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Javani, Vajiheh, Ahmadi Bonabi, Saeid, Ashena, Malihe. (1401). Predicting Nike Company Stock Price in Stock Market Using NARX Artificial Neural Network Method. سامانه مدیریت نشریات علمی, 4(1), 13-25. doi: 10.22098/rsmm.2023.10393.1081
Vajiheh Javani; Saeid Ahmadi Bonabi; Malihe Ashena. "Predicting Nike Company Stock Price in Stock Market Using NARX Artificial Neural Network Method". سامانه مدیریت نشریات علمی, 4, 1, 1401, 13-25. doi: 10.22098/rsmm.2023.10393.1081
Javani, Vajiheh, Ahmadi Bonabi, Saeid, Ashena, Malihe. (1401). 'Predicting Nike Company Stock Price in Stock Market Using NARX Artificial Neural Network Method', سامانه مدیریت نشریات علمی, 4(1), pp. 13-25. doi: 10.22098/rsmm.2023.10393.1081
Javani, Vajiheh, Ahmadi Bonabi, Saeid, Ashena, Malihe. Predicting Nike Company Stock Price in Stock Market Using NARX Artificial Neural Network Method. سامانه مدیریت نشریات علمی, 1401; 4(1): 13-25. doi: 10.22098/rsmm.2023.10393.1081

Predicting Nike Company Stock Price in Stock Market Using NARX Artificial Neural Network Method

Research in Sport Management and Marketing
مقاله 2، دوره 4، شماره 1، فروردین 2023، صفحه 13-25  XML اصل مقاله (742.66 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22098/rsmm.2023.10393.1081
نویسندگان
Vajiheh Javani email orcid 1؛ Saeid Ahmadi Bonabi؛ Malihe Ashena2
1Department of Sport Management Faculty of Physical Education & Sport Sciences,University of Tabriz , Tabriz, IRAN
2Assisstant Professor of Sport Management, Faculty of Humanities, Bozorgmehr, University of Qaenat
چکیده
One of the biggest challenges for the companies in the stock market is to predict the trend. This research aims to study the price action of the Nike stock trend in the NYSE. The price data was gathered from NYSE in a certain time period, and other variables such as Volume, Crude Oil Prices Brent – Europe, Effective Federal Funds Rate, Gold Fixing Price, etc. those who are recognized as influential variables on stock price, are gathered from The World Bank data source. We used one Artificial Neural Networks models to analyze the data for predicting time series (NARX). After correcting the objections such as invalid data's, the Error Autocorrelation gets 95% desire, and with 0.36 MSE error, we get the approval and the model adequate to predict the trend. With these settings, the input vectors and target vectors will be randomly divided into three sets, and then the model tries to learn from the first part of the data and then test it on the test data, and at the end, the last 24-month data predicted from the model and then compared with the central values. The data comparison showed a high overlap near 95%, which proved the adequacy of the model in predicting the price trend of Nike company.
کلیدواژه‌ها
Stock Market؛ Artificial Neural Network؛ Nike؛ Time-series؛ NARX
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