{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T14:46:56Z","timestamp":1758811616958,"version":"3.44.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T00:00:00Z","timestamp":1715212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of historical data and five distinct architectures for neural networks. A multi-layer perceptron was the first step, followed by a simple recurrent neural network, a gated recurrent unit network and two long-short-term memory (LSTM) networks. The results of the analyses performed on these models have demonstrated a powerful capacity for prediction. Additionally, the findings of this research point to the fact that the application of intelligent systems can simplify the decision-making process in financial markets, which is a substantial advantage. Furthermore, by comparing the predicted outcome errors between the models, the LSTM presents the lowest error with a higher computational time in the training phase. The LSTM was able to accurately forecast the closing price of the day as well as the price for the following one and two days in advance. In conclusion, the empirical results demonstrated that these models could accurately predict financial data for trading purposes and that the application of intelligent systems, such as the LSTM network, represents a promising advancement in financial technology.<\/jats:p>","DOI":"10.1093\/jigpal\/jzae050","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T15:13:48Z","timestamp":1711466028000},"source":"Crossref","is-referenced-by-count":0,"title":["The application of artificial neural networks to forecast financial time series"],"prefix":"10.1093","volume":"33","author":[{"given":"D","family":"Gonz\u00e1lez-Cort\u00e9s","sequence":"first","affiliation":[{"name":"NEOMA Business School , rue du Mar\u00e9chal Juin, Mont Saint Aignan Cedex 76825, France, daniel-alejandro.gonzalez-cortes.20@neoma-bs.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"E","family":"Onieva","sequence":"additional","affiliation":[{"name":"Faculty of Engineering , University of Deusto, 48007, Bilbao, Spain, enrique.onieva@deusto.es"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"I","family":"Pastor","sequence":"additional","affiliation":[{"name":"Faculty of Engineering , University of Deusto, 48007, Bilbao, Spain, iker.pastor@deusto.es"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J","family":"Wu","sequence":"additional","affiliation":[{"name":"NEOMA Business School , rue du Mar\u00e9chal Juin, Mont Saint Aignan Cedex 76825, France, jian.wu@neoma-bs.fr"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"2025092510270510200_ref1","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3390\/electronics8030292","article-title":"A state-of-the-art survey on deep learning theory and architectures","volume":"8","author":"Alom","year":"2019","journal-title":"Electronics"},{"key":"2025092510270510200_ref2","doi-asserted-by":"crossref","DOI":"10.1145\/2959100.2959180","article-title":"Ask the gru","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems - RecSys 16","author":"Bansal","year":"2016"},{"key":"2025092510270510200_ref3","doi-asserted-by":"crossref","first-page":"2680","DOI":"10.1016\/j.jbankfin.2011.05.019","article-title":"Financial contagion and the real economy","volume":"36","author":"Baur","year":"2012","journal-title":"Journal of Banking & Finance"},{"key":"2025092510270510200_ref4","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Transactions on Neural Networks"},{"key":"2025092510270510200_ref5","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113464","article-title":"Stock market movement forecast: A systematic review","volume":"156","author":"Bustos","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"2025092510270510200_ref6","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-018-24271-9","article-title":"Recurrent neural networks for multivariate time series with missing values","volume":"8","author":"Che","year":"2018","journal-title":"Scientific Reports"},{"key":"2025092510270510200_ref7","doi-asserted-by":"crossref","DOI":"10.3115\/v1\/D14-1179","article-title":"Learning phrase representations using rnn encoder\u2013decoder for statistical machine translation","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Cho","year":"2014"},{"volume-title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","year":"2014","author":"Chung","key":"2025092510270510200_ref8"},{"key":"2025092510270510200_ref9","doi-asserted-by":"crossref","first-page":"10389","DOI":"10.1016\/j.eswa.2011.02.068","article-title":"Using artificial neural network models in stock market index prediction","volume":"38","author":"Guresen","year":"2011","journal-title":"Expert Systems with Applications"},{"key":"2025092510270510200_ref10","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"2025092510270510200_ref11","doi-asserted-by":"crossref","DOI":"10.21437\/Interspeech.2016-491","article-title":"Lstm, gru, highway and a bit of attention: An empirical overview for language