Internet of Things and Cloud Computing 2020; 8(4): 46-51 http://www.sciencepublishinggroup.com/j/iotcc doi: 10.11648/j.iotcc.20200804.12 ISSN: 2376-7715 (Print); ISSN: 2376-7731 (Online) Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines Md. Farhad Hossain 1, * , Sharmin Islam 2 , Partha Chakraborty 3 , Ajit Kumar Majumder 4 1 Department of Statistics, Comilla University, Cumilla, Chattogram, Bangladesh 2 Department of Statistics, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Dhaka, Bangladesh 3 Department of Computer Science and Engineering, Comilla University, Cumilla, Chattogram, Bangladesh 4 Department of Statistics, Jahangirnagar University, Savar, Dhaka, Bangladesh Email address: * Corresponding author To cite this article: Md. Farhad Hossain, Sharmin Islam, Partha Chakraborty, Ajit Kumar Majumder. Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines. Internet of Things and Cloud Computing. Vol. 8, No. 4, 2020, pp. 46-51. doi: 10.11648/j.iotcc.20200804.12 Received: August 26, 2020; Accepted: December 23, 2020; Published: December 31, 2020 Abstract: 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. Keywords: Time series Forecasting, Financial Market, Support Vector Machines, Dhaka Stock Exchange, Machine Learning 1. Introduction Financial time series forecasting is one of the most challenging applications of modern time series forecasting [1, 4]. Stock price time series are data-intensive, noisy, dynamic, unstructured, and highly uncertain [20]. There have been many studies on forecasting time-series data. In recent years, the neural network has been successfully applied to financial time series modeling from Stock Price Index [9, 12] to the option price [11]. Over the past decade, neural networks have been successfully used for modeling financial time series [1, 19]. Recently, Support Vector Machines (SVM), a novel neural network algorithm developed by Vapnik and his colleagues is a focus research field in the world [14, 16]. SVM method, which was first suggested by Vapnik has recently been used in a range of applications such as in data mining, classification, regression, and time series forecasting [10, 7, 13]. The SVM has become a hot topic of intensive study due to its successful application in classification tasks [17, 3] and regression tasks [8, 18], especially on time series prediction [2]. SVM is a training algorithm for learning