Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models

TL;DR

This paper introduces a deep learning-based framework for estimating variance-covariance matrices in portfolio optimization, demonstrating improved performance with LSTM-RNN models over classical methods across stocks and cryptocurrencies.

Contribution

It proposes a novel deep learning approach using LSTM-RNN, DeepVAR, and GPVAR for variance-covariance estimation in portfolio optimization, highlighting the benefits of longer observation windows.

Findings

LSTM-RNN models generally yield the best portfolio performance.

Longer observation windows improve deep learning model accuracy.

Less frequent rebalancing enhances portfolio returns.

Abstract

This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use these forecasts to optimize portfolios of stocks and cryptocurrencies. Our analysis presents results across different combinations of observation windows and rebalancing periods to compare performances of classical and deep learning variance-covariance estimation methods. The conclusions of the study are that although the strategies (portfolios) performance differed significantly between different combinations of parameters, generally the best results in terms of the information ratio and annualized returns are obtained using the LSTM-RNN models. Moreover, longer observation windows translate into better performance of the deep learning models indicating that these methods require longer windows to be able to efficiently capture the long-term dependencies of the variance-covariance matrix structure. Strategies with less frequent rebalancing typically perform better than these with the shortest rebalancing windows across all considered methods.