TITLE:
Robust Factor Analysis and Its Applications in the CSI 100 Index
AUTHORS:
Yingying Zhang
KEYWORDS:
Robustness, Factor Analysis, R Software, CSI 100 Index, Financial Indicators
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.2 No.7,
July
14,
2014
ABSTRACT:
We apply the object-oriented robust factor
analysis R package robustfa to the 28 financial indicators of the 100 listed
companies in China’s Chinese Securities Index (CSI) 100 index in the first
quarter of 2013. First of all, according to the size of the data, we
automatically choose a robust estimator, the robust Ogk estimator. By the
Mahalanobis distances which are computed by the robust Ogk estimator, greater
than the critical value, we find a total of 47 abnormal points. This paper
discovers that the results of the sample correlation matrix, the rotated factor
loading matrix, the contribution of the factors to the original variables, the
contribution rate, the cumulative contribution rate, the screeplot of the eigenvalues
of the sample correlation matrix, the scatter plot of the first two factor
scores, factor scores, and the sorted scores according to factor scores etc.
computed by the classical estimator and the robust Ogk estimator are quite
different. Finally, we condense the 28 financial indicators to 5 factors by
combining the principal component analysis method and the robust Ogk estimator:
Provident fund market value factor, profit factor, market value profit rate
factor, value per share factor, and asset liability factor. Finally, we sort
the 5 factor scores from high to low of each factor, and also get some special
stocks according to the factor scores. The robust factor analysis results provide
a good basis for investors to choose the stocks.