Prediction of International Stock Market Movement Using Technical Analysis Methods and TSK

This research aimed to propose a method to improve forecasting accuracy of the technical analysis of future closing price using Takagi-Sugeno-Kang (TSK) fuzzy model to merge the forecasting of three technical prediction methods. The historical data available for London Stock Market is employed in this study to verify the performance of the proposed model compared to technical predictions. Fuzzy data modelling emerges as an advanced technique in predicting future closing prices. In this study, the predictions of three technical analysis methods were modelled by Fuzzy Methods to enhance the predicted closing price. The fuzzy rules were extracted by using Fuzzy-C-Means (FCM) algorithm. Data set from year 2008 to 2012 is dividing in two parts for training and verification purpose. The Fuzzy C-Means clustering (FCM) is applied on the six days Moving Average (SDMA), the Moving Average Convergence Divergence (MACD), and the Relative Strength Index (RSI) technical analysis to predict the future price, which is, target variable of the TSK fuzzy model. A prediction accuracy close to 94.7%, is achieved in predicting two days ahead closing prices of London Stock Market. The results are very encouraging and easy to implement in real-time trading system. Keywords: Technical Forecasting, Fuzzy Modelling, TSK, FCM, clustering, moving average.