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

Abdullah Hamoud Abdulrahman Al-Nefaie · Theyazn H. H. Aldhyani

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

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.

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References

2019CNNpred: CNN-based stock market prediction using a diverse set of variables 2018An innovative neural network approach for stock market prediction 2021Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions 2019Predicting the daily return direction of the stock market using hybrid machine learning algorithms 2020An integrated framework of deep learning and knowledge graph for prediction of stock price trend: An application in Chinese stock exchange market 2006Stock market prediction with multiple classifiers 2019Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction 2019Stock Market Trend Prediction Using High-Order Information of Time Series

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