A nonlinear subspace multiple kernel learning for financial distress prediction of Chinese listed companies

Financial distress prediction (FDP) is of great importance for managers, creditors and investors to take correct measures so as to reduce loss. Many quantitative methods have been proposed to develop empirical models for FDP recently. In this paper, a nonlinear subspace multiple kernel learning (MKL) method is proposed for the task of FDP. A key point is how basis kernels could be well explored for measuring similarity between samples while a MKL strategy is used for FDP. In the proposed MKL method, a divide-and-conquer strategy is adopted to learn the weights of the basis kernels and the optimal predictor for FDP, respectively. The optimal weights of the basis kernels in linear combination is derived through solving a nonlinear form of maximum eigenvalue problem instead of solving complicated multiple-kernel optimization. Support vector machine (SVM) is then used to generate an optimal predictor with the optimally linearly-combined kernel. In experiments, the proposed method is compared with other FDP methods on Normal and ST Chinese listed companies during the period of 2006–2013, in order to demonstrate the prediction performance. The performance of the proposed method is superior to the state-of-the-art predictor compared in the experiments.