Risk-Hedged Venture Capital Investment Recommendation

With the increasing accessibility of transactional data in venture finance, venture capital firms (VCs) face great challenges in developing quantitative tools to identify new investment opportunities. Recommendation techniques have the possibility of helping VCs making data-driven investment 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 filtering to catch and predict the VCs’ investment behaviours [17]. However, two fundamental challenges in venture finance make conventional recommendation techniques difficult to apply. First, risk factors should be cautiously considered when making investments: for a potential startup, a VC needs to specifically estimate how well this new investment can fit into its holding investment portfolio in such a way that investment risk can be hedged. Second, The investme...

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: Xiaoxue Zhao, Weinan Zhang, Jun Wang 0012
• Keywords: Control Systems, RECSYS 2015