Title: Associative classification model for forecasting stock market trends

Authors: Everton Castelão Tetila; Bruno Brandoli Machado; Jose F. Rorigues Jr.; Diego A. Zanoni; Nícolas A. De Souza Belete; Thayliny Zardo; Michel Constantino; Hemerson Pistori

Addresses: Faculty of Exact Sciences and Technology, Federal University of Grande Dourados, Dourados, MS, Brazil ' Department of Computer Science, Federal University of Mato Grosso do Sul, Ponta Porã, MS, Brazil ' Department of Computer Science, University of São Paulo, São Carlos, SP, Brazil ' Department of Local Development, Dom Bosco Catholic University, Campo Grande, MS, Brazil ' Department of Local Development, Dom Bosco Catholic University, Campo Grande, MS, Brazil ' Department of Local Development, Dom Bosco Catholic University, Campo Grande, MS, Brazil ' Department of Local Development, Dom Bosco Catholic University, Campo Grande, MS, Brazil ' Department of Local Development, Dom Bosco Catholic University, Campo Grande, MS, Brazil

Abstract: This paper proposes an associative classification model based on three technical indicators to forecast future trends of stock market. Our methodology assessed the performance of nine technical indicators, using a portfolio of ten stocks and a 12-year time series. The experimental results showed that the use of a set of technical indicators leads to higher classification rates compared to the use of sole technical indicators, reaching an accuracy of 88.77%. The proposed approach also uses a multidimensional data cube that allows automatic updating of stock market asset values, which are essential to keep the forecast updated. The results indicate that our approach can support investors and analysts to operate in the stock market.

Keywords: stock market trends; technical indicators; associative classification; data mining; business intelligence.

DOI: 10.1504/IJBIDM.2021.115968

International Journal of Business Intelligence and Data Mining, 2021 Vol.19 No.1, pp.97 - 112

Received: 29 Jan 2019
Accepted: 09 May 2019

Published online: 11 Jun 2021 *

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