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A novel recurrent neural network based online portfolio analysis for high frequency trading

Xinwei Cao, Adam Francis, Xujin Pu, Zenan Zhang Orcid Logo, Vasilios Katsikis Orcid Logo, Predrag Stanimirovic Orcid Logo, Ivona Brajevic Orcid Logo, Shuai Li Orcid Logo

Expert Systems with Applications, Volume: 233, Start page: 120934

Swansea University Authors: Adam Francis, Shuai Li Orcid Logo

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DOI (Published version): 10.1016/j.eswa.2023.120934

Abstract

The Markowitz model, a Nobel Prize winning model for portfolio analysis, paves the theoretical foundation in finance for modern investment. However, it remains a challenging problem in the high frequency trading (HFT) era to find a more time efficient solution for portfolio analysis, especially when...

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Published in: Expert Systems with Applications
ISSN: 0957-4174
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63868
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spelling v2 63868 2023-07-12 A novel recurrent neural network based online portfolio analysis for high frequency trading 8449248c17fec32f131097c0d1a768cc Adam Francis Adam Francis true false 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2023-07-12 FGSEN The Markowitz model, a Nobel Prize winning model for portfolio analysis, paves the theoretical foundation in finance for modern investment. However, it remains a challenging problem in the high frequency trading (HFT) era to find a more time efficient solution for portfolio analysis, especially when considering circumstances with the dynamic fluctuation of stock prices and the desire to pursue contradictory objectives for less risk but more return. In this paper, we establish a recurrent neural network model to address this challenging problem in runtime. Rigorous theoretical analysis on the convergence and the optimality of portfolio optimization are presented. Numerical experiments are conducted based on real data from Dow Jones Industrial Average (DJIA) components and the results reveal that the proposed solution is superior to DJIA index in terms of higher investment returns and lower risks. Journal Article Expert Systems with Applications 233 120934 Elsevier BV 0957-4174 Recurrent neural network, Pareto frontier, Portfolio analysis, Markowitz model, Time-varying problem 15 12 2023 2023-12-15 10.1016/j.eswa.2023.120934 http://dx.doi.org/10.1016/j.eswa.2023.120934 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University National Natural Science Foundation of China [Grant Number: 72271109] , The Ministry of Education of Humanities and Social Science Project of China [Grant Number: 22YJA630116]. 2023-08-24T10:35:59.7709861 2023-07-12T11:21:14.9593998 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Xinwei Cao 1 Adam Francis 2 Xujin Pu 3 Zenan Zhang 0000-0003-3073-0664 4 Vasilios Katsikis 0000-0002-8208-9656 5 Predrag Stanimirovic 0000-0003-0655-3741 6 Ivona Brajevic 0000-0002-2999-3187 7 Shuai Li 0000-0001-8316-5289 8 63868__28200__80d06f7599724a3bae9ff1a740982cee.pdf 63868.pdf 2023-07-27T13:03:12.2080105 Output 2831193 application/pdf Version of Record true © 2023 The Author(s). Published by Elsevier Ltd. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title A novel recurrent neural network based online portfolio analysis for high frequency trading
spellingShingle A novel recurrent neural network based online portfolio analysis for high frequency trading
Adam Francis
Shuai Li
title_short A novel recurrent neural network based online portfolio analysis for high frequency trading
title_full A novel recurrent neural network based online portfolio analysis for high frequency trading
title_fullStr A novel recurrent neural network based online portfolio analysis for high frequency trading
title_full_unstemmed A novel recurrent neural network based online portfolio analysis for high frequency trading
title_sort A novel recurrent neural network based online portfolio analysis for high frequency trading
author_id_str_mv 8449248c17fec32f131097c0d1a768cc
42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 8449248c17fec32f131097c0d1a768cc_***_Adam Francis
42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Adam Francis
Shuai Li
author2 Xinwei Cao
Adam Francis
Xujin Pu
Zenan Zhang
Vasilios Katsikis
Predrag Stanimirovic
Ivona Brajevic
Shuai Li
format Journal article
container_title Expert Systems with Applications
container_volume 233
container_start_page 120934
publishDate 2023
institution Swansea University
issn 0957-4174
doi_str_mv 10.1016/j.eswa.2023.120934
publisher Elsevier BV
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
url http://dx.doi.org/10.1016/j.eswa.2023.120934
document_store_str 1
active_str 0
description The Markowitz model, a Nobel Prize winning model for portfolio analysis, paves the theoretical foundation in finance for modern investment. However, it remains a challenging problem in the high frequency trading (HFT) era to find a more time efficient solution for portfolio analysis, especially when considering circumstances with the dynamic fluctuation of stock prices and the desire to pursue contradictory objectives for less risk but more return. In this paper, we establish a recurrent neural network model to address this challenging problem in runtime. Rigorous theoretical analysis on the convergence and the optimality of portfolio optimization are presented. Numerical experiments are conducted based on real data from Dow Jones Industrial Average (DJIA) components and the results reveal that the proposed solution is superior to DJIA index in terms of higher investment returns and lower risks.
published_date 2023-12-15T10:36:00Z
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