A Machine Learning Approach to Startup Success Prediction in the Context of Venture Capital Industry

Startups play a fundamental role as drivers of innovation and growth in today’s world economies, while their failure rate keeps strikingly high. Venture capitalists, as one of the major sources of financing of early-stage companies are continuously seeking to identify promising companies to reach high returns. However, their decision-making is characterized as a time-consuming, labour-intensive and ineffective process. While machine learning methods have the potential to aid and improve the decision-making of venture capitalist, its utilization in venture capital is still very limited. The goal of the thesis is to apply machine learning methods to predict startups’ success with the focus on the needs of the venture capital industry. For that purpose, a unique approach to the definition of startup success is introduced. Four machine learning classification methods are applied to the preprocessed dataset. Overall, all models’ results have shown the potential of using machine learning algorithms to predict the success of new ventures. The random forest proved to be the best predictor from the set, achieving almost 90% accuracy. Furthermore, the most important indicators of startups’ success are identified. The thesis extends the literature on predictive modelling in venture capital and shows that machine learning methods could support investment decisions of venture capitalists.