An Investigation of Forecasting Tadawul All Share Index (TASI) Using Machine Learning

EasyChair Preprint 7373

Abstract

Stock markets are one of the most complex, and dynamic environments. To make predictions about the stock prices, we may require combining several sources of market information. Another possibility is to attempt to monitor and predict the stock index prices of a target market. In this study, we investigated several machine learning algorithms to predict the Saudi stock price index by utilizing Bloomberg’s most used indicators. The collected data represents 26 years of TASI index prices. Several machine learning algorithms were investigated for forecasting midterm TASI index pricing. Two Recurrent Neural Network (RNN) architectures (deeper, and shallower architectures) were created and their performances in forecasting TASI index prices are contrasted. Furthermore, several traditional machine learning methods such as Linear regression, decision trees, and random forests are also studied for index price prediction. The experiments suggested that with 26 years of TASI index transactions, simple machine learning models are generally suitable to make better midterm index price forecasting in comparison with more complex ML models.

Keyphrases: Long and Short-Term Memory Network, Stock index price prediction, TASI, deep learning, index price, machine learning, multivariate time series

@booklet{EasyChair:7373,
  author    = {Galal Binmakhashen and Adnan Bakather and Ali Abdulqader Bin-Salem},
  title     = {An Investigation of Forecasting Tadawul All Share Index (TASI) Using Machine Learning},
  howpublished = {EasyChair Preprint 7373},
  year      = {EasyChair, 2022}}