Abstract
Classification in statistics is usually used to solve the problems of identifying to which set of categories, such as subpopulations, new observation belongs, based on a training set of data containing information (or instances) whose category membership is known. The article aims to use the Gaussian Mixture Model to model the daily closing price index over the period of 1/1/2013 to 16/8/2020 in the Kingdom of Saudi Arabia. The daily closing price index over the period declined, which might be the effect of corona virus, and the mean of the study period is about 7866.965. The closing price is the last regular deal that took place during the continuous trading period. If there are no transactions on the stock during the day, the closing price is the previous day's closing price. The closing auction period comes after the continuous trading period (from 3:00 PM to 3:10 PM), during which investors can enter by buying and selling the stocks at this period. The experimental results show that the best mixture model is E (equal variance) with three components according to the BIC criterion. The expectation-maximization (EM) algorithm converged in 2 repetitions. The data source is from Tadawul KSA.