Lin William Cong
Cornell SC Johnson College of Business
Lin William Cong is the Rudd Family Professor of Management and Associate Professor of Finance at the Johnson Graduate School of Management at Cornell University, where he serves as the founding faculty director for the FinTech Initiative. He is also a Kauffman Foundation Junior Faculty Fellow, and Poets & Quants World Best Business School Professor, and serves on the editorial boards for journals such as the Management Science. He also co-founded two global forums on Crypto and Blockchain Economics Research (CBER-Forum.org) and on AI and Big Data in Finance Research (ABFR-Forum.org). Professor Cong received his Ph.D. in Finance and MS in Statistics from Stanford University, where he was the president of the Ph.D. Students Association and won the Liberman Fellowship and Asian American Award for leadership. He graduated as the top student in Physics from Harvard University, where he completed an A.M. in Physics jointly with A.B. in Math and Physics, an Economics Minor, and a language citation in French. Professor Cong’s research spans financial economics, information economics, FinTech, Economic Data Science, and Entrepreneurship (theory and intersection with digitization and development). He has received numerous accolades such as the Asseth-Kaiko Prize for Research in Cryptoeconomics, International Centre for Pension Management Research Award, AAM-CAMRI-CFA Institute Prize in Asset Management, CME Best paper Award, Finance Theory Group Best Paper Award, the Shmuel Kandel Award in Financial Economics, and Yihong Xia Best Paper Award. He has been invited to speak, teach, and advise at hundreds of world-renowned universities, venture funds, investment and trading shops, and government agencies such as IMF, Blackrock, Asset Management Association of China, Ant Financial, SEC, ChainLink, and federal reserve banks. He has also been consulted for several FinTech regulatory litigation cases.
Keynote Title: AI Applications in Investments and Managerial Decision-making
Abstract: In this talk, I discuss applications of deep reinforcement learning (DRL) in portfolio management and corporate finance. The first application directly optimizes the objectives of portfolio management via DRL instead of the conventional supervised-learning-based paradigms that entail first-step estimations of return distributions or risk premia. Our multi-sequence neural network AlphaPortfolio model is tailored to distinguishing features of financial data and allows potential market interactions and training without labels. AlphaPortfolio yields stellar out-of-sample performances that are robust under various economic restrictions and market conditions. Moreover, we project AlphaPortfolio onto simpler modeling spaces to uncover key drivers of investment performance, including their rotation and nonlinearity. The "economic distillation" tools we invent can be used for interpreting AI and big data models in general. In the second application, we build a DRL framework to find the most effective combination of managerial actions for a given business objective and to use historical actions to back out managers' objectives in practice, be it long versus short horizon, or enterprise value versus equity value maximization, or ESG considerations, etc. DRL derives the optimal control/action trajectory under known reward structure; once combined with an inverse reinforcement learning module, our model is equivalent to the popular generative adversarial networks and reveals managers' various considerations when making decisions.