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

Minh Duc Nguyen1

  • 1Department of Economic Information Systems, University of Economics, Hue University, Hue, Vietnam.

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Deep learning models enhance portfolio optimization for diverse investor risk preferences. Long Short-Term Memory (LSTM) models outperformed 1D-CNN, leading to superior investment performance and risk-adjusted returns.

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Area of Science:

  • Quantitative Finance
  • Computational Finance
  • Machine Learning Applications

Background:

  • Traditional portfolio optimization faces challenges with market volatility and diverse investor risk appetites.
  • Integrating advanced predictive models is crucial for enhancing portfolio performance.
  • Deep learning offers novel approaches to financial forecasting and asset allocation.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for portfolio optimization tailored to investor risk preferences.
  • To compare the efficacy of Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN) in financial forecasting for portfolio construction.
  • To assess the performance of different portfolio frameworks (Mean-Variance with Forecasting, Risk Parity Portfolio, Maximum Drawdown Portfolio) when integrated with deep learning predictions.

Main Methods:

  • Utilized daily returns data for VN-100 stocks (2017-2024) to train and test deep learning models.
  • Combined LSTM and 1D-CNN prediction models with MVF, RPP, and MDP portfolio frameworks.
  • Evaluated portfolio performance based on risk-adjusted returns and total returns during the 2023-2024 test period.

Main Results:

  • LSTM demonstrated superior accuracy and stability compared to 1D-CNN in forecasting stock returns.
  • Portfolios constructed using LSTM predictions generally outperformed those using 1D-CNN.
  • The LSTM+MVF combination yielded the best risk-adjusted returns, while LSTM+MDP achieved the highest total return.

Conclusions:

  • Deep learning models, particularly LSTM, significantly improve portfolio optimization outcomes.
  • Tailoring predictive models to specific portfolio frameworks enhances investment performance according to risk profiles.
  • Future research should explore incorporating diverse data sources and transaction costs for more robust portfolio strategies.