Stock Price Prediction Using Support Vector Machine Approach

Proceedings of The International Academic Conference on Management and Economics

Year: 2019


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Stock Price Prediction Using Support Vector Machine Approach

Naliniprava Tripathy




The present study predicts the direction of the movement of the closing price of S&P BSE TECK index from January 2008 to January 2018 by using Support Vector Machines model. Further, the study uses performance measurement ‘Hit ratio’ to determine the accuracy of the SVM model. The outcomes of the study indicate that the average prediction accuracy is 60.2% after financial crises period 2008. The study also finds that the direction of the market movement is positive when closing price is higher than previous day’s closing price.  The study suggests that SVM model has better prediction performance in short and medium term compared to long term. The study indicates that an investor can make profit by investing in the Indian stock market.

Keywords: Support Vector Machine, Stock Market Prediction, BSE TECK index.