Comparison of Return Rate Efficiencies of Forecasting Methods in Stock Market Investment

Prediction of prices in stock market is an important research topic to direct investments to items with high return rates. This thesis compares available time series prediction methods for predicting of stock market prices. The available methods that have been employed for time series forecasting are support vector regression, autoregressive moving average and k-nearest neighbours. They are applied on four years of stock market data obtained from London Stock Exchange to train each model and to test the performance of the proposed techniques to select the best forecasting method. The result of the tests show that support vector regression gives less forecasting error compared to other methods of forecasting. Keywords: Stock Market Forecasting, Support Vector Regression, ARMA, k-Nearest Neighbours.