{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,7]],"date-time":"2025-12-07T21:43:27Z","timestamp":1765143807684,"version":"3.41.2"},"reference-count":52,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJICC"],"published-print":{"date-parts":[[2023,10,24]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Borsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June 2021.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The most suitable hybrid model was chosen as PSO &amp; MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO &amp; MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model structure.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Using metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex models.ing, test and overall dataset to check every model's efficiency.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-02-2023-0030","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T09:43:49Z","timestamp":1689673429000},"page":"847-866","source":"Crossref","is-referenced-by-count":10,"title":["An application on forecasting for stock market prices: hybrid of some metaheuristic algorithms with multivariate adaptive regression splines"],"prefix":"10.1108","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6088-9644","authenticated-orcid":false,"given":"Dilek","family":"Sabanc\u0131","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serhat","family":"K\u0131l\u0131\u00e7arslan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kemal","family":"Adem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"issue":"3","key":"key2023102015014885000_ref001","first-page":"83","article-title":"Prediction of gold prices using artificial neural networks","volume":"9","year":"2017","journal-title":"International Journal of Engineering Research and Development"},{"issue":"13","key":"key2023102015014885000_ref002","first-page":"86","article-title":"Prediction of the relationship between the BIST 100 index and advanced stock market indices using artificial neural network:(2011-2015)","year":"2016","journal-title":"Journal of New Theory"},{"key":"key2023102015014885000_ref003","doi-asserted-by":"publisher","first-page":"13","DOI":"10.14569\/SpecialIssue.2011.010303","article-title":"Forecasting the Tehran stock market by artificial neural network","year":"2011","journal-title":"International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence"},{"issue":"3","key":"key2023102015014885000_ref004","first-page":"32","article-title":"Stock price prediction using k-nearest neighbor (KNN) algorithm","volume":"3","year":"2013","journal-title":"International Journal of Business, Humanities and Technology"},{"issue":"2","key":"key2023102015014885000_ref005","first-page":"18","article-title":"Stock market forecasting: artificial neural network and linear regression comparison in an emerging market","volume":"18","year":"2005","journal-title":"Journal of Financial Management and Analysis"},{"issue":"3","key":"key2023102015014885000_ref006","doi-asserted-by":"crossref","first-page":"5932","DOI":"10.1016\/j.eswa.2008.07.006","article-title":"Surveying stock market forecasting techniques\u2013Part II: soft computing methods","volume":"36","year":"2009","journal-title":"Expert Systems with Applications"},{"issue":"60","key":"key2023102015014885000_ref007","first-page":"759","article-title":"Forecasting Turkish stock market price with macroeconomic variables from the multivariate adaptive regression splines (MARS) model","volume":"15","year":"2020","journal-title":"Journal of Ya\u015far University"},{"key":"key2023102015014885000_ref008","unstructured":"Black, P.E. 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