Venture capital investment: from rule of thumb to data science
Creators: name: Zhong, Hao
Publisher: name: No Publisher Supplied
Year: 2019

description: Recent years have witnessed the booming of venture capital market. Traditionally, venture investors (e.g., business angels, venture capitalists, private equity investors) make investment decisions based on past investment experiences, social relationship and/or qualitative assessment on startups. By offering capital and advice, venture investors could receive high financial returns once portfolio companies successfully exit, via acquisition or IPO (Initial Public Offering). Meanwhile, startups backed by venture capitals have higher opportunities to exit successfully, which are entrepreneurs’ striving goals at all times. It is thus critical for venture capitalists to find startups with high financial return potentials, and likewise for startups to get financing and business advice from right investors/mentors, especially at an early stage. Extensive research has been conducted on venture capital investment analyses, mostly from finance and/or managerial perspectives in a quantitative manner. Studies based on post hoc methodologies (e.g., interviews and surveys) to understand venture capitalists’ natural decision-making process are doubtful. People’s retrospection is subject to rationalization and post hoc recall biases. Thus, from both academic and practical perspectives, the entrepreneurial finance industry has an active call for quantitative and methodologically sound studies on venture capital investments.Which startups should venture capitalists invest in, when is the proper time to fund, and what is the right amount? What are intrinsic hidden drivers for reaching investment deals? In this dissertation, we address these research problems by utilizing cutting-edge data-driven analytical methodologies. This dissertation starts with a background introduction and the scope of research problems, followed by an extensive literature review on state-of-the-art research. We then develop an analytical approach to assist venture capitalists to make better decisions on potential investment deals. We adopt recommender system techniques to learn VCs’ investment preferences and identify the right startup candidates at the screening stage. Our method mainly uses historical investment deals and additional firmographics, including startups’ geographic locations, their industry categories, historical acquisition records, leading products. We provide investment strategy, based on Modern Portfolio Theory, to maximize financial returns while suppressing investment risks. Also, we found venture capital investments and social relationships have a strong association. We propose and develop a probabilistic latent factor model to foresee venture capital investment deals using the information of social connections between members of VC firms and startups. We uniquely approach the problem -- not through vague organizational social connection but directly using social information between members from both parties (VC firms and startups). To the best of our knowledge, we are the first to employ social information between venture capital firms and entrepreneurial companies for venture capital deals prediction and recommendation.