Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines

Support Vector Machines (SVM) has been a naval research field in scientific research for forecasting. This study deals with the application of SVM in financial time series predicting. This paper suggests a model of stock market prediction based on SVMs with appropriate parameter values. A data set of daily closing prices of five selected companies such as Alhaj Textiles Limited, Apex Tannery Limited, Jamuna Bank Limited, Padma Oil Company, and Square Pharmaceuticals Limited of the Dhaka Stock Exchange (DSE) from 01 January 2017 to 13 August 2019 was selected and uses these data to train the model and checks the predictive power of the model. The obtained results show that all the companies closing stock prices are non-stationary. Also the number of support vectors and mean square error is decreasing pattern with the increase of kernel parameter. It is also found that original data and predicted data are very much identical. The result shows that in all the cases SVM model has some predictive power it can be used to forecast financial time series. Several methods, such as SVM, ARIMA, single exponential smoothing, and double exponential smoothing, were performed to predict Bangladesh's stock market. Amazingly, the outcome shows the most efficient method to be Support Vector Machine because of its lowest forecasting errors.