Long Short-Term Memory and Discrete Wavelet Transform based Univariate Stock Market Prediction Model
Analyzing financial situations in the current scenario is difficult, as it requires understanding the quality and value of investments. This study predicted the movement of stock prices in the Saudi Arabian stock market (Tadawul) over a one-week period using a proposed integrated model of Long Short...
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| Format: | Article |
| Language: | English |
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University of Zagreb, Faculty of organization and informatics
2024-01-01
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| Series: | Journal of Information and Organizational Sciences |
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| Online Access: | https://hrcak.srce.hr/file/471910 |
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| author | Mutasem Jarrah |
| author_facet | Mutasem Jarrah |
| author_sort | Mutasem Jarrah |
| collection | DOAJ |
| description | Analyzing financial situations in the current scenario is difficult, as it requires understanding the quality and value of investments. This study predicted the movement of stock prices in the Saudi Arabian stock market (Tadawul) over a one-week period using a proposed integrated model of Long Short-Term Memory (LSTM), which combines LSTM, Discrete Wavelet Transform (DWT), and Autoregressive Integrated Moving Average (ARIMA). Historical closing prices of a group of four companies listed on Tadawul were used as input for the proposed LSTM model, which consists of memory units capable of storing long time periods. Once the LSTM model predicted the closing values of stocks in Tadawul, they were further analyzed using the ARIMA model. The prediction accuracy of the proposed LSTM model and the traditional ARIMA model were 97.54% and 96.29% respectively. Therefore, the proposed integrated model of LSTM is considered a useful tool for predicting stock market values. The results emphasize the significance of Deep Learning (DL) and leveraging multiple information sources in predicting stock prices. |
| format | Article |
| id | doaj-art-27e4d88c95844c9395c45e7f98bf1f25 |
| institution | Kabale University |
| issn | 1846-3312 1846-9418 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | University of Zagreb, Faculty of organization and informatics |
| record_format | Article |
| series | Journal of Information and Organizational Sciences |
| spelling | doaj-art-27e4d88c95844c9395c45e7f98bf1f252025-01-10T10:05:48ZengUniversity of Zagreb, Faculty of organization and informaticsJournal of Information and Organizational Sciences1846-33121846-94182024-01-0148226327710.31341/jios.48.2.2Long Short-Term Memory and Discrete Wavelet Transform based Univariate Stock Market Prediction ModelMutasem Jarrah0Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah – Saudi ArabiaAnalyzing financial situations in the current scenario is difficult, as it requires understanding the quality and value of investments. This study predicted the movement of stock prices in the Saudi Arabian stock market (Tadawul) over a one-week period using a proposed integrated model of Long Short-Term Memory (LSTM), which combines LSTM, Discrete Wavelet Transform (DWT), and Autoregressive Integrated Moving Average (ARIMA). Historical closing prices of a group of four companies listed on Tadawul were used as input for the proposed LSTM model, which consists of memory units capable of storing long time periods. Once the LSTM model predicted the closing values of stocks in Tadawul, they were further analyzed using the ARIMA model. The prediction accuracy of the proposed LSTM model and the traditional ARIMA model were 97.54% and 96.29% respectively. Therefore, the proposed integrated model of LSTM is considered a useful tool for predicting stock market values. The results emphasize the significance of Deep Learning (DL) and leveraging multiple information sources in predicting stock prices.https://hrcak.srce.hr/file/471910Long Short-Term Memorydeep learningpredictionUnivariateDiscrete Wavelet Transform |
| spellingShingle | Mutasem Jarrah Long Short-Term Memory and Discrete Wavelet Transform based Univariate Stock Market Prediction Model Journal of Information and Organizational Sciences Long Short-Term Memory deep learning prediction Univariate Discrete Wavelet Transform |
| title | Long Short-Term Memory and Discrete Wavelet Transform based Univariate Stock Market Prediction Model |
| title_full | Long Short-Term Memory and Discrete Wavelet Transform based Univariate Stock Market Prediction Model |
| title_fullStr | Long Short-Term Memory and Discrete Wavelet Transform based Univariate Stock Market Prediction Model |
| title_full_unstemmed | Long Short-Term Memory and Discrete Wavelet Transform based Univariate Stock Market Prediction Model |
| title_short | Long Short-Term Memory and Discrete Wavelet Transform based Univariate Stock Market Prediction Model |
| title_sort | long short term memory and discrete wavelet transform based univariate stock market prediction model |
| topic | Long Short-Term Memory deep learning prediction Univariate Discrete Wavelet Transform |
| url | https://hrcak.srce.hr/file/471910 |
| work_keys_str_mv | AT mutasemjarrah longshorttermmemoryanddiscretewavelettransformbasedunivariatestockmarketpredictionmodel |