Stock Price Forecasting Framework based on the Support Vector Regression and Monte Carlo Method
M V Kamal1, D Vasumathi2
1M V Kamal, Research Scholar, Department of CSE, JNTU, Hyderabad (Telangana), India.
2D. Vasumathi, Department of CSE, JNTU, Hyderabad (Telangana), India.
Manuscript received on 20 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 3716-3720 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B14710982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1471.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Stock market and its prices prediction are considered as one of the challenging task in financial forecasting. In my research, the framework is created based on the support vector regression (SVR) and Monte Carlo method to predict the stock price. The radial basis function (RBF) has high capacity, simpler design, and adopted for kernel function in SVR. The stock price of four companies Microsoft, Facebook, Amazon and Google is used to analyze the efficiency of the proposed method. The different parameters like mean square error (MSE), mean absolute error (MAE) measured to estimate the outcome of the proposed method. The experimental result showed the efficiency of the SVR-Monte Carlo in terms of error value. The MSE for the SVR-Monte Carlo in Google stock obtained as 0.2162 and MAE for the predicted value is 0.0164.
Keywords: Mean Square Error, Monte Carlo, Radial Basis Function, Stock Market Price Prediction, and Support Vector Regression.
Scope of the Article: Patterns and Frameworks