research-article

Stock market trend detection and automatic decision-making through a network-based classification model

Authors:

Liang ZhaoAuthors Info & Claims

Natural Computing, Volume 20, Issue 4

Pages 791 - 804

https://doi.org/10.1007/s11047-020-09829-9

Published: 01 December 2021 Publication History

Abstract

Many complex systems observed in nature and society can be described in terms of network. A salient feature of networks is the presence of community patterns. Network-based models have already been applied in the analysis of data from very diverse areas, from epidemics modeling to periodicity detection in meteorological data. In this paper, inspired by the formation of community structures, such as the metabolic networks and the anatomical and functional connectome observed in biological neural networks, we present a model which makes use of connector hubs to detect price trend reversals and to automatize decision-making processes in stock market trading operations for selecting a good investment strategy and improve the returns. It starts by mapping the historical stock price time series as a network, where each node represents a price variation range and the edges are generated according to the time sequential order in which these ranges occur. Afterwards, communities of the constructed network so far are detected, which represent the up and down trends of the stock prices. The model has two phases: (1) Trend detection phase, where the price trend communities are detected and trend labels are generated; and (2) Operating phase. In this phase, the proposed technique predicts trend labels to future stock prices, in such a way that these trends can be used as triggers to perform buying and selling operations of the stock. We evaluate the model by applying it on historical data from 10 of the most traded stocks from both NYSE and the Brazilian Stock Exchange (Bovespa). The obtained results are promising, with the model’s best returns being able to outperform the stock price returns for the same period in 15 out of the 20 cases under consideration.

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Cited By

Arora AAggarwal K(2022)Detecting Community Structure in Financial Markets Using the Bat Optimization AlgorithmInternational Journal of Information Technology Project Management10.4018/IJITPM.31342113:3(1-21)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.4018/IJITPM.313421
Dessain J(2022)Machine learning models predicting returnsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116970199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116970

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Stock market trend detection and automatic decision-making through a network-based classification model
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cover image Natural Computing: an international journal

Natural Computing: an international journal Volume 20, Issue 4

Dec 2021

185 pages

ISSN:1567-7818

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© The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2021

Accepted: 26 November 2020

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Cited By

Arora AAggarwal K(2022)Detecting Community Structure in Financial Markets Using the Bat Optimization AlgorithmInternational Journal of Information Technology Project Management10.4018/IJITPM.31342113:3(1-21)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.4018/IJITPM.313421
Dessain J(2022)Machine learning models predicting returnsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116970199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116970

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