Future performance classification of high-technology venture investments with limited data

Abstract

The system we propose allows the classification of future performances of high-technology venture investments on the basis of limited, successively available information. Our system helps investors to decide whether to invest in a young High-Technology Venture (HTV) or not. In order to cope with uncertain data we apply a Fuzzy-Rule-based Classifier. As we want to attain an objective and clear decision making process we implement a learning algorithm that learns rules from given realworld examples. The availability of data on early-stage investments is typically limited. For this reason we equipped our system with a bootstrapping mechanism which multiplies the number of examples without changing their inherent quality and structure. All these features make an operational and reliable investment decision support system in the context of early stage venture capital investments possible.