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.
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.
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.
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.
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.
