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

Abstract

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.

Keywords

• Venture capital
• Machine learning
• Business angels
• Artificial intelligence
• Gradient Tree Boosting