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<p>Portfolio optimization is an integral domain of study in quantitative finance and gained significant attention from researchers and industry professionals. Deep Reinforcement Learning (DRL) has emerged as a powerful tool for dynamically managing asset allocations aligning with investors’ objectives. However, the critical aspect of asset pre-selection is overlooked. This paper presents a new hybrid approach that combines multi-level return prediction, volatility forecasting, and DRL-based portfolio optimization. We use four predictive models to predict returns: Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). A GARCH model is used to estimate asset volatility. To enhance selection robustness, a novel scoring function is introduced that incorporates predicted returns, momentum, earning growth, and volatility. The weights of these components are adaptively learned using a regime-aware Bayesian optimization strategy. The top-scoring assets are pre-selected at each period and passed to the DRL agent, enabling a dynamic, context-aware portfolio construction. In addition, the portfolio weights are rebalanced at the end of each period using the proposed dynamic rebalancing strategy. The efficacy of our proposed approach is validated using stocks from the NASDAQ-100 and NIFTY-100 indices. The empirical results demonstrated the effectiveness of the proposed methodology based on several performance metrics when compared with benchmarks.</p>

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Dynamic Asset Pre-selection Guided Portfolio Optimization Using Deep Reinforcement Learning

  • 期刊论文

  • 发表日期：

    2026年8月1日

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  • Himanshu Choudhary,
  • Arishi Orra,
  • Manoj Thakur

摘要

Portfolio optimization is an integral domain of study in quantitative finance and gained significant attention from researchers and industry professionals. Deep Reinforcement Learning (DRL) has emerged as a powerful tool for dynamically managing asset allocations aligning with investors’ objectives. However, the critical aspect of asset pre-selection is overlooked. This paper presents a new hybrid approach that combines multi-level return prediction, volatility forecasting, and DRL-based portfolio optimization. We use four predictive models to predict returns: Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM). A GARCH model is used to estimate asset volatility. To enhance selection robustness, a novel scoring function is introduced that incorporates predicted returns, momentum, earning growth, and volatility. The weights of these components are adaptively learned using a regime-aware Bayesian optimization strategy. The top-scoring assets are pre-selected at each period and passed to the DRL agent, enabling a dynamic, context-aware portfolio construction. In addition, the portfolio weights are rebalanced at the end of each period using the proposed dynamic rebalancing strategy. The efficacy of our proposed approach is validated using stocks from the NASDAQ-100 and NIFTY-100 indices. The empirical results demonstrated the effectiveness of the proposed methodology based on several performance metrics when compared with benchmarks.