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A model based on random subspace ensembles of support vector machines is proposed to forecastrisk of Chinese commercial banks. For one thing the results show that the proposed model performed dramatically betterwhen compared with the remaining ensemble learning algorithms, such as bagging, dagging, multiboosting and adaboosting.For anotherthe results show that support vector machines could be availably applied as base learners in the ensemble learning model when compared with multilayer neural networks (MLPs), which have been perceived as benchmark methods during previous studies. Another vital finding of the paper is that macroeconomic conditions indexes play an important role in financial risk forecasting.

Financial Risk Forecasting based on Random Subspace Ensemblesof Support Vector Machines: A study of Chinese commercial banks