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

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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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Acknowledgements

This work is supported in part by the Sao Paulo State Research Foundation (FAPESP) under Grant Nos. 2015/50122-0 and 2013/07375-0, the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, the Pro-Rectory of Research (PRP) of University of Sao Paulo under grant number 2018.1.1702.59.8, the C4AI under grant number FAPESP/IBM/USP: 19/07665-4 and the Brazilian National Council for Scientific and Technological Development (CNPq) under Grant No. 303199/2019-9.

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  1. Institute of Mathematics and Computer Science, University of Sao Paulo, São Carlos, Brazil

    Tiago Colliri

  2. Faculty of Philosophy, Science, and Letters, University of Sao Paulo, Ribeirão Preto, Brazil

    Liang Zhao

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  1. Tiago Colliri
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Colliri, T., Zhao, L. Stock market trend detection and automatic decision-making through a network-based classification model. Nat Comput 20, 791–804 (2021). https://doi.org/10.1007/s11047-020-09829-9

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