主题：From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses
主讲人：Baozhong Yang，佐治亚州立大学罗宾逊商学院副教授
时间：9月22日（周三）上午10:00-11:30
地点：4-101教室
语言：英文
摘要：
An AI analyst we build to digest corporate financial information, qualitative disclosure, and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analysts. In the contest of “man vs machine,” the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI’s computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of “machine plus human” (instead of human displacement) in high-skill professions.
主讲人介绍：
Baozhong Yang is the H. Talmage Dobbs Jr Chair in Finance and Associate Professor of Finance at the J. Mack Robinson College of Business in Georgia State University. He is also the Director of the FinTech Lab at the Robinson College, one of the first such labs associated with a business school in the nation. He has co-organized the inaugural and second GSU-RFS FinTech Conferences, a leading FinTech conference that offers dual submission to the premier journal Review of Financial Studies. Professor Yang has also served as referees for the leading finance and economics journals and served on the Program Committees of prestigious conferences such as the Western Finance Association and the SFS Cavalcade meetings.
Professor Yang’s research interests span theoretical and empirical studies in FinTech, Investments, and Corporate Finance. His most recent research involves innovative applications of Machine Learning and AI to study economic questions in Capital Markets and Corporate Finance. Professor Yang’s research has been published in leading academic journals in finance, accounting, operations research, computer sciences, and mathematics, including the Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, Management Science, Mathematics of Operations Research, IEEE Computer, and Advances in Mathematics. His research has been widely cited and recognized by prizes such as the Emerald Citations of Excellence, and Chicago Quantitative Alliance Annual Academic Competition Prize, and the Yihong Xia Best Paper Prize.
Professor Yang’s papers have been extensively presented at prestigious conferences, such as the National Bureau of Economic Research (NBER) Big Data, NBER Economics of AI, NBER Blockchain, NBER Law and Economics, American Finance Association, and Western Finance Association Meetings. He has been invited to present at leading universities, including Stanford University, UCLA, University of Maryland, University of Minnesota, and University of Toronto. Professor Yang’s research has been also widely covered by the media, including the NBER Digest, Bloomberg, Wall Street Journal, Financial Times, Forbes, The Guardian, CNBC, Chicago Booth Review, Columbia Law School Blog, Duke University FinTech Blog, and University of Oxford Business Law Blog.
Professor Yang received his Ph.D. in Finance from Stanford University and Ph.D. in Mathematics from the Massachusetts Institute of Technology. He has also been a Gold Medalist in the 33rd International Mathematical Olympiad while in high school.
