Abstract:
Stock price forecasting, is one of the most significant financial complexities, since data are not
reliable and noisy, impacting many factors. This article offers a learning machine model for the
stock price prediction using Support vector machine-Regression (SVR) with two different kernels
which are Radial Basis Function (RBF) and linear kernel. This study shows the Prediction and
accuracy comparison between Support Vector Regression (SVR) and Linear Regression (LR) and
also the accuracy comparison for different kernels of Support Vector Regression (SVR). The
model has used sum squared error (SSE) to determine the accuracy of each algorithm; which has
shown significant improvement than the other studies. This analysis is conducted on the price data
of about five years of Grameenphone listed on Dhaka Stock Exchange (DSE). The highest
accuracy was found with Linear Regression model in every case with the highest accuracy of about
97.07 percent followed by SVR (Linear) model and SVR (radial basis function) model with the
highest accuracy rate of about 97.06 and 96.82 percent. In some cases, the accuracy of SVR (radial
basis function) was higher than SVR (linear). But it was the Linear Regression which had the
highest accuracy of all in every case.
