Predicting Corporate Venture Capital Investment

Abstract

Corporate venture capital (CVC) has been growing rapidly in the past decades. As a critical first step for effective CVC investment, the selection of appropriate portfolio companies is challenging and difficult due to the large number of potential targets and the high uncertainty arising from an investment deal. In this study, we adopt the design science approach and develop a prediction model to support CVC investment decisions by identifying a list of potential investees from a large pool of portfolio companies for a CVC investor. We develop five key features using data science techniques including business proximity, wisdom of crowds in CVC investments, strategic alignment, status differential, and geographic proximity. To evaluate the performance of the proposed model, we plan to conduct experiments on the CrunchBase dataset.