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The objective is to address the issues in hyperparameter optimization and deliver a high-performance predictive model for stock market trends tested on the Dow Jones Industrial Average (DJIA) dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The proposed ARO-GRU hybrid model uses a GRU for time-series stock price prediction and an ARO to dynamically optimize the model\u2019s parameters. ARO-GRU was benchmarked against various models, including single-layer and multi-layer GRU, BiLSTM and long short-term memory (LSTM) models optimized by genetic algorithms (GA) or ARO. Performance was assessed using metrics such as the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and <jats:italic>R<\/jats:italic>-squared (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>).<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The experimental results showed that the ARO-GRU model significantly outperformed its counterparts. Compared to the best alternative model (LSTM-ARO), ARO-GRU reduced the MSE by 81.8% (from 22.731 to 1.864 for the AAPL stock) and the MAPE by 64% (from 0.025 to 0.009). It achieved an average <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> score improvement of 5.3% across all tested stocks, demonstrating a better model fit. In addition, the ARO-GRU model required 83% less computational time than the LSTM-ARO model, further validating its efficiency.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This study introduces the integration of the ARO algorithm with the GRU for stock market prediction, marking a novel combination of efficiency and optimization. By demonstrating significant improvements in prediction accuracy and computation time, this study provides a robust and scalable solution for dynamic stock-trading systems.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-11-2024-0589","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T22:37:35Z","timestamp":1744324655000},"page":"418-443","source":"Crossref","is-referenced-by-count":2,"title":["A hybrid model integrating GRU with\u00a0artificial rabbit optimization for\u00a0stock price prediction"],"prefix":"10.1108","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0934-7118","authenticated-orcid":false,"given":"Xiaohua","family":"Zeng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"issue":"3","key":"key2025050804575656700_ref001","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.11591\/ijeecs.v13.i3.pp1087-1094","article-title":"Modelling volatility of Kuala Lumpur composite index (KLCI) using SV and GARCH models","volume":"13","year":"2019","journal-title":"Indonesian Journal of Electrical Engineering and Computer Science"},{"issue":"1","key":"key2025050804575656700_ref002","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/jpas\/9464938","article-title":"Time-series forecasting using SVMD-LSTM: a hybrid approach for stock market prediction","volume":"2025","year":"2025","journal-title":"Journal of Probability and Statistics"},{"issue":"3","key":"key2025050804575656700_ref003","doi-asserted-by":"publisher","first-page":"475","DOI":"10.21098\/jimf.v5i3.1151","article-title":"Bigdata algorithms and prediction: bingos and risky zones in sharia stock market index","volume":"5","year":"2019","journal-title":"Journal of Islamic Monetary Economics and Finance"},{"issue":"1","key":"key2025050804575656700_ref068","first-page":"1","article-title":"Advances in artificial rabbits optimization: a comprehensive review","volume":"1","year":"2024","journal-title":"Archives of Computational Methods in Engineering"},{"issue":"11","key":"key2025050804575656700_ref004","doi-asserted-by":"publisher","first-page":"16633","DOI":"10.1002\/er.6910","article-title":"Multivariate gated recurrent unit for battery remaining useful life forecast: a deep learning approach","volume":"45","year":"2021","journal-title":"International Journal of Energy Research"},{"issue":"3","key":"key2025050804575656700_ref005","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1002\/jsc.2404","article-title":"Artificial intelligence and fintech: an overview of opportunities and risks for banking, investments, and microfinance","volume":"30","year":"2021","journal-title":"Strategic Change"},{"issue":"5","key":"key2025050804575656700_ref006","first-page":"2104","article-title":"Residual GRU networks optimized by moth-flame optimization for stock price forecasting","volume":"9","year":"2022","journal-title":"IEEE Transactions on Computational Social Systems"},{"issue":"1","key":"key2025050804575656700_ref007","first-page":"44","article-title":"Multi-factor stock forecasting based on the GA-transformer model","volume":"20","year":"2021","journal-title":"Journal of Guangzhou University"},{"key":"key2025050804575656700_ref008","first-page":"80","volume-title":"Deep Learning with Python","year":"2021"},{"issue":"10","key":"key2025050804575656700_ref009","doi-asserted-by":"publisher","first-page":"3765","DOI":"10.3390\/su10103765","article-title":"Genetic algorithm-optimized long short-term memory network for stock market prediction","volume":"10","year":"2018","journal-title":"Sustainability"},{"key":"key2025050804575656700_ref010","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/iscmi63661.2024.10851608","article-title":"Advanced stock price prediction with xLSTM-based models: improving long-term forecasting","year":"2024"},{"key":"key2025050804575656700_ref011","article-title":"Research on high-frequency stock price prediction based on CEEMDAN-SSA-LSTM","volume":"2022","year":"2023","journal-title":"Jiangxi University of Finance and Economics"},{"key":"key2025050804575656700_ref012","doi-asserted-by":"crossref","unstructured":"Garita, M. 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