Özet:

The existence of outliers in multivariate data sets contaminates the parameter estimations and reduces the power of the statistical test by increasing the variance of the errors. This situation leads to deviations from the assumptions that the variables have equal variance and multivariate normal distribution. Mahalanobis distance is one of the techniques frequently used in multivariate outliers and it is calculated on the basis of multivariate location and covariance matrix, which are sensitive measures against outliers. In addition, due to the problems such as misidentification of a normal observation as an outlier and the presence of masking of an outlier, robust measures have been used. In this study, it is aimed at comparing the performance of classical and robust Mahalanobis measures. 1.239.507 stock transactions executed by investors between the periods of January 2013 - December 2017 in New York Stock Exchange and NASDAQ are used for analysis. In order to determine outlying transactions, volume and value of trade have been analyzed. Mahalanobis distances based on classical and robust measures have been calculated for each transaction and the measures are compared. As a result, the masked observations which cannot be detected by classical and robust Minimum Volume Ellipsoid measures, have been detected as outlying by Fast - Minimum Covariance Determinant (Fast MCD) measure. It has been concluded that Fast MCD can be used as an efficient estimator of multivariate location and scatter in the presence of masked data for multivariate datasets in financial applications.

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