\chapter{Data Description}
\label{ch:data}

In this paper, I obtained quarterly financial and market information from different issuers (ranging from publicly-traded US bank holding companies to industrial industries, etc) during 1996-2008.  Issuers consisted of myriad of companies that have either defaulted or are currently stable.  The data for the predictors was obtained from the financial statements from each issuer.  There were 186 individual issuers that were collected.  Each issuer reported 4 data points of each variable for one year.  


\section{Defaulted Bonds}

To provide a encompass a wider selection of issuers, defaulted bonds were chosen industry and time independent.  Issuers that defaulted most (but not all) of these subsequently facing regulatory intervention as well.  In total there were 37 issuers that have defaulted in the time span of this model's data collection.
\begin{itemize}
\item \textbf{Airlines:} Delta Air Lines Inc, Northwest Airlines Corporation, FLYi, Inc,
\item \textbf{Financial Insitutions:} Lehman Brothers, Bear Stearns, WAMU
\item Delphi Corporation, Levitz Home Furnishings, Inc, Winn-Dixie Stores Inc, Calpine Generating Co LLC
\end{itemize}



\section{Data}


As mentioned in an early section, predictors should target explaining/correlating with the following \textit{CAMEL} framework.  This is the most commonly used typology of risk factors for banks is the one regulators use to summarize the results of their on-site examinations. These framework is setup to qualitatively or quantitatively represent the amount of cash the company has on hand, incoming cash flow of a company, the strength of the corporate structure, and how easily the company can liquidate the assets.  The financial variables listed below were chosen to target the stability of the company.  A subset of the variables listed below are used in common practice in calculation of the Altman Z-score (Altman 1968) \cite{altman68}. Altman, using Multiple Discriminant Analysis, combined a set of financial ratios to come up with the Z-Score. This score uses statistical techniques to predict a company's probability of failure.

The predictors will use information from the financial and balance sheet of an issuer.  Listed below are the definitions and/or and explanation of the parameters.  Also to incorporate market behavior, there are equity-driven measures added to the possible set of parameters.   

\begin{itemize}
\item \textbf{Working Capital:} Operating liquidity available to a business, calculated as current assets minus current liabilities
\item \textbf{Retained Earnings:} The percentage of net earnings not paid out as dividends, but retained by the company to be reinvested in its core business or to pay debt.
\item \textbf{Earnings Before Debit and Interest \textbf{(EBIT)}:} A company's earning power, represents the earnings which the company has achieved.  Measure of a issuer's profitability.
\item \textbf{Market Value of Equity:}  The price at which investors buy or sell a issue.
\item \textbf{Equity Market Volatility:} movements in the market in which it is traded
\item \textbf{Sales:} Total dollar amount collected for goods and services provided
\item \textbf{Total Liabilities:} The liabilities found by adding current liabilities to long-term debts. 
\item \textbf{Total Assets:} All the property owned by a corporation
\item \textbf{Net Income:} The company's total earnings, minus expenses and taxes
\end{itemize}

Equity market indicators may serve as vital flags of risk that may not be reflected in the company's financial statements.  Equity markets identify those believed to be taking on greater risk, by a higher total equity volatility.  Companies with greater equity volatility should be more likely to become insolvent.  


For the model in this paper, ratios of the above financial parameters were used so that the predictors can not only encapsulate the standard \textit{CAMEL} framework, but also incorporate market behaviors.  For example A company's debt ratio is a leverage ratio calculated by dividing total liabilities by total assets. This ratio measures the extent to which total assets have been financed with debt.  



\section{Predictors for Logistic Regression Model}


The following are the ratios and equity market information that were calculated from the collected parameters for each issuer.  From financial analysis, when analyzing certain ratios each targets certain areas of credit risk fundamentals  

\begin{itemize}
\item \textbf{Working Capital/Total Assets:} Measures the short-term liquidity of the issuer
\item \textbf{Retained Earnings/Total Assets:} Measures Historic Profitabilty
\item \textbf{EBIT/Total Assets:} Measures Current Profitabilty
\item \textbf{Market Equity/Total Liabilities} Measures Leverage
\item \textbf{Sales/Total Assets:} Approxmated the competitive situation of the company
\item \textbf{Net Income/Sales:} An equvalient measurement of the Profit Margin
\item \textbf{Equity Market Shares Volatility} 
\item \textbf{Price of Last Trade on Market} 
\end{itemize}

