Dynamic portfolio rebalancing through reinforcement learning

Portfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on p...

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Main Authors: Lim, Eddy Qing Yang, Cao, Qi, Quek, Cai
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/162716
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1627162022-11-07T05:49:40Z Dynamic portfolio rebalancing through reinforcement learning Lim, Eddy Qing Yang Cao, Qi Quek, Cai School of Computer Science and Engineering Engineering::Computer science and engineering Reinforcement Learning Dynamic Portfolio Rebalancing Portfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets. Published version 2022-11-07T05:49:40Z 2022-11-07T05:49:40Z 2022 Journal Article Lim, E. Q. Y., Cao, Q. & Quek, C. (2022). Dynamic portfolio rebalancing through reinforcement learning. Neural Computing and Applications, 34(9), 7125-7139. https://dx.doi.org/10.1007/s00521-021-06853-3 0941-0643 https://hdl.handle.net/10356/162716 10.1007/s00521-021-06853-3 2-s2.0-85121744954 9 34 7125 7139 en Neural Computing and Applications © The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Reinforcement Learning
Dynamic Portfolio Rebalancing
spellingShingle Engineering::Computer science and engineering
Reinforcement Learning
Dynamic Portfolio Rebalancing
Lim, Eddy Qing Yang
Cao, Qi
Quek, Cai
Dynamic portfolio rebalancing through reinforcement learning
description Portfolio managements in financial markets involve risk management strategies and opportunistic responses to individual trading behaviours. Optimal portfolios constructed aim to have a minimal risk with highest accompanying investment returns, regardless of market conditions. This paper focuses on providing an alternative view in maximising portfolio returns using Reinforcement Learning (RL) by considering dynamic risks appropriate to market conditions through dynamic portfolio rebalancing. The proposed algorithm is able to improve portfolio management by introducing the dynamic rebalancing of portfolios with vigorous risk through an RL agent. This is done while accounting for market conditions, asset diversifications, risk and returns in the global financial market. Studies have been performed in this paper to explore four types of methods with variations in fully portfolio rebalancing and gradual portfolio rebalancing, which combine with and without the use of the Long Short-Term Memory (LSTM) model to predict stock prices for adjusting the technical indicator centring. Performances of the four methods have been evaluated and compared using three constructed financial portfolios, including one portfolio with global market index assets with different risk levels, and two portfolios with uncorrelated stock assets from different sectors and risk levels. Observed from the experiment results, the proposed RL agent for gradual portfolio rebalancing with the LSTM model on price prediction outperforms the other three methods, as well as returns of individual assets in these three portfolios. The improvements of the returns using the RL agent for gradual rebalancing with prediction model are achieved at about 27.9–93.4% over those of the full rebalancing without prediction model. It has demonstrated the ability to dynamically adjust portfolio compositions according to the market trends, risks and returns of the global indices and stock assets.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Lim, Eddy Qing Yang
Cao, Qi
Quek, Cai
format Article
author Lim, Eddy Qing Yang
Cao, Qi
Quek, Cai
author_sort Lim, Eddy Qing Yang
title Dynamic portfolio rebalancing through reinforcement learning
title_short Dynamic portfolio rebalancing through reinforcement learning
title_full Dynamic portfolio rebalancing through reinforcement learning
title_fullStr Dynamic portfolio rebalancing through reinforcement learning
title_full_unstemmed Dynamic portfolio rebalancing through reinforcement learning
title_sort dynamic portfolio rebalancing through reinforcement learning
publishDate 2022
url https://hdl.handle.net/10356/162716
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