Predicting startup success presents a formidable challenge due to the inherently volatile landscape of the entrepreneurial ecosystem. The advent of extensive databases like Crunchbase jointly with available open data enables the application of machine learning and artificial intelligence for more accurate predictive analytics. This paper focuses on startups at their Series B and Series C investment stages, aiming to predict key success milestones such as achieving an Initial Public Offering (IPO), attaining unicorn status, or executing a successful Merger and Acquisition (M\&A). We introduce novel deep learning model for predicting startup success, integrating a variety of factors such as funding metrics, founder features, industry category. A distinctive feature of our research is the use of a comprehensive backtesting algorithm designed to simulate the venture capital investment process. This simulation allows for a robust evaluation of our model's performance against historical data, providing actionable insights into its practical utility in real-world investment contexts. Evaluating our model on Crunchbase's, we achieved a 14 times capital growth and successfully identified on B round high-potential startups including Revolut, DigitalOcean, Klarna, Github and others. Our empirical findings illuminate the importance of incorporating diverse feature sets in enhancing the model's predictive accuracy. In summary, our work demonstrates the considerable promise of deep learning models and alternative unstructured data in predicting startup success and sets the stage for future advancements in this research area.

翻译：预测创业成功因创业生态系统固有的波动性而极具挑战性。Crunchbase等大型数据库与可获取的开放数据共同推动了机器学习和人工智能在更精准预测分析中的应用。本文聚焦于处于B轮及C轮融资阶段的初创企业，旨在预测其实现首次公开募股（IPO）、达到独角兽地位或成功完成并购（M&A）等关键里程碑。我们提出了一种新型深度学习模型，整合融资指标、创始人特征、行业类别等多维因素以预测创业成功。本研究的一大特色在于设计了可模拟风险投资流程的全面回溯测试算法。该模拟能够基于历史数据对模型性能进行稳健评估，为真实投资场景中的实际效用提供可操作的洞见。基于Crunchbase数据评估，我们的模型实现了14倍资本增长，并成功识别出Revolut、DigitalOcean、Klarna、GitHub等B轮高潜力初创企业。实证结果揭示了整合多元化特征集对提升模型预测精度的重要性。总的来说，本研究展现了深度学习模型与非结构化替代数据在预测创业成功方面的巨大潜力，并为该研究领域的未来发展奠定了基础。