Risk-Hedged Venture Capital Investment Recommendation

With the increasing accessibility of transactional data in venture finance, venture capital firms (VCs) face great chal-lenges in developing quantitative tools to identify new in-vestment opportunities. Recommendation techniques have the possibility of helping VCs making data-driven invest-ment decisions by providing an automatic screening process of a large number of startups across different domains on the basis of their past investment data. A previous study has shown the potential advantage of using collaborative fil-tering to catch and predict the VCs ’ investment behaviours [17]. However, two fundamental challenges in venture fi-nance make conventional recommendation techniques diffi-cult to apply. First, risk factors should be cautiously con-sidered when making investments: for a potential startup, a VC needs to specifically estimate how well this new invest-ment can fit into its holding investment portfolio in such a way that investment risk can be hedged. Second, The invest-ment behaviours are much sparser than conventional recom-mendation applications and a VC’s investments are usually limited to a few industry categories, making it impossible to use a topic-diversification method to hedge the risk. In this paper, we solve the startup recommendation problem from a risk management perspective. We propose 5 risk-aware startup selection and ranking algorithms to catch the VCs ’ investment behaviours and predict their new invest-ments. Apart from the contribution on the new risk-aware recommendation model, our experiments on the collected CrunchBase dataset show significant performance improve-ments over strong baselines