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A novel recurrent neural network based online portfolio analysis for high frequency trading
Expert Systems with Applications, Volume: 233, Start page: 120934
Swansea University Authors: Adam Francis, Shuai Li
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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...
Published in: | Expert Systems with Applications |
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ISSN: | 0957-4174 |
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Elsevier BV
2023
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URI: | https://cronfa.swan.ac.uk/Record/cronfa63868 |
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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 |
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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 |
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Journal article |
container_title |
Expert Systems with Applications |
container_volume |
233 |
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120934 |
publishDate |
2023 |
institution |
Swansea University |
issn |
0957-4174 |
doi_str_mv |
10.1016/j.eswa.2023.120934 |
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Elsevier BV |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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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 |
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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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1775102602285940736 |
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11.017731 |