Assessment of an Optimal Multilayer Perceptron Model in Estimation of Stock's Price Rates; A Sample from the Turkish Stock Exchange BIST

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dc.contributor.authorDuman, Mustafa Ozan
dc.contributor.authorTagluk, Mehmet Emin
dc.date.accessioned2026-04-04T13:18:59Z
dc.date.available2026-04-04T13:18:59Z
dc.date.issued2024
dc.departmentİnönü Üniversitesi
dc.description8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024 -- 21 September 2024 through 22 September 2024 -- Malatya -- 203423
dc.description.abstractStock price prediction (SPP) is critical for both investors and financial experts to make accurate choices about real estate share transactions. Nowadays, Artificial Neural Networks (ANNs) are commonly used in SPP algorithms. In this study, the performance of a Multi-Layer Perceptron (MLP), a subclass of ANN, in forecasting the closing price of the following day as well as the Direction of Change (DOC) of stock was investigated. The experiments were conducted on four randomly selected stocks from Borsa Istanbul (BIST), using Sigmoid and Rectified Linear Unit (ReLU) activation functions and optimizing with variation of the node numbers in each of the hidden layers of the ANN. For each stock, the data comprised 500 workdays recorded over April 7, 2022, to April 4, 2024 period were used for training and testing the NN model taken under analyses. The predicted values come through ANN were compared with actual data using the Mean Absolute Percentage Error (MAPE) metric. The optimal outcomes were obtained with MLP with ReLU activation function and 8-5-3-1 nodes in the subsequent layers. It was observed that the predictability of stocks changed with macroeconomic and microeconomic factors as well as different time periods, which shows that stock prices also depend on temporal parameters. The overall results showed that stocks' price prediction may not be impossible, but not an easy business. © 2024 IEEE.
dc.identifier.doi10.1109/IDAP64064.2024.10710792
dc.identifier.isbn979-833153149-2
dc.identifier.scopus2-s2.0-85207866724
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP64064.2024.10710792
dc.identifier.urihttps://hdl.handle.net/11616/108048
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250329
dc.subjectArtificial Neural Networks
dc.subjectMultilayer Perceptron
dc.subjectStock Price Prediction
dc.subjectTime Series
dc.titleAssessment of an Optimal Multilayer Perceptron Model in Estimation of Stock's Price Rates; A Sample from the Turkish Stock Exchange BIST
dc.typeConference Object

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