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Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators

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dc.contributor.authorAli H. Dhaferen_US
dc.contributor.authorFauzias Mat Noren_US
dc.contributor.authorGamal Alkawsien_US
dc.contributor.authorAbdulaleem Z. Al-Othmanien_US
dc.contributor.authorNuradli Ridzwan Shahen_US
dc.contributor.authorHuda M. Alshanbarien_US
dc.contributor.authorKhairil Faizal Khairien_US
dc.contributor.authorYahia Baasharen_US
dc.date.accessioned2024-05-29T02:26:08Z
dc.date.available2024-05-29T02:26:08Z
dc.date.issued2022
dc.date.submitted2022-4-26
dc.descriptionComputational Intelligence and Neuroscience Volume 2022en_US
dc.description.abstractStock price prediction is one of the major challenges for investors who participate in the stock markets. Therefore, different methods have been explored by practitioners and academicians to predict stock price movement. Artificial intelligence models are one of the methods that attracted many researchers in the field of financial prediction in the stock market. This study investigates the prediction of the daily stock prices for Commerce International Merchant Bankers (CIMB) using technical indicators in a NARX neural network model. The methodology employs comprehensive parameter trails for different combinations of input variables and different neural network designs. The study seeks to investigate the optimal artificial neural networks (ANN) parameters and settings that enhance the performance of the NARX model. Therefore, extensive parameter trails were studied for various combinations of input variables and NARX neural network configurations. The proposed model is further enhanced by preprocessing and optimising the NARX model’s input and output parameers. The prediction performance is assessed based on the mean squared error (MSE), R-squared, and hit rate. The performance of the proposed model is compared with other models, and it is shown that the utilisation of technical indicators with the NARX neural network improves the accuracy of one-step-ahead prediction for CIMB stock in Malaysia. The performance of the proposed model is further improved by optimising the input data and neural network parameters. The improved prediction of stock prices could help investors increase their returns from investment in stock markets.en_US
dc.identifier.doi10.1155/2022/9208640
dc.identifier.epage13
dc.identifier.issn1687-5265
dc.identifier.issue1
dc.identifier.other1927-20
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85127484889&origin=resultslist&sort=plf-f&src=s&st1=Empirical+Analysis+For+Stock+Price+Prediction+Using+Narx+Model+With+Exogenous+Technical+Indicator&sid=8102c27f3c57f9f8a4b013ee95de3556&sot=b&sdt=b&sl=112&s=TITLE-ABS-KEY%28Empirical+Analysis+For+Stock+Price+Prediction+Using+Narx+Model+With+Exogenous+Technical+Indicator%29&relpos=0&citeCnt=0&searchTerm=
dc.identifier.urihttps://www.hindawi.com/journals/cin/2022/9208640/
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10569
dc.identifier.volume2022
dc.language.isoenen_US
dc.publisherHindawien_US
dc.relation.ispartofComputational Intelligence and Neuroscienceen_US
dc.titleEmpirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicatorsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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