Jianqing Fan is Frederick L. Moore Professor of Finance, Professor of Statistics, Former Chairman of Department of Operations Research and Financial Engineering and Director of Committee of Statistical Studies at Princeton University, where he directs both financial econometrics and statistics labs. He was the past president of the Institute of Mathematical Statistics and International Chinese Statistical Association. He is co-editing Journal of Business and Economics Statistics, and was the co-editor of The Annals of Statistics, Probability Theory and Related Fields, Journal of Econometrics and Econometrics Journal. After receiving his Ph.D. from the University of California at Berkeley, he has been appointed as assistant, associate, and full professor at the University of North Carolina at Chapel Hill (1989-2003), professor at the University of California at Los Angeles (1997-2000), and professor at the Princeton University (2003--). His published work on statistics, economics, finance, machine learning and computational biology has been recognized by The 2000 COPSS Presidents' Award, The 2007 Morningside Gold Medal of Applied Mathematics, Guggenheim Fellow in 2009, P.L. Hsu Prize in 2013, Royal Statistical Society Guy medal in silver in 2014, Senior Noether Scholar Award in 2018, and election to Academician of Academia Sinica and follow of American Associations for Advancement of Science, Institute of Mathematical Statistics, American Statistical Association, and Society of Financial Econometrics. His research interest includes high-dimensional statistics, machine learning, financial econometrics, and computational biology.
This talk first gives an overview on the genesis of machine learning and AI and how statistical and computational methods have evolved with growing dimensionality and sample sizes and become the foundation of modern machine learning and AI. It will also outline how ideas of trading modeling biases and variances have been developed into high-dimensional statistics and machine learning, with focus on deep learning models. We will outline the opportunities and challenges of statistical machine learning in financial applications. We will showcase the applications to predicting bond risk premia using macroeconomic time series, portfolio choices, and high-frequency finance.