Nguyen Minh Duc
Department of Economic Information Systems, University of Economics, Hue University, Hue, Vietnam.
PLoS One. 2025 Aug 19;20(8):e0330547. doi: 10.1371/journal.pone.0330547. eCollection 2025.
This study introduces a deep learning-based framework for portfolio optimization tailored to different investor risk preferences. We combine two prediction models, Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN), with three portfolio frameworks: Mean-Variance with Forecasting (MVF), Risk Parity Portfolio (RPP), and Maximum Drawdown Portfolio (MDP). Each framework represents a distinct risk profile: return-seeking, moderate-risk and conservative. The dataset is constructed from daily returns of VN-100 stocks in Vietnam, covering the period from 2017 to 2024. Forecasts from the deep learning models are integrated into each optimization approach. Results from the 2023-2024 test period showed that LSTM outperforms 1D-CNN in both accuracy and stability. Portfolios using LSTM achieved better performance. LSTM+MVF delivers the best risk-adjusted returns, while LSTM+MDP achieves the highest total return. The study highlights the value of aligning predictive models with appropriate optimization strategies for improved investment outcomes. Future work may include other asset classes, transaction cost modeling, and dynamic rebalancing. Combining deep learning with macroeconomic or alternative data could also improve forecasting and portfolio outcomes.
本研究介绍了一种基于深度学习的投资组合优化框架，该框架针对不同投资者的风险偏好进行了定制。我们将两种预测模型，即长短期记忆网络（LSTM）和一维卷积神经网络（1D-CNN），与三种投资组合框架相结合：预测均值方差（MVF）、风险平价投资组合（RPP）和最大回撤投资组合（MDP）。每个框架代表一种不同的风险状况：追求回报型、中等风险型和保守型。数据集由越南VN-100股票的日回报率构建而成，涵盖2017年至2024年期间。深度学习模型的预测结果被整合到每种优化方法中。2023 - 2024测试期的结果表明，LSTM在准确性和稳定性方面均优于1D-CNN。使用LSTM的投资组合表现更佳。LSTM + MVF提供了最佳的风险调整后回报，而LSTM + MDP实现了最高的总回报。该研究强调了将预测模型与适当的优化策略相结合以改善投资结果的价值。未来的工作可能包括其他资产类别、交易成本建模和动态再平衡。将深度学习与宏观经济数据或另类数据相结合也可能改善预测和投资组合结果。
