Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach

• URL: http://arxiv.org/abs/2505.03760v1
• Date: Sun, 20 Apr 2025 10:17:37 GMT
• Title: Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
• Authors: Arishi Orra, Aryan Bhambu, Himanshu Choudhary, Manoj Thakur, Selvaraju Natarajan,
• Abstract summary: This study proposes a volatility-guided portfolio optimization framework that dynamically constructs portfolios based on investors' risk profiles.<n>The efficacy of the proposed methodology is established using stocks from the Dow $30$ index.
• Score: 2.2835610890984164
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Portfolio optimization requires dynamic allocation of funds by balancing the risk and return tradeoff under dynamic market conditions. With the recent advancements in AI, Deep Reinforcement Learning (DRL) has gained prominence in providing adaptive and scalable strategies for portfolio optimization. However, the success of these strategies depends not only on their ability to adapt to market dynamics but also on the careful pre-selection of assets that influence overall portfolio performance. Incorporating the investor's preference in pre-selecting assets for a portfolio is essential in refining their investment strategies. This study proposes a volatility-guided DRL-based portfolio optimization framework that dynamically constructs portfolios based on investors' risk profiles. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is utilized for volatility forecasting of stocks and categorizes them based on their volatility as aggressive, moderate, and conservative. The DRL agent is then employed to learn an optimal investment policy by interacting with the historical market data. The efficacy of the proposed methodology is established using stocks from the Dow $30$ index. The proposed investor-specific DRL-based portfolios outperformed the baseline strategies by generating consistent risk-adjusted returns.

Related papers

• Building crypto portfolios with agentic AI [46.348283638884425]
  The rapid growth of crypto markets has opened new opportunities for investors, but at the same time exposed them to high volatility.<n>This paper presents a practical application of a multi-agent system designed to autonomously construct and evaluate crypto-asset allocations.
  arXiv  Detail & Related papers  (2025-07-11T18:03:51Z)
• Your Offline Policy is Not Trustworthy: Bilevel Reinforcement Learning for Sequential Portfolio Optimization [82.03139922490796]
  Reinforcement learning (RL) has shown significant promise for sequential portfolio optimization tasks, such as stock trading, where the objective is to maximize cumulative returns while minimizing risks using historical data.<n>Traditional RL approaches often produce policies that merely memorize the optimal yet impractical buying and selling behaviors within the fixed dataset.<n>Our approach frames portfolio optimization as a new type of partial-offline RL problem and makes two technical contributions.
  arXiv  Detail & Related papers  (2025-05-19T06:37:25Z)
• $α$-DPO: Adaptive Reward Margin is What Direct Preference Optimization Needs [45.46582930202524]
  $alpha$-DPO is an adaptive preference optimization algorithm for large language models. It balances the policy model and the reference model to achieve personalized reward margins. It consistently outperforms DPO and SimPO across various model settings.
  arXiv  Detail & Related papers  (2024-10-14T04:29:57Z)
• Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent [44.99833362998488]
  We develop a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy.
  arXiv  Detail & Related papers  (2024-07-19T17:40:39Z)
• Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization [49.396692286192206]
  We study the use of deep reinforcement learning for responsible portfolio optimization by incorporating ESG states and objectives. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation.
  arXiv  Detail & Related papers  (2024-03-25T12:04:03Z)
• Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization [0.0]
  As a model-free algorithm, deep reinforcement learning (DRL) agent learns and makes decisions by interacting with the environment in an unsupervised way. We propose a hybrid portfolio optimization model combining the DRL agent and the Black-Litterman (BL) model. Our DRL agent significantly outperforms various comparison portfolio choice strategies and alternative DRL frameworks by at least 42% in terms of accumulated return.
  arXiv  Detail & Related papers  (2024-02-23T16:01:37Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• E2EAI: End-to-End Deep Learning Framework for Active Investing [123.52358449455231]
  We propose an E2E that covers almost the entire process of factor investing through factor selection, factor combination, stock selection, and portfolio construction. Experiments on real stock market data demonstrate the effectiveness of our end-to-end deep leaning framework in active investing.
  arXiv  Detail & Related papers  (2023-05-25T10:27:07Z)
• Asset Allocation: From Markowitz to Deep Reinforcement Learning [2.0305676256390934]
  Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets. We conduct an extensive benchmark study to determine the efficacy and reliability of a number of optimization techniques.
  arXiv  Detail & Related papers  (2022-07-14T14:44:04Z)
• Deep Reinforcement Learning for Long-Short Portfolio Optimization [7.131902599861306]
  This paper constructs a Deep Reinforcement Learning (DRL) portfolio management framework with short-selling mechanisms conforming to actual trading rules.<n>Key innovations include development of a comprehensive short-selling mechanism in continuous trading that accounts for dynamic evolution of transactions across time periods.<n>Compared to traditional approaches, this model delivers superior risk-adjusted returns while reducing maximum drawdown.
  arXiv  Detail & Related papers  (2020-12-26T16:25:20Z)
• Learning Risk Preferences from Investment Portfolios Using Inverse Optimization [25.19470942583387]
  This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization. We demonstrate our methods on real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings.
  arXiv  Detail & Related papers  (2020-10-04T21:29:29Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.