Classification of Financial Stock Data Using the Vector Technology in Statistical Learning

Abstract

The financial markets are considered to be of continuous, dynamic and accelerating movement, which leads to the need for techniques methods and means for analysis and decision-making, which push investors and analysts in the financial markets to use various analysis methods and forecasting approaches for the direction of the financial market movement to make decisions for different investments in order to determine the direction of the stock movement, thus the Support Vector Machine method (SVM) is used to classify financial stock data to determine the direction of the stock, whether it is in an upward or downward trend. The aim of the research is to classify the financial stock data using five variables, where data from The Iraqi Islamic Bank for Investment and Development is used to clarify the method of the Support Vector Machine. The results showed accuracy of this technique and the performance of the Support Vector Machine is good.