Reference no: EM133265665
A common application of Discriminant Analysis is the classification of bonds into various bond rating classes. These ratings are intended to reflect the risk of the bond and influence the cost of borrowing for companies that issue bonds. Various financial ratios culled from annual reports are often used to help determine a company's bond rating.
The Excel spreadsheet BondRatingProb1.xls ( XLS)contains two sheets named Training data and Validation data. These are data from a sample of 95 companies selected from COMPUSTAT financial data tapes. The company bonds have been classified by Moody's Bond Ratings (1980) into seven classes of risk ranging from AAA, the safest, to C, the most risky. The data include ten financial variables for each company. These are:
LOPMAR: Logarithm of the operating margin,
LFIXMAR: Logarithm of the pretax fixed charge coverage,
LTDCAP: Long-term debt to capitalization,
LGERRAT: Logarithm of total long-term debt to total equity,
LLEVER: Logarithm of the leverage,
LCASHLTD: Logarithm of the cash flow to long-term debt,
LACIDRAT: Logarithm of the acid test ratio,
LCURRAT: Logarithm of the current assets to current liabilities,
LRECTURN: Logarithm of the receivable turnover,
LASSLTD: Logarithm of the net tangible assets to long-term debt.
The data are divided into 81 observations in the Training data sheet and 14 observations in the Validation data sheet. The bond ratings have been coded into numbers in the column with the title CODERTG, with AAA coded as 1, AA as 2, etc.
Question:
1. Use XLMiner to develop Discriminant Analysis and Neural Networks models to classify the bonds in the Validation data sheet. You will need to use the score new data option.
2. What is the performance of the best classifier you have been able to find? Notice that there is order in the class variables (i.e., AAA is better than AA, which is better than A,...). Would certain misclassification errors be worse than others? If so, how would you suggested measuring this?
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