University of Oulu

Cao, X., Francis, A., Pu, X., Zhang, Z., Katsikis, V., Stanimirovic, P., Brajevic, I., & Li, S. (2023). A novel recurrent neural network based online portfolio analysis for high frequency trading. In Expert Systems with Applications (Vol. 233, p. 120934). Elsevier BV.

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

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Author: Cao, Xinwei1; Francis, Adam2; Pu, Xujin1;
Organizations: 1School of Business, Jiangnan University, Wuxi, China
2College of Engineering, Swansea University, Swansea, UK
3Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Athens, Greece
4Faculty of Science and Mathematics, University of Nis, Nis, Serbia
5Faculty of Applied Management, Economics and Finance, University Business Academy, Belgrade, Serbia
6Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.7 MB)
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Language: English
Published: Elsevier, 2023
Publish Date: 2023-10-13


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.

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Series: Expert systems with applications
ISSN: 0957-4174
ISSN-E: 1873-6793
ISSN-L: 0957-4174
Volume: 233
Article number: 120934
DOI: 10.1016/j.eswa.2023.120934
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: We are also thankful to the support by National Natural Science Foundation of China [Grant Number: 72271109] and The Ministry of Education of Humanities and Social Science Project of China [Grant Number: 22YJA630116].
Dataset Reference: No data was used for the research described in the article.
Copyright information: © 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (