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COMPARATIVE STUDY OF SUPPORT VECTOR REGRESSION TECHNIQUES IN A PREDICTION MODEL FOR STOCK MARKET VOLATILITY

ABSTRACT

Stock markets are used to raise the funds by corporate sectors and government organizations. Stock market volatility is an important phenomenon which helps in deciding the mature of market that it is high or low volatile. A predictive model was proposed for stock market volatility using big data analytics and the exclusive big data analysis is required to observe and conclude the trend of market. Both fundamental and technical analyses were used in that model. In fundamental analysis, sentiment analysis was used to find the sentiment score and in case of technical analysis classification and regression techniques were used to predict the future value of a stock.. This paper further represents the predictive results using graphs and also compares results of various Support Vector Regressors i.e. linear, polynomial and radial basic function regression.

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