Articlegreen2003

Forecasting credit ratings using an ANN and statistical techniques

30 citations0/0 files storedBond University Research Portal (Bond University)

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications Credit Risk and Financial Regulations

Abstract

In a liberal environment the conceptual importance of credit rating has increased significantly. The objective of this study is to explore and find out the effect of the financial performance data of a firm relative to the credit rating of a debt issue of that firm. The study also proposes to capture the relationship, if any, between financial performance data and credit rating given by experts in an appropriate model. Financial data relevant to debt issue ratings are obtained from the publications of a premier credit rating agency in India. Data analysis involved the building of a model using conventional multiple linear discriminant analysis and Artificial Neural Network Systems. Artificial Neural Networks (ANN) model was found to be superior to the discriminant analysis model. The ANN model can be used to increase speed and efficiency of the rating process in practical applications. In addition, if experts provide better-input data, it can be relied upon to provide an automatic rating to a significant extent.

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1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1985Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress 2001A study of using artificial neural networks to develop an early warning predictor for credit union financial distress with comparison to the probit model 2001Detection of financial distress via multivariate statistical analysis

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2019Credit Rating Forecasting Using Machine Learning Techniques 2020Application of Deep Neural Networks to assess corporate Credit Rating 2008The Effects of Credit Ratings on Stock Returns in China 2019Does credit rating revision affect the price of a special class of common stock?

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