2019/04/09 by Yoshiharu Sato, Sato, Yoshiharu
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Engineering · Mathematics · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Economic theories and models #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Portfolio Management (q-fin.PM) #Reinforcement Learning in Robotics #Stock Market Forecasting Methods #cs.AI #cs.LG #q-fin.PM #stat.ML
paper · pdf · doi:10.48550/arxiv.1904.04973
openalex publication_date 2019/04/10 · arxiv created 2019/05/03 · arxiv updated 2019/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Financial portfolio management is one of the problems that are most frequently encountered in the investment industry. Nevertheless, it is not widely recognized that both Kelly Criterion and Risk Parity collapse into Mean Variance under some conditions, which implies that a universal solution to the portfolio optimization problem could potentially exist. In fact, the process of sequential computation of optimal component weights that maximize the portfolio's expected return subject to a certain risk budget can be reformulated as a discrete-time Markov Decision Process (MDP) and hence as a stochastic optimal control, where the system being controlled is a portfolio consisting of multiple investment components, and the control is its component weights. Consequently, the problem could be solved using model-free Reinforcement Learning (RL) without knowing specific component dynamics. By examining existing methods of both value-based and policy-based model-free RL for the portfolio optimization problem, we identify some of the key unresolved questions and difficulties facing today's portfolio managers of applying model-free RL to their investment portfolios.