MLFE LAB

Work in progress

Preprints

  • Breaking the Dimensional Barrier: A Pontryagin-Guided Direct Policy Optimization for Continuous-Time Multi-Asset Portfolio, Jeonggyu Huh*, Jaegi Jeon, Hyeng-Keun Koo, Byung-Hwa Lim* [Paper] [Code] [Slides]
  • Breaking the Dimensional Barrier: Dynamic Portfolio Choice with Parameter Uncertainty via Pontryagin Projection, Jeonggyu Huh*, Hyeng-Keun Koo [Paper] [Slide]
  • From Value Bounds to Policy-Distance and Active-Face Certificates: Same-Grid Duality for Constrained Dynamic Portfolios [Link]
  • A Computational Framework for Decision-Aligned Conditional Betas in Cost-Aware Portfolio Optimization, Dongwan Shin*, Hojin Ko, Jeonggyu Huh*

Papers in Progress

  • How Far Can Analytical Portfolio Rules Take Us? Learning and Refining Consumption and Investment, Jeonggyu Huh, Jaegi Jeon, Hyeng Keun Koo, Byung Hwa Lim
  • Amortized Feedback Planning: Turning Model-Based Rollouts into Executable Policies
  • Finite Horizon and Optimal Portfolio Choice with Stochastic Income: A Reinforcement Learning Approach, with Seyoung Park, Hojin Ko, Alain Bensoussan
  • Deep Hedging through Bellman-Guided Direct Policy Optimization, with Seungho Na, Hojin Ko
  • Scalable Distributionally Robust Portfolio Choice via Pontryagin-Guided Direct Policy Optimization, with Ho-Jun Lee
  • Real-Time Pricing of Equity-Linked Securities Using Deep Operator Networks, with Yoonyoung Byun, Jae Wook Song, Juhwan Kim
  • Discounted Alpha: A Machine Learning Framework for Equity Valuation, with Dongwan Shin, Thummim Cho