NUS Quantitative Finance Joint Seminar Series (Webinar)
NUS Quantitative Finance Joint Seminar Series (Webinar)
This webinar is jointly organised by Risk Management Institute and Centre for Quantitative Finance
A Short Biography of Speakers
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.
Professor Xin GUO is the Coleman Fung Chair professor at the IEOR department of UC Berkeley. Prior to Berkeley, she worked at IBM Watson research center (1999-2003) and at Cornell University (2004-2007). Her current research interests include stochastic controls and games, machine learning theory, medical data and financial time series data analysis.
| 10.00AM – 11.00AM
(Singapore Time Zone, GMT +8) |
Title: How do Machines Learn Finance?
Prof Jianqing FAN (Princeton University) Abstract: This talk first gives an overview on the genesis of machine learning and AI and how statistical and computational methods have evolved with big data 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 highlight three applications: portfolio choices with text data via feature screening, predicability of the momentum and duration in high-frequency finance via several statistical machine learning methods, and sparse portfolio allocation and Sharpe ratio estimation. |
| 11.00AM – 12.00AM
(Singapore Time Zone, GMT +8) |
Title: GANs through MFGs and SDEs approximations
Prof Xin GUO (University of California, Berkeley) Abstract: Generative Adversarial Networks (GANs) have celebrated great empirical success, especially in image generation and processing and more recently in simulation of financial time series data. In this talk, we will discuss some of our recent works in mathematical understanding of GANs. The first is the theoretical connection between GANs and mean field games (MFGs) and optimal transport (OT): interpreting MFGs as GANs, on one hand, allows us to devise GANs-based algorithms to solve high dimensional MFGs. Interpreting GANs as MFGs and OT, on the other hand, provides a new and probabilistic foundation for GANs. The second is on approximating GANs training in the form of SDEs. This SDEs approximation provides basic analytical tools to investigate some well-recognized issues (e.g., convergence and explosive gradient) in the machine learning community for GANs training. |
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