{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T13:27:51Z","timestamp":1776778071243,"version":"3.51.2"},"reference-count":37,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:00:00Z","timestamp":1638748800000},"content-version":"vor","delay-in-days":339,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006247","name":"Anhui University of Science and Technology","doi-asserted-by":"publisher","award":["13200382"],"award-info":[{"award-number":["13200382"]}],"id":[{"id":"10.13039\/501100006247","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The foremost aim of this research was to forecast the performance of three stock market indices using the multilayer perceptron (MLP), recurrent neural network (RNN), and autoregressive integrated moving average (ARIMA) on historical data. Moreover, we compared the extrapolative abilities of a hybrid of ARIMA with MLP and RNN models, which are called ARIMA\u2010MLP and ARIMA\u2010RNN. Because of the complicated and noisy nature of financial data, we combine novel machine\u2010learning techniques such as MLP and RNN with ARIMA model to predict the three stock market data. The data used in this study are taken from the Pakistan Stock Exchange, National Stock Exchange India, and Sri Lanka Stock Exchange. In the case of Pakistan, the findings show that the ARIMA\u2010MLP and ARIMA\u2010RNN beat the individual ARIMA, MLP, and RNN models in terms of accuracy. Similarly, in the case of Sri Lanka and India, the hybrid models show more robustness in terms of forecasting than individual ARIMA, MLP, and RNN models based on root\u2010mean\u2010square error and mean absolute error. Apart from this, ARIMA\u2010MLP outperformed the ARIMA\u2010RNN in the case of Pakistan and India, while in the context of Sri Lanka, ARIMA\u2010RNN beat the ARIMA\u2010MLP in forecasting. Our findings reveal that the hybrid models can be regarded as a suitable option for financial time\u2010series forecasting.<\/jats:p>","DOI":"10.1155\/2021\/5663302","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T01:05:14Z","timestamp":1638839114000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["An Application of Hybrid Models for Weekly Stock Market Index Prediction: Empirical Evidence from SAARC Countries"],"prefix":"10.1155","volume":"2021","author":[{"given":"Zhang","family":"Peng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farman Ullah","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faridoon","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Parvez Ahmed","family":"Shaikh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9607-8993","authenticated-orcid":false,"given":"Dai","family":"Yonghong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7038-0262","authenticated-orcid":false,"given":"Ihsan","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5158-1659","authenticated-orcid":false,"given":"Farid","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00126626"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/614342"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40854-017-0074-9"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0925-2312(01)00702-0"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2007.06.001"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2008.07.020"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2014.09.087"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2015.05.005"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1103\/physrevlett.99.256601"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2009.08.004"},{"key":"e_1_2_10_11_2","first-page":"20","volume-title":"Parallel Distributed Processing","author":"McClelland J. 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