Hacking the venture industry: An Early-stage Startups Investment framework for data-driven investors

• Main Authors: Francesco Corea, Giorgio Bertinetti, Enrico Maria Cervellati
• Format: Article
• Language: English
• Published: Elsevier 2021-09-01
• Series: Machine Learning with Applications
• Subjects: Venture capital; Machine learning; Business angels; Artificial intelligence; Gradient Tree Boosting
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666827021000311
• ISSN: 2666-8270

Investing in early-stage companies is incredibly hard, especially when no data are available to support the decision process. Venture capitalists often rely on gut feeling or heuristics to reach a decision, which is biased and potentially harmful. This work proposes a new data-driven framework to help investors be more effective in selecting companies with a higher probability of success. We built upon existing interdisciplinary research and augmented it with further analysis on more than 600,000 companies over a 20-year timeframe. The resulting framework is therefore a smart checklist of 21 relevant features that may help investors to select the companies more likely to succeed.