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Start Date

16-8-2018 12:00 AM

Description

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 apply data science to develop a prediction model, smartCVC, to facilitate CVC investments by identifying a list of highly promising investees from a large pool of portfolio companies for a CVC investor. Several key features are constructed including business proximity, similar investment choices, and strategic alignment. To evaluate the performance of our prediction model, we conduct experiments on the CrunchBase dataset. Results show that the model that incorporates the three key features significantly outperforms the benchmark method.

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Aug 16th, 12:00 AM

smartCVC: Data Science Meets Corporate Venture Capital

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 apply data science to develop a prediction model, smartCVC, to facilitate CVC investments by identifying a list of highly promising investees from a large pool of portfolio companies for a CVC investor. Several key features are constructed including business proximity, similar investment choices, and strategic alignment. To evaluate the performance of our prediction model, we conduct experiments on the CrunchBase dataset. Results show that the model that incorporates the three key features significantly outperforms the benchmark method.