Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions
This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavel...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2021/9954341 |
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| author | Abdullah H. Alenezy Mohd Tahir Ismail S. Al Wadi Muhammad Tahir Nawaf N. Hamadneh Jamil J. Jaber Waqar A. Khan |
| author_facet | Abdullah H. Alenezy Mohd Tahir Ismail S. Al Wadi Muhammad Tahir Nawaf N. Hamadneh Jamil J. Jaber Waqar A. Khan |
| author_sort | Abdullah H. Alenezy |
| collection | DOAJ |
| description | This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values according to correlation, the Engle and Granger Causality test, and multiple regressions. The input variables in this study have been collected from Saudi Authority for Statistics and Saudi Central Bank. The output variable is obtained from Tadawul. The performance of the proposed model (MODWT-LA8-ANFIS) is evaluated in terms of mean error (ME), root mean square error (RMSE), and mean absolute percentage error (MAPE). Also, we have compared the MODWT-LA8-ANFIS model with traditional models, which are autoregressive integrated moving average (ARIMA) model and ANFIS model. The obtained results show that the performance of MODWT-LA8-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is capable of decomposing in the stock markets. |
| format | Article |
| id | doaj-art-ddc3e32b1b97452c8cdec83ec9ce5501 |
| institution | OA Journals |
| issn | 2314-4629 2314-4785 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-ddc3e32b1b97452c8cdec83ec9ce55012025-08-20T02:21:33ZengWileyJournal of Mathematics2314-46292314-47852021-01-01202110.1155/2021/99543419954341Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet FunctionsAbdullah H. Alenezy0Mohd Tahir Ismail1S. Al Wadi2Muhammad Tahir3Nawaf N. Hamadneh4Jamil J. Jaber5Waqar A. Khan6Department of Mathematics, College of Science, University of Ha’il, Hail, Saudi ArabiaSchool of Mathematical Science, Universiti Sains Malaysia, Penang, MalaysiaDepartment of Risk Management and Insurance, Faculty of Business, The University of Jordan, Amman, JordanCollege of Computing and Informatics, Saudi Electronic University, Riyadh 11673, Saudi ArabiaDepartment of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi ArabiaDepartment of Risk Management and Insurance, Faculty of Business, The University of Jordan, Amman, JordanDepartment of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi ArabiaThis study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values according to correlation, the Engle and Granger Causality test, and multiple regressions. The input variables in this study have been collected from Saudi Authority for Statistics and Saudi Central Bank. The output variable is obtained from Tadawul. The performance of the proposed model (MODWT-LA8-ANFIS) is evaluated in terms of mean error (ME), root mean square error (RMSE), and mean absolute percentage error (MAPE). Also, we have compared the MODWT-LA8-ANFIS model with traditional models, which are autoregressive integrated moving average (ARIMA) model and ANFIS model. The obtained results show that the performance of MODWT-LA8-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is capable of decomposing in the stock markets.http://dx.doi.org/10.1155/2021/9954341 |
| spellingShingle | Abdullah H. Alenezy Mohd Tahir Ismail S. Al Wadi Muhammad Tahir Nawaf N. Hamadneh Jamil J. Jaber Waqar A. Khan Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions Journal of Mathematics |
| title | Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions |
| title_full | Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions |
| title_fullStr | Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions |
| title_full_unstemmed | Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions |
| title_short | Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions |
| title_sort | forecasting stock market volatility using hybrid of adaptive network of fuzzy inference system and wavelet functions |
| url | http://dx.doi.org/10.1155/2021/9954341 |
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