Date: 27 March 2026, Friday
Time: 13.30 – 14.30
“AI for Economics and Finance”
by
Lin William Cong
Cornell University
https://zoom.us/j/8909487052?omn=96499682404
Meeting ID: 890 948 7052
Abstract
I characterize modern AI development as featuring two core themes: (i) goal-oriented end-to-end optimization in large modelling space, and (ii) generative pre-trained foundational models that enable economic world models and AI agents. Combining the insights from both, I identify promising directions in AI for Social Science Research. To start, I introduce Goal-Oriented Algorithms in Large Space (GOALS) involving transformer-based reinforcement learning or panel trees, which are particularly suited for answering questions related to optimal decision-making and modelling grouped heterogeneity in social science research. In several specific financial applications, I show how GOALS can effectively and flexibly manage investment portfolios, generate test portfolios or latent factors for evaluating extant pricing models or more accurate pricing, and separate assets of higher and lower return predictabilities under different macroeconomic regimes. Among them, I highlight how GenAI based on GOALS can assist corporate decision-making that entails complex, high-dimensional, and non-linear stochastic control during which managers possessing various business objectives learn and adapt via dynamic interactions with the market environment. With pre-trained foundational models and GOALS effectively capturing human agents’ optimizing behavior in a given economy or market, or supplying the machine equivalent of that, I further introduce the concept of data-driven generative equilibrium for counterfactual analysis. Specifically, I show how one can take a data-driven approach to examine the counterfactual equilibrium in the online lending market when borrowers endogenously adopt LLMs to complete loan applications. Extending this further, I will discuss AI-Agent-Based modeling and how that can be combined with economic world models.
I conclude the talk with remarks on several caveats or unresolved issues when applying AI in social science research: (i) When using AI agents for experimentation or to generate counterfactual data, : we need to understand AI agents as a new species that potentially differ from humans, necessitating the new field of behavioral economics of AI; (ii) Large models and computations may not be the optimal path, and we need to effectively bridge theory with data; (iii) Before we can train AI social scientists, we need to build the ImageNet equivalent for empirical studies in the social sciences.
Bio
Lin William Cong is the President’s Chair Professor in Finance, Computing and Data Science at Nanyang Technological University, with joint appointments at Nanyang Business School (Associate Dean and Professor in the Banking and Finance Division) and the College of Computing and Data Science (Professor in Data Science and AI Divisions). Prior to joining NTU, he served as the Rudd Family Endowed Chair Professor of Management and Finance, and faculty director for the FinTech initiative at Cornell University, and on the Finance faculty at the University of Chicago Booth School of Business. He is also an Editor at the Management Science, an editorial board member for multiple leading academic and practitioner journals, a Research Associate (senior research fellow) at the National Bureau of Economic Research (NBER), a Research Fellow at the European Centre of Economic Policy Research (CEPR), and a senior fellow and founding track director (“Tech, Digital Markets and AI”) at the Asian Bureau of Finance and Economic Research (ABFER), a faculty scientist at the Initiative for Cryptocurrencies & Contracts (IC3), the founding director of Center of AI for Social Science at Shenzhen Loop Area Institute (SLAI), and a lead founder of multiple international research forums (www.CBER-Forum.org and www.ABFR-Forum.org). He is also a member of multiple professional organizations such as the American Economic Association, European Finance Association, and the Econometric Society.