An Intelligent Approach for Predicting Stock Market Movements in Emerging Markets Using Optimized Technical Indicators and Neural Networks

Sagaceta-Mejía Alma Rocío, Sánchez-Gutiérrez Máximo Eduardo () and Fresán-Figueroa Julián Alberto
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Sagaceta-Mejía Alma Rocío: Departamento de Física y Matemáticas, Universidad Iberoamericana, Ciudad de México, México
Sánchez-Gutiérrez Máximo Eduardo: Colegio de Ciencia y Tecnología, Universidad Autónoma de la Ciudad de México, Ciudad de México, México
Fresán-Figueroa Julián Alberto: Departamento de Matemáticas Aplicadas y Sistemas, Universidad Autónoma Metropolitana Unidad Cuajimalpa, Ciudad de México, México

Economics - The Open-Access, Open-Assessment Journal, 2024, vol. 18, issue 1, 14

Abstract: Integrating big data analytics and machine learning algorithms has become increasingly important in the fast-changing landscape of stock market investment. The numerical findings showcase the tangible impact of our methodology on the accuracy and efficiency of stock market trend predictions. Identifying and selecting the most salient features (technical indicators) is critical in predicting the trend direction of exchange-traded funds (ETFs) in emerging markets, leveraging financial and economic indicators. Our methodology encompasses an array of statistical techniques strategically employed to identify critical technical indicators with significant implications for time series problems. We improve the efficacy of our model by performing systematic evaluations of statistical and machine learning methods across multiple sets of features or technical indicators, resulting in a more accurate trend prediction mechanism. Notably, our approach not only achieves a substantial reduction in the computational cost of the proposed neural network model by selecting only 5% of the total technical indicators for predicting ETF trends but also enhances the accuracy rate by approximately 2%.

Keywords: ETF; emerging markets; neural networks; feature selection; data mining (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:econoa:v:18:y:2024:i:1:p:14:n:1019

DOI: 10.1515/econ-2022-0073

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