Abstract

Traditionally, venture investors (e.g., business angels, venture capitalists, private equity investors) make investment decisions based on their past investment experiences, social connections and/or qualitative assessment on startups. Current studies on venture capital investment, from finance and/or managerial perspectives, are mostly based on post hoc methodologies (e.g., interviews and surveys). However, people’s retrospection is subject to rationalization and recall biases. Therefore, the entrepreneurial finance industry has an active call for quantitative and methodologically sound studies on venture capital investments. To this end, our research aimed to apply cutting-edge data analytics technology to help venture capitalists make better and smarter investment decisions. At first, we attempted to address the problem from a personalized portfolio perspective. We developed a Probabilistic Latent Factor model to estimate investors’ investment preferences collaboratively. By taking account of potential investment returns and risks, we utilized Modern Portfolio Theory to optimize the startup portfolio generated from the investment preference model. As a result, this strategy can maximize investment returns along with the potential risks suppressed, and in the meantime, meet the investment preferences of the investors. Following this work, we also attempted to study the impact of prominent social ties between members of VC firms and start-up companies to the investment decision-making process, which was critical but attracted little attention. Specifically, we developed a Social-Adjusted Probabilistic Matrix Factorization (PMF) model to exploit members’ social connections information from VC firms and startups for investment recommendations. Our model brought in more flexibility, and the results inherently provided meaningful managerial implications for the controllers of VC firms and startups.

Time

11月21日（周四）10 : 00 - 11 : 30

Speaker

Dr. Hao Zhong received his Ph.D. in Management (concentration on data mining) from Rutgers Business School at Rutgers, the State University of New Jersey in 2019. He was also a founding member and Big Data Scientist at EZFintel (Shenzhen) Ltd., a start-up FinTech company that leverages Big Data and Artificial Intelligence to provide Risk Management software, platforms and customized solutions for the Financial Services industry since 2017. He has internship experiences from American Express (via DIA Associates), Philips Research-NA and Bell Labs, Alcatel-Lucent during the period of his doctoral study. Prior to joining Rutgers, he received his M.Sc. in Computer Science from York University in 2013 and B.Sc. in Electronic Information Science and Technology from University of Science and Technology of China (USTC) in 2011. His general areas of research are data mining, business analytics, and recommender systems, with a focus on developing effective and efficient data mining techniques for emerging and challenging business applications. He has published prolifically in top venues of data mining and operations research, including refereed journals (ANOR, IEEE TMC, IEEE TKDE) and conference proceedings (ACM SIGKDD, IEEE ICDM, SIAM SDM, etc).

Venue

信息管理与工程学院308室

上海财经大学（第三教学楼西侧）

上海市杨浦区武东路100号