Extracting Common Factors to Classify Companies Listed in the Stock Exchange of Thailand by Using an Accounting Based Model
Journal Title: International Journal of the Computer, the Internet and Management - Year 2018, Vol 26, Issue 1
Abstract
The objective of this study is to classify the company listed in the Stock Exchange of Thailand by applying the Principle Component Analysis (PCA) to overcome the multicollinearity problem in the purpose of extracting the common factors used in the Logistic regression model. The data set are gathered from the companies’ annual financial statements registered in the Stock Exchange of Thailand since 2002 to 2016. The data set consists of 101 companies which there are only 29 distressed companies that have been defaulted on corporate bonds and there are 72 non-distressed companies that have not been defaulted on corporate bonds. The log-odds score are calculated based on the predicted default probability generated from the Logistic regression whose variables are integrated with the 4 statistically significant common factors. These scores are used to classify the company’s risk level. The overall accuracy of the predicted model is 92.3% which we can group the companies’ default probability into 5 different categories as very good, good, rather good, rather not good, and not good with a credit score below 475, 475-500, 500-535, 535-570, and above 570 respectively in each of which the percentages of companies contained in each categories are 58.42, 12.87, 4.95, 8.91, and 14.85 respectively.
Authors and Affiliations
Krisada Khruachalee
Keywords
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