Margin Variations in Support Vector Regression for the Stock Market Prediction

Support Vector Regression (SVR) has been applied successfully to financial time series prediction recently. In SVR, the ε-insensitive loss function is usually used to measure the empirical risk. The margin in this loss function is fixed and symmetrical. Typically, researchers have used methods such as crossvalidation or random selection to select a suitable ε for that particular data set. In addition, financial time series are usually embedded with noise and the associated risk varies with time. Using a fixed and symmetrical margin may have more risk inducing bad results and may lack the ability to capture the information of stock market promptly. In order to improve the prediction accuracy and to consider reducing the downside risk, we extend the standard SVR by varying the margin. By varying the width of the margin, we can reflect the change of volatility in the financial data; by controlling the symmetry of margins, we are able to reduce the downside risk. Therefore, we focus on the study of setting the width of the margin and also the study of its symmetry property.