modeling in speech recognition","volume-title":"Interspeech 2016","author":"Irie","year":"2016"},{"key":"2025092510270510200_ref12","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115537","article-title":"Applications of deep learning in stock market prediction: recent progress","volume":"184","author":"Jiang","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"2025092510270510200_ref13","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.eswa.2018.03.002","article-title":"Forecasting the volatility of stock price index: a hybrid model integrating lstm with multiple garch-type models","volume":"103","author":"Kim","year":"2018","journal-title":"Expert Systems with Applications"},{"key":"2025092510270510200_ref14","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"volume-title":"Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence","year":"2019","author":"Lee","key":"2025092510270510200_ref15"},{"key":"2025092510270510200_ref16","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1093\/rfs\/1.1.41","article-title":"Stock market prices do not follow random walks: evidence from a simple specification test","volume":"1","author":"Lo","year":"1988","journal-title":"The Review of Financial Studies"},{"key":"2025092510270510200_ref17","doi-asserted-by":"crossref","first-page":"2573","DOI":"10.1016\/j.jbankfin.2010.04.014","article-title":"The use of technical analysis by fund managers international evidence","volume":"34","author":"Menkhoff","year":"2010","journal-title":"Journal of Banking & Finance"},{"volume-title":"Automatic Differentiation in Pytorch","year":"2017","author":"Paszke","key":"2025092510270510200_ref18"},{"key":"2025092510270510200_ref19","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2019.11.040","article-title":"3dacn: 3D augmented convolutional network for time series data","volume":"513","author":"Pei","year":"2020","journal-title":"Information Sciences"},{"key":"2025092510270510200_ref20","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0188107","article-title":"Predictability of machine learning techniques to forecast the trends of market index prices: hypothesis testing for the korean stock markets","volume":"12","author":"Pyo","year":"2017","journal-title":"PLoS One"},{"key":"2025092510270510200_ref21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.chaos.2016.01.004","article-title":"Application of artificial neural network for the prediction of stock market returns: the case of the Japanese stock market","volume":"85","author":"Qiu","year":"2016","journal-title":"Chaos, Solitons & Fractals"},{"key":"2025092510270510200_ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.11113\/matematika.v33.n1.956","article-title":"The use of artificial neural network and multiple linear regressions for stock market forecasting","volume":"33","author":"Sagir","year":"2017","journal-title":"MATEMATIKA"},{"article-title":"Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition","year":"2014","author":"Sak","key":"2025092510270510200_ref23"},{"key":"2025092510270510200_ref24","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106181","article-title":"Financial time series forecasting with deep learning: a systematic literature review: 2005\u20132019","volume":"90","author":"Sezer","year":"2020","journal-title":"Applied Soft Computing"},{"key":"2025092510270510200_ref25","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.asoc.2015.09.040","article-title":"Artificial neural networks in business: two decades of research","volume":"38","author":"Tkac","year":"2016","journal-title":"Applied Soft Computing"},{"volume-title":"Neural Machine Translation Advised by Statistical Machine Translation","year":"2016","author":"Wang","key":"2025092510270510200_ref26"},{"key":"2025092510270510200_ref27","doi-asserted-by":"crossref","DOI":"10.1016\/j.intfin.2022.101589","article-title":"Rethinking financial contagion: Information transmission mechanism during the covid-19 pandemic","volume":"79","author":"Yarovaya","year":"2022","journal-title":"Journal of International Financial Markets, Institutions and Money"}],"container-title":["Logic Journal of the IGPL"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jigpal\/article-pdf\/33\/5\/jzae050\/57465239\/jzae050.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jigpal\/article-pdf\/33\/5\/jzae050\/57465239\/jzae050.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T14:27:16Z","timestamp":1758810436000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jigpal\/article\/doi\/10.1093\/jigpal\/jzae050\/7665617"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,9]]},"references-count":27,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,8,15]]}},"URL":"https:\/\/doi.org\/10.1093\/jigpal\/jzae050","relation":{},"ISSN":["1367-0751","1368-9894"],"issn-type":[{"type":"print","value":"1367-0751"},{"type":"electronic","value":"1368-9894"}],"subject":[],"published-other":{"date-parts":[[2025,10]]},"published":{"date-parts":[[2024,5,9]]},"article-number":"jzae050"}}