Hybrid Approach for Financial Forecasting with Support Vector Machines

Financial markets are the world’s biggest business platforms. Therefore financial forecasting is getting a lot of attention in today economic context. Different kinds of forecasting methods, models have introduced by the research community. However risk involved with trading on those markets are very high. Such complexity made it difficult to make consistent profit from it. Building an accurate forecasting model is still an active and interesting research area for academic community. Recently nonlinear statistical models such as neural network, support vector machine have shown great capability to forecast financial markets over conventional methods. Research proposed a hybrid support vector machine model consist with wavelet transform and k-means clustering for Foreign exchange market forecasting. Model analyzes the trends and makes a forecast by entirely depending on the past exchange data.