• Richa Jha, Prachi Kewaliya and Harish Patidar


Abstract- The “Implementation of Machine Learning for Stock Market Prediction System” is an act of determining the future value of a company or stock traded on a financial exchange. Support Vector Machine (SVM) is a very specific type of machine learning algorithm characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with Support Vector Machine by predicting the weekly movement direction. To evaluate the forecasting ability of SVM, we compare its performance with those of other machine learning algorithms such as linear regression. The experiment results show that Support Vector Machine outperforms other Machine Learning Algorithm like Decision Trees. Further, we propose a combining model by integrating SVM with other classification methods. The combining model performs best among both the forecasting methods. The successful prediction of a stock’s future price will yield a maximum profit to the investor. Keywords: Author Guide, Article, Camera-Ready Format, Paper Specifications, Paper Submission.
How to Cite
Harish Patidar, R. J. P. K. and. (1). STOCK MARKET PREDICTION USING SUPPORT VECTOR MACHINE. ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING ISSN: 2456-1037 INTERNATIONAL JOURNAL IF:7.98, ELJIF: 6.194(10/2018), Peer Reviewed and Refereed Journal, UGC APPROVED NO. 48767, 5(1). Retrieved from