Stock Price Prediction Using Support Vector Machine Approach

Naliniprava Tripathy

32 citations0/0 files storedProceedings of the International Academic Conference on Management and Economics

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

Abstract

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.

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2003Financial time series forecasting using support vector machines 2001Application of support vector machines in financial time series forecasting 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 2004An application of support vector machines in bankruptcy prediction model 2008A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression 2006Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets 2013Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm 2004Forecasting stock market movement direction with support vector machine

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