Advanced investing with deep learning for risk-aligned portfolio optimization.

ABSTRACT: 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. P

SUBMITTER: Nguyen MD 

PROVIDER: S-EPMC12364330 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature