Improving Venture Investment Methods at the Stage of Selecting Innovative Companies

Venture financing has been fueling technological innovation for the past 30 years. While there is general knowledge in the industry regarding the features that the company should possess to have a good shot at being successful, funds usually apply this knowledge in the subjective and non-systematic way. The goal of this study is to use mathematical models to derive such factors of a startup that would always increase probability of it being a success for the investor. Hypothesis of the study is that there are such factors that always increase or decrease the chances of successful VC investment. The object of the thesis is the decision-making process of VC investing in a startup. The subject of the thesis is startup-related factors influencing the decision making for VC investing in European Series A deals. In the paper random forest and regression models were used to successfully find out and confirm that there were factors (e.g. age of the company) that had a consistent effect on the probability of start-up success.