{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:15:15Z","timestamp":1762784115840,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Faculty of Administration at the Universidad Nacional de Colombia, Manizales"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Investment in equity assets is characterized by high volatility, both in prices and returns, which poses a constant challenge for the efficient management of risk and profitability. In this context, investors continuously seek innovative strategies that enable them to maximize their returns within acceptable risk levels, in accordance with their investment profile. The purpose of this research is to develop a model with a high predictive capacity for equity asset returns through the application of artificial intelligence techniques that integrate genetic algorithms and neural networks. The methodology is framed within a technical analysis-based investment approach, using the Relative Strength Index as the main indicator. The results show that more than 58% of the predictions generated with the proposed methodology outperformed the results obtained through the traditional technical analysis approach. These findings suggest that the incorporation of genetic algorithms and neural networks constitutes an effective alternative for optimizing investment strategies in equity assets, by providing superior returns and more accurate predictions in most of the analyzed cases.<\/jats:p>","DOI":"10.3390\/computers14110487","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T13:51:08Z","timestamp":1762782668000},"page":"487","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in Stock Market Investment Through the RSI Indicator"],"prefix":"10.3390","volume":"14","author":[{"given":"Alberto","family":"Agudelo-Aguirre","sequence":"first","affiliation":[{"name":"Administration Department, Universidad Nacional de Colombia, Carrera 27 # 64-60, Manizales 500001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4608-281X","authenticated-orcid":false,"given":"N\u00e9stor","family":"Duque-M\u00e9ndez","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Computing, Universidad Nacional de Colombia, Carrera 27 # 64-60, Manizales 500001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro","family":"Galvis-Fl\u00f3rez","sequence":"additional","affiliation":[{"name":"Management of Information Systems, Universidad Nacional de Colombia, Carrera 27 # 64-60, Manizales 500001, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/jrfm7010001","article-title":"Revisiting the Performance of MACD and RSI Oscillators","volume":"7","author":"Chong","year":"2014","journal-title":"J. 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