  The credit rating is an evaluation of a company's credit risk that values the
ability to pay back the debt and predict the likelihood of the debtor
defaulting. There are various features influencing credit rating. Therefore, it
is essential to select substantive features to explore the main reason for
credit rating change. To address this issue, this paper exploited Principal
Component Analysis and Factor Analysis as feature selection algorithms to
select important features, summarized the similar features together, and
obtained a minimum set of features for four sectors, Financial Sector, Energy
Sector, Health Care Sector, Consumer Discretionary Sector. This paper used two
data sets, Financial Ratio and Balance Sheet, with two mappings, Detailed
Mapping, and Coarse Mapping, converting the target variable(credit rating) into
categorical variable. To test the accuracy of credit rating prediction, Random
Forest Classifier was used to test and train feature sets. The results showed
that the accuracy of Financial Ratio feature sets was higher than that of
Balance Sheet feature sets. In addition, Factor Analysis can reduce the number
of features significantly to obtain almost the same accuracy that can decrease
dramatically the time spent on analyzing data; we also summarized seven
dominant factors and ten dominant factors affecting credit rating change in
Financial Ratio and Balance Sheet by utilizing Factor Analysis, respectively,
which can explain the reason of credit rating change better.
