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Predicting Close Price in Emerging Saudi Stock Exchange: Time Series Models

Electronics (Basel), 2022-11, Vol.11 (21), p.3443 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2079-9292 ;EISSN: 2079-9292 ;DOI: 10.3390/electronics11213443

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  • Title:
    Predicting Close Price in Emerging Saudi Stock Exchange: Time Series Models
  • Author: Al-Nefaie, Abdullah H. ; Aldhyani, Theyazn H. H.
  • Subjects: Algorithms ; Artificial intelligence ; Correlation coefficients ; Data collection ; deep learning ; Economic development ; Economic growth ; Forecasting ; Investments ; Machine learning ; Mathematical models ; Methods ; Multilayer perceptrons ; Neural networks ; Prices ; Pricing ; Saudi Arabia ; Saudi stock exchange ; Securities markets ; statistical analysis ; Stock exchanges ; Stock price forecasting ; Stock-exchange ; Trends
  • Is Part Of: Electronics (Basel), 2022-11, Vol.11 (21), p.3443
  • Description: The forecasting of stock prices is an important area of research because of the benefits it provides for individuals, corporations, and governments. The purpose of this study is to investigate the application of a key of study to the prediction of the adjusted closing price of a particular firm. Estimating a stock’s volatility is one of the more difficult tasks that traders must undertake. Investors are able to mitigate the risks associated with their portfolios and investments to a greater extent when stock prices can be accurately predicted. Prices of stocks do not move in a linear fashion. We propose artificial intelligence (AI) for multilayer perceptron (MLP) and long short-term memory (LSTM) models to predict fluctuations on the Saudi Stock Exchange (Tadawul). This paper focuses on the future forecasting of the stock exchange in the communication, energy, financial, and industrial sectors. The historical records from Tadawul were used as a basis for data collection for these sectors, in time periods from 2018 to 2020. For the purpose of predicting the future values of various stock market sectors, the AI algorithms were applied over a period of 60 days. They demonstrated highly effective performance when simulated using input data, which was carried out to validate the proposed model. In addition, the correlation coefficient (R) of the LSTM and MLP models for predicting the stock market in four sectors in the Saudi Stock Exchange (Tadawul) was >0.9950, which indicates that the outcomes were in good agreement with the predicted values. The outcomes of the forecasts were provided for each method based on four different measures. Among all the algorithms utilized in this work, LSTM demonstrated the most accurate findings and had the best capacity for model fitting.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2079-9292
    EISSN: 2079-9292
    DOI: 10.3390/electronics11213443
  • Source: Coronavirus Research Database
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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