# Default Prediction for Companies

__CSE691 Computational Finance __

### Spring 2004

__Introduction__

Certain fundamental analysis models can be to predict the probability that a company will default
on its debt or go bankrupt in a given period of time. This project seeks to analyze and test models
that predict the default probabilities for each publicly traded company and derive measures of
corporate health.The data used was that provided by corporate balance sheets and income statements
reported on *http://finance.yahoo.com*. More comprehensive financial data is usually confidential propriety
information and is not available freely.

I have studied and analyzed the following prediction models and applied them to our current database
of 4000 stocks. I have also done a separate analysis for the S&P 500 stocks:

Z - Score Model
Vaseick Kealhofer Model(EDF Credit measure)
Black - Scholes Model

I have also tried to correlate the different strategies and come up with a unified view towards predicting
default.

This project was done under the guidance of Prof.Steven Skiena
as part of the course on
Computational Finance taken at the
Computer Science Department,Stony Brook University in Spring 2004.
__Initial background work__

Click here to see the initial project proposal.

Click here to see the first presentation.

Click here to see the second presentation.

__The Z Score Model__

This is one of the venerable models for assessing the distress of industrial corporations. The unique
characteristics of business failures are examined in order to specify and quantify the variables which
are effective indicators and predictors of corporate distress. Specifically, a set of financial and
economic ratios are analyzed in a corporate distress prediction context using a multiple discriminant
statistical methodology.

Click here for the theory and results.
__Expected Default Frequency Credit measure(EDF)__

There are three steps required to calculate an EDF measure :
(1) Estimate the surrent market value and volatility of the firm's assets
(2) Determine how far the firm is from default, i.e its distance - to - default
(3) Scale the distance - to - default to a probability.

Click here for the theory and results
__Black-Scholes Model__

The model is one of the most widely accepted of all financial models, right from its inception in 1973
and has its roots in Option Pricing Theory. The model assumes that the firm's equity is a perpetual
option with the default point acting as an absorbing barrier for the firm's asset value. When the
value hits the default point, the firm is assumed to default.

Click here for the theory and results

__Common Analysis__

Pairwise Correlation analysis to see how well correlated the bankruptcy prediction is for all
S&P 500 stocks by each pair of methods. Click here.

Measuring the consistency of prediction across time. Click here.

Break down of analysis by industrial sector. Click here.

Break down of analysis by company size(market cap) and correlating the default probability with
market capiltaization. Click here.

__Miscellaneous__

I have also created a separate database for the S&P 500 Balance Sheet information and Income Statement information
from the existing database.

Click here to create the database.

__References and Literature Survey__

The Informational Content And Accuracy of Implied Asset Volatility as a Measure of Total Firm Risk.
Working Paper A Model For Bankruptcy Prediction
Assessing the probability of Bankruptcy
Predicting Financial Distress of Companies : The Z-Score and ZETA Models
Capital Structure and the prediction of Bankruptcy
Bayesian Analysis of Duration Models : An Application to Chapter 11 Bankruptcy
Correlated Default Risk
Debt Valuation with Endogenous Default and Chapter 11 Reorgnization
Choosing Bankruptcy Predictors Using Discriminant Analysis, Logit Analysis, and Genetic Algorithms
Predicting Corporate Financial Distress : A Time-Series CUSUM Methodology
Forecasting Bankruptcy More Accurately: A Simple Hazard Model
Multi - Period Corporate Failure Prediction With Stochastic Covariates
The Choice Among Traditional Chapter 11, Prepackaged Bankrupcty, and Out-of-Court Restructuring
The Link between Default and Recovery Rates: Theory,Empirical Evidence and Implications
Research Insights - The Barra Credit Series: Forecasting Default in the Face of Uncertainity
finance.yahoo.com
Options,Futures and Other Derivatives 5th Edition John C.Hull