The 19th Frontier Forum for Digital Technology and Finance

Abstract：

With the widespread penetration of digital economy and digital finance in our production and life, a large amount of unstructured, high-dimensional and other characteristics of big data (Big Data) has been generated. Big data provides new data sources for scientific research in various disciplines, and machine learning methods that are good at processing big data are increasingly valued by various social sciences such as economics and management. The purpose of this lecture is to explain the basic concepts and principles of machine learning, and through three cases of empirical papers in economics, discuss how to apply machine learning methods to empirical research in social sciences represented by economics.

Speaker Introduction:

Guo Feng is a professor and head of the Investment Department in the School of Public Economics and Management, Shanghai University of Finance and Economics. He is also the executive director of the Digital-Physical Integration and Intelligent Governance Laboratory, vice president of the Fogrow ESG Research Institute, and a senior research fellow at the Digital Finance Research Center of Peking University. His research areas include digital economy and digital finance, machine learning and big data analysis, public economics, etc. He has published over 50 papers in both Chinese and English academic journals such as "Economic Research" (2), "Management World" (3), "China Economic Review", "Journal of Economic Behavior & Organization", and "Research Policy". Eleven of his papers have a citation rate of over 100, and two of them have a citation rate of over 1000, which are two of the top 5 most-cited papers since the inception of "China Economic Review". He has also published over 90 economic commentaries in mainstream media and authored three academic monographs. He has led more than 10 research projects including the National Social Science Fund for Youth, Shanghai Philosophy and Social Science Planning Projects, and National Science Foundation for Postdoctoral Science.