• Nargis khan

Abstract: Prediction of stock market is the act of trying to investigate the future value of a company stock or other financial instrument traded on a financial exchange. The successful forecasting of a stock's future price will maximize investor’s gains. This paper proposes a machine learning model to predict financial stock market prices. The proposed algorithm integrates calculations of technical parameters and linear support vector machine (SVM). Support Vector Machine (SVM) is comparatively new learning algorithm that has the desirable characteristics to control the decision function and the use of the kernel method. Proposed model is based on the study of stocks historical data and technical parameters. The proposed model was applied and evaluated using Indian stock (NSE) datasets. Firstly, two leading technical parameters that are Moving average convergence/Divergence (MACD) and Stochastic oscillator are calculated from the raw datasets. Secondly, Support Vector Machine is used in analyzing and predicting the stock performance. It is expected that Our Experimental results will suggest that SVM is a powerful predictive tool for stock predictions in the financial market.

Keywords: - Stock market, machine learning; SVM;

"Stock market prediction using machine learning : an approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.320-325, June 2019, Available :http://www.jetir.org/papers/JETIR1906O48.pdf

"Stock market prediction using machine learning : an approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp320-325, June 2019, Available at : http://www.jetir.org/papers/JETIR1906O48.pdf