startups. • We propose a new fused large language model to predict startup success. • Our model uses textual descriptions and fundamental information for prediction. • Our evaluation is based on data for N = 20,172 startups from Crunchbase. • Results show that our fused large language model is better than common baselines.