Machine learning models for predicting success of startups
DOI:
https://doi.org/10.5585/gep.v12i2.18942Keywords:
Startup success prediction, Machine learning, Investment in startups, Crunchbase platform.Abstract
This study analyzes results from machine learning models to predict the success of startups. As a proxy for success, we considered the investor's perspective, according to which startup buyout or IPO (Initial Public Offering) are ways to recover the investment. The literature review addresses startups and funding mechanisms, previous studies on prediction of startup success via machine learning models, and trade-offs between machine learning techniques. The empirical study comprised a quantitative research based on secondary data from the American Crunchbase platform, with startups from 171 countries. The research design used as filter startups founded between June/2010 and June/2015, as well as a prediction window from June/2015 to June/2020 to predict startup success. The final sample, after the data preprocessing stage, comprised 18,571 startups. Six binary classification models were used for success prediction: Logistic Regression, Decision Tree, Random Forest, Extreme Gradient Boosting, Support Vector Machine, and Neural Networks. In the end, the Random Forest and Extreme Gradient Boosting models had the best performance in the classification task. This article involving machine learning and startups contributes to research in hybrid fields by combining perspectives from Business and Data Science. Additionally, it contributes to investors with a tool for initial mapping of startups in search of targets with greater probability of success.
References
Arroyo, J., Corea, F., Jimenez-Diaz, G., & Recio-Garcia, J. A. (2019). Assessment of machine learning performance for decision support in venture capital investments. IEEE Access, 7, 124233–124243. https://doi.org/10.1109/ACCESS.2019.2938659
Basole, R. C., Park, H., & Chao, R. O. (2019). Visual Analysis of Venture Similarity in Entrepreneurial Ecosystems. IEEE Transactions on Engineering Management, 66(4), 568–582. https://doi.org/10.1109/TEM.2018.2855435
Bento, F. R. S. R. (2017). Predicting Start-up Success with Machine Learning. Master Program in Information Management. Instituto Superior de Estatística e Gestão da Informação. Universidade Nova de Lisboa. Recuperado de: https://run.unl.pt/bitstream/10362/33785/1/TGI0132.pdf. Acesso em 14/mai/2020.
Blank, S. (2013). The Four Steps to the Epiphany: successful strategies for products that win. Pescadero: K&S Ranch Press.
CB Insights. (2020). The Complete List of Unicorn Companies. Recuperado de: https://www.cbinsights.com/research-unicorn-companies. Acesso em 10/dez/2020.
Cremades, A. (2016). The Art of Startup Fundraising: pitching investors, negotiating the deal, and everything else entrepreneurs need to know. Hoboken: John Wiley & Sons.
Crunchbase. Crunchbase Platform. Disponível em: https://www.Crunchbase.com/. Acesso em: 12 mar. 2020.
Dalle, J.-M., Den Besten, M. & Menon, C. (2017). Using Crunchbase for economic and managerial research. OECD Science, Technology and Industry Working Papers. Recuperado de: https://pdfs.semanticscholar.org/aa83/4b1ddd1d6c96bde1c6e526be6bb2a99ad011.pdf. Acesso em 07/jun/2020.
Ertel, W. (2017). Introduction to Artificial Intelligence. 2nd ed. London: Springer.
Facelli, K., Lorena, A. C., Gama, J., & de Carvalho, A. C, P. L. F. (2019). Inteligência Artificial: uma abordagem de aprendizado de máquina. Rio de Janeiro: LTC.
Gastaud, C., Carniel, T., & Dalle, J.-M. (2019). The varying importance of extrinsic factors in the success of startup fundraising: competition at early-stage and networks at growth-stage. arXiv preprint arXiv:1906.03210. Recuperado de: https://arxiv.org/abs/1906.03210. Acesso em 03/jun/2020.
Gereto, M. A. S. (2019). Caracterização dos ciclos de investimentos de venture capital em startups brasileiras em termos de rodadas de investimento e estratégias de desinvestimento a partir de dados da Crunchbase. Dissertação de mestrado em administração. FGV - Faculdade Getúlio Vargas, 2019. Recuperado de; http://bibliotecadigital.fgv.br/dspace;/handle/10438/27468. Acesso em 01/jun/2020.
Hsieh, K.-H., & Li, E. Y. (2017). Progress of Fintech industry from venture capital point of view. In: Proceedings of The 17th International Conference on Electronic Business. ICEB, Dubai, p. 297-301. Recuperado de: http://iceb.johogo.com/proceedings/2017/ICEB_2017_paper_36-WIP.pdf. Acesso em 3/jun/2020.
Kemeny, T., Nathan, M., & Almeer, B. (2017). Using Crunchbase to explore innovative ecosystems in the US and UK. Birmingham Business School Discussion Paper Series. Recuperado de: http://epapers.bham.ac.uk/3051/1/bbs-dp-2017-01-nathan.pdf. Acesso em 01/abr/2020.
Kosterich, A., & Weber, M. S. (2018). Starting up the News: The Impact of Venture Capital on the Digital News Media Ecosystem. International Journal on Media Management, 20(4), 239–262. https://doi.org/10.1080/14241277.2018.1563547
Kubat, M. (2017). An Introduction to Machine Learning. 2nd ed. Suiça: Springler.
Liang, E., & Daphne Yuan, S.-T. (2013). Investors Are Social Animals: Predicting Investor Behavior using Social Network Features via Supervised Learning Approach. In: Proceedings of the Workshop on Mining and Learning with Graphs (MLG-2013), Chicago. Recuperado de: http://chbrown.github.io/kdd-2013-usb/workshops/MLG/doc/liang-mlg13.pdf. Acesso em 03/jun/2020.
Losada, B. (2020). Finanças para Startups: o essencial para empreender, liderar e investir em startups. São Paulo: Editora Saint Paul.
National. Small Business Failure Rate. Recuperado de: https://www.national.biz/2019-small-business-failure-rate-startup-statistics-industry/. Acesso em 06/abr/2020.
Nylund, P. A., & Cohen, B. (2017). Collision density: driving growth in urban entrepreneurial ecosystems. International Entrepreneurship and Management Journal, 13(3), 757–776. https://doi.org/10.1007/s11365-016-0424-5
Pan, C., Gao, Y., & Luo, Y. (2018). Machine Learning Prediction of Companies ‘Business Success. CS229: Machine Learning, Stanford University. Recuperado de: http://cs229.stanford.edu/proj2018/report/88.pdf. Acesso em 25/mar/2020.
Porter, M. E. (2005). Estratégia Competitiva. Rio de Janeiro: Campus.
Profitfromtech (2020). The Ultimate List of Startup Statistics for 2020. Recuperado de: https://www.profitfromtech.com/startup-statistics/. Acesso em 01/out/2020.
Ries, E. (2012). A Startup Enxuta. 1ª ed. São Paulo: Leya.
Shan, Z., Cao, H., & Lin, Q. (2014). Capital Crunch: Predicting Investments in Tech Companies. CS221 Project: Crunchbase Investment Prediction. Training, 5831(32462), 32462. Recuperado de: http://www.zifeishan.org/files/capital-crunch.pdf. Acesso em 12/jun/2020.
Skiena, S. S. (2017). The Data Science Design Manual. Suiça: Springer.
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10.1016/j.jnc.2023.126435
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