Inovativní investiční management: Analýza vlivu rizikového kapitálu na růst evropských high-tech startupů s využitím machine learning metod

This thesis deals with an analysis of venture capital influence on the growth of European high-tech firms. The analysis is implemented through the use of modern economic methods and machine learning methods. The aim of the thesis is to assess whether venture capital can have a statistically significant influence on the growth of chain indexes of total revenues, total assets, cash flows and employment of European high-tech firms. The theoretical part provides a literature research and concepts concerning investment management; venture capital, machine learning and Big Data are described. The practical part describes a process of a data file gathering and its analysis, using modern economic methods and machine learning methods, specifically a fixed effect model, coarsened exact matching, high dimensional fixed effects model and a state-of-the-art model of double/biased machine learning for causal parameters assessment. In the frame of this study, no statistically significant effects of venture capital influence on selected financial indicators of high-tech start-ups in Europe were discovered, which contradicts the results of the literature research, where most of the papers report statistically significant effects of venture capital on the growth of firms. One of the main reasons is probably the fact that the firms in our data file have been present on the market approximately for 10 years in average. My thesis thus provides new findings about that venture capital does not have to affect the growth of such firms which are in the more advanced stage of a start-up cycle. Simultaneously the thesis provides a proof of the importance publishing results with insignificant, zero effects.