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

Maciej Wysocki () and Paweł Sakowski
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Maciej Wysocki: University of Warsaw, Faculty of Economic Sciences; Quantitative Finance Research Group
Paweł Sakowski: University of Warsaw, Faculty of Economic Sciences; Quantitative Finance Research Group

No 2022-12, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. In this study we propose a novel framework for variance-covariance 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 that consisted 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.

Keywords: Portfolio Optimization; Deep Learning; Variance-Covariance Matrix Forecasting; Investment Strategies; Recurrent Neural Networks; Long Short-Term Memory Neural Networks (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G11 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-fmk
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https://www.wne.uw.edu.pl/download_file/1724/0 First version, 2022 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2022-12

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