(untitled) (Journal of Hama University, 2022)
The main objective of this research is to evaluate the effectiveness of using support vector machine in predicting the performance of stocks by applying it to the eleven private commercial banks listed on the Damascus Stocks Exchange, during the time period from 2012 to 2020. The study was done through Extracting the data contained in the financial reports and published by the study sample banks. The research hypotheses were tested using the spss modeler program, and the research reached a set of results, the most important of which is the validity of support vector machine model to predict the performance of the stocks of private commercial banks listed on the Damascus Stocks Exchange, where its accuracy in prediction reached 85.71%, and that the financial ratios included in the model are significant . The study presented a set of proposals, the most important of which are: Emphasis on the banks’ commitment to disclosure and transparency when preparing the financial statements, because the information contained in these statements is the basis on which to analyze their financial performance, and to study other factors affecting the market value of shares, which were not addressed in the current study, such as: inflation, interest rates, exchange rates, in addition to testing the validity of other models of artificial intelligence such as: genetic algorithms, fuzzy logic, artificial neural networks, random forests, and benefiting from them in predicting the performance of banks and companies in general and the performance of their shares in particular.