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A novel recurrent neural network based online portfolio analysis for high frequency trading
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Vasilios Katsikis ,
Predrag Stanimirovic ,
Ivona Brajevic ,
Shuai Li
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 |
| Published: |
Elsevier BV
2023
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| Online Access: |
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| 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. |
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| 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 |