Smart Tangency Portfolio: Deep Reinforcement Learning for Dynamic Rebalancing and Risk–Return Trade-Off

Jiayang Yu () and Kuo-Chu Chang
Additional contact information
Jiayang Yu: Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA
Kuo-Chu Chang: Department of Systems Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA

IJFS, 2025, vol. 13, issue 4, 1-35

Abstract: This paper proposes a dynamic portfolio allocation framework that integrates deep reinforcement learning (DRL) with classical portfolio optimization to enhance rebalancing strategies and risk–return management. Within a unified reinforcement-learning environment for portfolio reallocation, we train actor–critic agents (Proximal Policy Optimization (PPO) and Advantage Actor–Critic (A2C)). These agents learn to select both the risk-aversion level—positioning the portfolio along the efficient frontier defined by expected return and a chosen risk measure (variance, Semivariance, or CVaR)—and the rebalancing horizon. An ensemble procedure, which selects the most effective agent–utility combination based on the Sharpe ratio, provides additional robustness. Unlike approaches that directly estimate portfolio weights, our framework retains the optimization structure while delegating the choice of risk level and rebalancing interval to the AI agent, thereby improving stability and incorporating a market-timing component. Empirical analysis on daily data for 12 U.S. sector ETFs (2003–2023) and 28 Dow Jones Industrial Average components (2005–2023) demonstrates that DRL-guided strategies consistently outperform static tangency portfolios and market benchmarks in annualized return, volatility, and Sharpe ratio. These findings underscore the potential of DRL-driven rebalancing for adaptive portfolio management.

Keywords: portfolio optimization; deep reinforcement learning; Proximal Policy Optimization (PPO); Advantage Actor–Critic (A2C); Conditional Value-at-Risk (CVaR); dynamic rebalancing; efficient frontier; risk–return trade-off (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7072/13/4/227/pdf (application/pdf)
https://www.mdpi.com/2227-7072/13/4/227/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijfss:v:13:y:2025:i:4:p:227-:d:1808366

Access Statistics for this article

IJFS is currently edited by Ms. Hannah Lu

More articles in IJFS from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().