Forecasting the movement direction of exchange rate with polynomial smooth support vector machine

It is a very interesting topic to forecast the movement direction of financial time series by machine learning methods. Among these machine learning methods, support vector machine (SVM) is the most effective and intelligent one. A new learning model is presented in this paper, called the polynomial smooth support vector machine (PSSVM). After being solved by Broyden–Fletcher–Goldfarb–Shanno (BFGS) method, optimal forecasting parameters are obtained. The exchange rate movement direction of RMB (Chinese renminbi) vs USD (United States Dollars) is investigated. Six indexes of Dow Jones China Index Series are used as the input. 4 sections with 180 time experiments have been completed. Many results show that the proposed learning model is effective and powerful.