| Summary: | 碩士 === 國立臺灣大學 === 商學研究所 === 85 === A credit rating is an opinion of the general
creditworthiness of an obligor based on relevant risk factors.
In the U.S., the credit rating has been practiced for more than
a century and has played an important role in corporate capital
raising, investment information providing for both individual
and institutional investors, and bank''''s credit granting.
However, until May 1997, Taiwan did not have its own credit
rating agent. In this early development stage, it is urgent to
have both academia and professionals participate in the design
of a credit rating system for our financial market. For the past
two decades, the thrust of the research in credit rating has
been toward applying statistical methods such as linear
regression, multivariate discriminant analysis (MDA), ordered
probit and multinominal logit to the classification problem;
however, they all require that the input data follow some
distribution assumptions. In recent years, artificial neural
network (ANN) has been proposed to overcome this drawback.
Majority of these researches have tried to explore the
applicability of neural network for the classification problem
of binary nature, i.e., classifying bonds into either one
specific rating or not. The main objective of this
research is to design the neural network based rating system
able to handle multi-class classification. To build the rating
models, we made an extensive literature survey and consulted
with senior analysts from Taiwan Ratings Corporation regarding
S&P''''s rating criteria. Eleven financial variables are selected
as the inputs for all the methods tested (with the exception for
the airline industry model where five more operational
performance variables are added); and the data were gathered
from Standard & Poor''''s Global Sector Review Creditstats. Our
empirical results showed that the multi-class model is
significantly harder to solve than the binary-class model. Also,
most of the past research applied their analyses on the data set
across all industries. Here,we have tested our models on both
the industry-specific data and the cross-industry data.Based on
our empirical results, we can conclude some findings as follows:
1. When dealing with unstructured problems like credit rating,
the neural network models seem to outperform both the MDA and
ordered logit methods. 2. For MDA and ANN, the industry-
specific rating models seem to outperform the cross-industry
models. However, for the ordered logit, the results are not
conclusive. 3. For all methods tested, the correctness ratios
for binary classification are much higher than that for multi-
class classification. 4.
For the airline industry models tested, the addition of
operational performance ratios significantly improves the
performance of the ANN models 5. The number of hidden layers in
the ANN is strongly affected by data complexity. When the data
are of homogeneous nature, the addition of the hidden layers
does not improve the performance of the model.
To further improve the performance of the ANN models, it is
essentialto consider other business risk factors.
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