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Sejong University, 03/2026–
- Associate Professor, Department of Economics
Korea Institute for International Economic Policy (KIEP), 08/2023–02/2026
- Research Fellow, International Finance Team, 01/2025–02/2026
- Associate Research Fellow, International Finance Team, 08/2023–12/2024
City University of Hong Kong, 05/2016–06/2023
- Assistant Professor (Finance), Department of Economics and Finance
Ph.D. in Economics, University of Pennsylvania, 2012
M.A. in Economics, University of Pennsylvania, 2011
B.Sc. in Physics and B.A. in Economics, Summa cum Laude, Seoul National University, 2006
Working Papers
Revise and Resubmit at the Journal of Economic Theory
This paper considers learning about unobservable state variables when their dynamics are ambiguous. The drift of the state process is perturbed and set-estimated by inverting a test. The evolution of the set estimate is explicitly characterized up to a system of differential equations extending the conditionally Gaussian filter and is embedded in recursive maxmin expected utility. Despite the fact that the agent is unconfident only about the drift of the state process, learning under ambiguity makes her behave as if she assumed excessive volatility for the state process. This helps explain why the long-run risk model elicits seemingly excessive long-run risk from returns data.
Do People Follow AI Advice? Evidence from a Pension Portfolio Choice Experiment, with Jeongbin Kim, Matthew Kovach, Kyu-Min Lee, Euncheol Shin, and Hector Tzavellas
We study how differences in AI-generated financial recommendations are transmitted into individual portfolio choices. In an experiment with 400 employed adults enrolled in workplace defined contribution pension plans in South Korea, participants allocate a hypothetical pension balance across eleven products and may revise it after receiving one of two fixed AI-generated recommendations. A 2×2 design randomizes recommendation content and whether the recommendation includes a short rationale. Approximately 37% of the experimentally induced difference between the aggressive and conservative recommendations passes through to final portfolios. This causal contrast changes expected portfolio return, volatility, allocations across risk grades, and the number of products held, but produces no detectable difference in computed Sharpe ratios. 81% of participants revise. Among revisers, 95% move toward the assigned recommendation and implement about half of the suggested adjustment. Rationales do not detectably alter pass-through. These results show that users partially and selectively transmit recommendation content into economically meaningful differences in risk exposure while retaining substantial weight on their initial choices.
Publications
Learning about Ambiguous Long-Term Prospects, 2026, Journal of Economic Theory
On the Timing Premium Puzzle, 2025, Economics Letters
Work in Progress
Can Learning Reduce the Dark Matter in the Long-Run Risk Model?
General Research Fund, Hong Kong Research Grants Council, 01/2022–12/2023
Research Interests
Theoretical Asset Pricing, Portfolio Choice, Decision Making under Ambiguity
Teaching
Sejong University, 2026–
Principles of Economics
Mathematics for Social Sciences
City University of Hong Kong, 2016–2023
Derivatives and Risk Management
Fixed Income Securities
Stochastic Calculus for Finance
Theoretical Asset Pricing (Part 2/4)
Financial Management
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