A framework for screening and portfolio selection in corporate venture capital. Issue 3 (23rd April 2020) - Herberger, Tim Alexander; Reinle, Felix
Abstract : Purpose: The purpose of this paper is to outline and demonstrate a method for screening and selection of potential portfolio companies (PCs) during the screening phase in corporate venture capital. Design/methodology/approach: The use of the data envelopment analysis (DEA) enables the consideration of individual, heterogeneous and multidimensional decision criteria in portfolio selection and the preceding screening process by the investor. Findings: The result of this method is a relative ranking of the PCs, with all the PCs considered serving as peer group. A weighting of individual criteria is not necessary because it is part of the functionality of DEA. The authors validate the proposed approach in a case study and show that it can be well combined with other models and theoretical frameworks. Practical implications: The method is particularly useful in two cases. First, if a highly specialized investor wishes to use a variety of individual selection criteria for portfolio selection. Second, if an investor only has insufficient (financial) data on potential PCs, but still wants to make a (pre-) selection based on observable (qualitative) characteristics. This model helps to make consistent, intersubjectively comprehensible decisions based on valid decision criteria and helps to optimize the decision-making process in the context of portfolio selection in CVC. Originality/value: This method allows the systematic selection of an attractive group from a large number of potential PCs, based on observable characteristics and taking into account individual strategic investment objectives, without having to make assumptions about underlying distributions or weights of decision criteria.