 How to succeed in the market? Predicting startup success using a machine learning approachJongwoo Kim, Hongil Kim and Youngjung Geum Technological Forecasting and Social Change, 2023, vol. 193, issue C Abstract: Predicting startup success is a critical task for startup entrepreneurs and investors. Previous studies focused only on the internal conditions of startups and did not extensively consider the effects of industry characteristics on startup success. To fill this research gap, this study proposes a model for predicting startup success, which considers the external environment and internal conditions. A machine learning model for predicting the success of a firm was developed, incorporating industry characteristics. Data were collected from 218,207 companies in Crunchbase from January 2011 to July 2021. After data preprocessing, six machine learning models were used to predict startup success and identify features significant for the prediction. Feature importance was also calculated to determine how each feature affects startup success prediction. The results indicate that media exposure, monetary funding, industry convergence level, and industry association level are significant for determining startup success. Keywords: Crunchbase; Data analytics; Machine learning; Startup; Startup success (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:193:y:2023:i:c:s0040162523002998 DOI: 10.1016/j.techfore.2023.122614 Access Statistics for this article Technological Forecasting and Social Change is currently edited by Fred Phillips More articles in Technological Forecasting and Social Change from Elsevier |
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