{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:03:31Z","timestamp":1781737411244,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Nacional Mayor de San Marcos","award":["RR N\u00b0 004305-2024"],"award-info":[{"award-number":["RR N\u00b0 004305-2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Predicting stock prices on stock markets is challenging due to the nonlinear and nonstationary nature of financial markets. This study presents a hybrid model based on integrated machine learning (ML) techniques\u2014neural networks, support vector regression (SVR), and decision trees\u2014that uses the stacking method to estimate the next day\u2019s maximum and minimum stock prices. The model\u2019s performance was evaluated using three data sets: Brazil\u2019s S\u00e3o Paulo Stock Exchange (iBovespa)\u2014Companhia Energ\u00e9tica do Rio Grande do Norte (CSRN) and CPFL Energia (CPFE)\u2014and one from the New York Stock Exchange (NYSE), the Dow Jones Industrial Average (DJI). The datasets covered the following time periods: CSRN and CPFE from 1 January 2008 to 30 September 2013, and DJI from 3 December 2018 to 31 August 2024. For the CSRN ensemble, the hybrid model achieved a mean absolute percentage error (MAPE) of 0.197% for maximum price and 0.224% for minimum price, outperforming results from the literature. For the CPFE set, the model showed a MAPE of 0.834% for the maximum price and 0.937% for the minimum price, demonstrating comparable accuracy. The model obtained a MAPE of 0.439% for the DJI set for maximum price and 0.474% for minimum price, evidencing its applicability across different market contexts. These results suggest that the proposed hybrid approach offers a robust alternative for stock price prediction by overcoming the limitations of using a single ML technique.<\/jats:p>","DOI":"10.3390\/a18080471","type":"journal-article","created":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T09:53:02Z","timestamp":1753696382000},"page":"471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Prediction of the Maximum and Minimum Prices of Stocks in the Stock Market Using a Hybrid Model Based on Stacking"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0158-0564","authenticated-orcid":false,"given":"Sebastian","family":"Tuesta","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda de Sistemas e Inform\u00e1tica, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5807-4323","authenticated-orcid":false,"given":"Nahum","family":"Flores","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda Econ\u00f3mica Estad\u00edstica y Ciencias Sociales, Universidad Nacional de Ingenier\u00eda, Lima 15333, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Mauricio","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda de Sistemas e Inform\u00e1tica, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.neucom.2018.04.014","article-title":"Fuzzy time-series model based on rough set rule induction for forecasting stock price","volume":"302","author":"Cheng","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_2","unstructured":"Forbes (2025, June 26). 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