A novel hybrid approach to measuring market interactions based on innovated SVM and WNN

In order to address nonlinearity and instability in financial issues, this paper aims at constructing a novel hybrid approach based on the integration of support vector machines (SVM) to wavelet neural networks (WNN) and at exploring its application in measuring financial market interactions. In the proposed methodology, a kind of WNN is employed to select input features for the SVM using variance rating analysis, with the SVM also improved in feature weighting through innovated discounted least square. Empirical results show that the proposed hybrid approach enables the market interaction analysis model to capture unique impact mechanism between financial markets in China more efficient than other analysis methods.