The Prediction of Bond Ratings using Statistical and Neural Network Techniques
Student Name: Shariful Alam
In this thesis an attempt was made to model
the CRISIL ratings of long-term bonds issued by manufacturing companies in
India in calendar year 2001, using sixteen financial fundamentals covering
six different dimensions. Three approaches were adopted for this modeling
effort, namely Multiple Discriminant Analysis, Multinomial Logistic Regression
and Artificial Neural Networks, after subjecting the sixteen independent
variables to principal component analysis to reduce redundancy, which yielded
six significant principal components. All the six principal components were
found to be useful in predicting the CRISIL rating in all the three models.
These models were next validated using the 2002 data. Though the performance of
the logistic regression model was comparable to that of the more complex feed
forward neural network with one hidden layer, both these models out-performed
both Fisher's and Quadratic Discriminant Analysis models in terms of the
accuracy of prediction in both the training and validation sets.