A new procedure in stock market forecasting based on fuzzy random auto-regression time series model
Various models used in stock market forecasting presented have been classified according to the data preparation, forecasting methodology, performance evaluation, and performance measure. However, these models have not sufficiently discussed in data preparation to overcome randomness, as well as unc...
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| Format: | Article |
| Language: | English |
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ScienceDirect
2018
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| Online Access: | http://eprints.uthm.edu.my/4880/1/AJ%202018%20%28132%29.pdf |
| _version_ | 1796869101365231616 |
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| author | Efendi, Riswan Arbaiya, Nureize Mat Deris, Mustafa |
| author_facet | Efendi, Riswan Arbaiya, Nureize Mat Deris, Mustafa |
| author_sort | Efendi, Riswan |
| collection | UTHM |
| description | Various models used in stock market forecasting presented have been classified according to the data preparation, forecasting methodology, performance evaluation, and performance measure. However, these models have not sufficiently discussed in data preparation to overcome randomness, as well as uncertainty and volatility of stock prices issues in achieving high forecasting accuracy. Therefore, the focus of this paper is the data preparation procedure of triangular fuzzy number to build an improved fuzzy random autoregression model using non-stationary stock market data for forecasting purposes. The improved forecasting model considers two types of input, which are data with low-high and single point values of stock market prices. Even though, low-high data present variability and volatility in nature, the single data has to be form in symmetry left-right spread to present variability and standard error. Then, expectations and variances, confidenceintervals of fuzzy random data are constructed for fuzzy input-output data. By using the input-output data and simplex approach, parameters of the model can be estimated. In this study, some real data sets were used to represent both types of inputs, which are the Kuala Lumpur stock exchange and Alabama University enrollment. The study found that variability and spread adjustment are important factors in data preparation to improve accuracy of the fuzzy random auto-regression model. |
| first_indexed | 2024-03-05T21:49:47Z |
| format | Article |
| id | uthm.eprints-4880 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:49:47Z |
| publishDate | 2018 |
| publisher | ScienceDirect |
| record_format | dspace |
| spelling | uthm.eprints-48802021-12-23T04:22:56Z http://eprints.uthm.edu.my/4880/ A new procedure in stock market forecasting based on fuzzy random auto-regression time series model Efendi, Riswan Arbaiya, Nureize Mat Deris, Mustafa QA Mathematics T Technology (General) QA273-280 Probabilities. Mathematical statistics Various models used in stock market forecasting presented have been classified according to the data preparation, forecasting methodology, performance evaluation, and performance measure. However, these models have not sufficiently discussed in data preparation to overcome randomness, as well as uncertainty and volatility of stock prices issues in achieving high forecasting accuracy. Therefore, the focus of this paper is the data preparation procedure of triangular fuzzy number to build an improved fuzzy random autoregression model using non-stationary stock market data for forecasting purposes. The improved forecasting model considers two types of input, which are data with low-high and single point values of stock market prices. Even though, low-high data present variability and volatility in nature, the single data has to be form in symmetry left-right spread to present variability and standard error. Then, expectations and variances, confidenceintervals of fuzzy random data are constructed for fuzzy input-output data. By using the input-output data and simplex approach, parameters of the model can be estimated. In this study, some real data sets were used to represent both types of inputs, which are the Kuala Lumpur stock exchange and Alabama University enrollment. The study found that variability and spread adjustment are important factors in data preparation to improve accuracy of the fuzzy random auto-regression model. ScienceDirect 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4880/1/AJ%202018%20%28132%29.pdf Efendi, Riswan and Arbaiya, Nureize and Mat Deris, Mustafa (2018) A new procedure in stock market forecasting based on fuzzy random auto-regression time series model. Information Sciences, 441. pp. 113-132. ISSN 0020-0255 https://doi.org/10.1016/j.ins.2018.02.016 |
| spellingShingle | QA Mathematics T Technology (General) QA273-280 Probabilities. Mathematical statistics Efendi, Riswan Arbaiya, Nureize Mat Deris, Mustafa A new procedure in stock market forecasting based on fuzzy random auto-regression time series model |
| title | A new procedure in stock market forecasting based on fuzzy random auto-regression time series model |
| title_full | A new procedure in stock market forecasting based on fuzzy random auto-regression time series model |
| title_fullStr | A new procedure in stock market forecasting based on fuzzy random auto-regression time series model |
| title_full_unstemmed | A new procedure in stock market forecasting based on fuzzy random auto-regression time series model |
| title_short | A new procedure in stock market forecasting based on fuzzy random auto-regression time series model |
| title_sort | new procedure in stock market forecasting based on fuzzy random auto regression time series model |
| topic | QA Mathematics T Technology (General) QA273-280 Probabilities. Mathematical statistics |
| url | http://eprints.uthm.edu.my/4880/1/AJ%202018%20%28132%29.pdf |
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