Classification of Company Credit Rating Using Artificial Neural Network with Data Factorization
Journal Title: International Journal of the Computer, the Internet and Management - Year 2018, Vol 26, Issue 3
Abstract
The objective of this study is to classify company credit rating applying an Artificial Neural Network (ANN) model incorporating with a Principle Component Analysis (PCA) technique for the purpose of extracting common factors from a large panel of input variables in order to overcome the multicollinearity problem in the financial figures. The rating reports are collected from a provided website of TRIS Rating (Thailand) Co., Ltd in September, 2018. The data set consists of 183 companies classified into 6 different levels of rating (AAA, AA, A, BBB, BB, and lower than B respectively). To construct the ANN model, 60% of the sample will be used as a training set and the remaining will be assigned to play a role as a testing set. With a pre-adjustment process, the Augmented Dickey Fuller test will be applied to each time series of the 26 selected financial figures. Then, a panel of standardized financial figures will be extracted. With 26 nodes of input layers and 53 nodes in 3 hidden layers, 80.91% of the company’s credit rating was correctly classified with the training set. In addition, the overall accuracy of the proposed ANN model was improved by 3.28% when they were applied with the testing set. With the empirical findings of this study, we can infer that the ANN model with data factorization would effectively provide constructive results in classifying the company credit rating.
Authors and Affiliations
Krisada Khruachalee
Keywords
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