In this article, by using the mixed-frequency data sampling (MIDAS) model, we investigate whether economic policy uncertainty (EPU) can predict financial stress. Our estimation results show that EPU has a significant positive effect on the future financial stress, indicating that EPU is a determinant of financial stress. Moreover, the out-of-sample prediction results show that the MIDAS model performs better than the traditional time-series OLS model.

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Classical linear discriminant analysis (LDA) is based on squared Frobenious norm and hence is sensitive to outliers and noise. To improve the robustness of LDA, in this paper, we introduce capped l2,1-norm of a matrix, which employs nonsquared l2-norm and "capped" operation, and further propose a novel capped l2,1-norm linear discriminant analysis, called CLDA. Due to the use of capped l2,1-norm, CLDA can effectively remove extreme outliers and suppress the effect of noise data. In fact, CLDA can be also viewed as a weighted LDA. CLDA is solved through a series of generalized eigenvalue problems with theoretical convergency. The experimental results on an artificial data set, some UCI data sets and two image data sets demonstrate the effectiveness of CLDA.