Hacking the venture industry: An Early-stage Startups Investment framework for data-driven investors
| Autor: | Francesco Corea, Enrico Maria Cervellati, Giorgio Stefano Bertinetti |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Settore SECS-P/11 - Economia degli Intermediari Finanziari
Artificial intelligence QA75.5-76.95 Venture capital Investment (macroeconomics) Checklist Business angels Data-driven Probability of success Settore SECS-P/09 - Finanza Aziendale Gradient Tree Boosting Work (electrical) Electronic computers. Computer science Machine learning Q300-390 Business Heuristics Cybernetics Industrial organization Hacker |
| Zdroj: | Machine Learning with Applications, Vol 5, Iss, Pp 100062-(2021) |
| ISSN: | 2666-8270 |
| DOI: | 10.1016/j.mlwa.2021.100062 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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