Stock Rating Analysis Based on Fast-MCD Robust Clustering Model
With the rapid development of information technology,the data contains more and more rich information,which makes the data inevitably contain contamination,and the larger the amount of data,the greater the possibility of contamination.Firstly,through simulation experiments,it is found that the traditional distance discriminant analysis is particularly sen-sitive to anomalies,and the traditional distance discriminant method is less effective when the proportion of contamination in the data increases to 30％.Secondly,the idea of Fast-MCD is applied to estimate the covariance matrix robustly and optimize it on the basis of traditional distance discrimination,and numerical simulation finds that the distance discrimination analy-sis based on Fast-MCD estimation has a significant defensive effect on contamination.Finally,800 A-share market 2021 company stock annual report data are selected as the training set and test set,respectively,the robust discrimination and traditional distance discrimination are used to discriminate the prediction and comparison of the test set data,and the results show that the results of the Fast-MCD robust distance discrimination method based on Fast-MCD are more accurate.
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