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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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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa63868
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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 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.
Keywords: Recurrent neural network, Pareto frontier, Portfolio analysis, Markowitz model, Time-varying problem
College: Faculty of Science and Engineering
Funders: National Natural Science Foundation of China [Grant Number: 72271109] , The Ministry of Education of Humanities and Social Science Project of China [Grant Number: 22YJA630116].
Start Page: 120934