Bankruptcy prediction has become during the past 3 decades a matter of ever rising academic interest and intensive research. This is due to the academic appeal of the problem, combined with its importance in practical applications. The practical
importance of bankruptcy prediction models grew recently even more, with “Basle-II” regulations, which were elaborated by Basle Committee on Banking Supervision to enhance the stability of international financial system. These regulations oblige financial institutions and banks to estimate the probability of default of their obligors.
There exist some fundamental economic theory to base bankruptcy prediction models on, but this typically relies on stock market prices of companies under consideration.
These prices are, however, only available for large public listed companies. Models for private firms are therefore empirical in their nature and have to rely on rigorous statistical analysis of all available information for such firms. In 95% of cases, this
information is limited to accounting information from the financial statements. Large databases of financial statements (e.g. Compustat in the USA) are maintained and often
available for research purposes.
Accounting information is particularly important for bankruptcy prediction models in emerging markets. This is because the capital markets in these countries are often underdeveloped and illiquid and don’t deliver sufficient stock market data, even for public/listed companies, for structural models to be applied.
The accounting information is normally summarized in so-called financial ratios. Such ratios (e.g. leverage ratio, calculated as Debt to Total Assets of a company) have a long
tradition in accounting analysis. Many of these ratios are believed to reflect the financial health of a company and to be related to the bankruptcy. However, these beliefs are often very vague (e.g. leverages above 70% might provoke a bankruptcy) and subjective. Quantitative bankruptcy prediction models objectify these beliefs in that they apply statistical techniques to the accounting data. [...]
Table of Contents
1. Introduction
2. Bankruptcy Prediction as a Classification Problem
2.1 Bankruptcy Prediction Models
2.2 Structural vs. Reduced Models and Explanatory Variables
2.3 Collinearity Issues
2.4 Sampling Considerations
2.5 Misclassification Costs
2.6 Measures for Model Performance
2.6.1 General
2.6.2 Performance Measures for Models with Probabilistic Output
2.6.3 Rank Correlation Measures
3. Discriminant Analysis
3.1 Discriminant Analysis as a Classification Technique
3.2 Bayesian Approach
3.2.1 Class membership as posterior probability
3.2.2 Assumption of multivariate normality
3.2.3 Distributions other than multivariate normal
3.3 Discriminant Functions Approach
3.3.1 General
3.3.2 Measures for importance of explanatory variables
3.3.3 Testing for mean differences
3.3.4 Importance and significance of discrimination functions
3.4 Stepwise Variable Selection
3.5 Sampling Considerations
3.6 Misclassifications Costs
3.7 Strengths vs. Weaknesses
3.8 Applications for Bankruptcy Prediction
4. Conditional Probability Models
4.1 General
4.2 Microeconomic Derivation
4.3 Model Estimation
4.4 Link to Discriminant Analysis
4.5 Significance Testing
4.5.1 Nested models and hypothesis testing
4.5.2 Test for overall significance of logit coefficients (omnibus test)
4.5.3 Wald test for linear restrictions and t-tests
4.5.4 Lagrange multiplier test
4.5.5 Raftery test
4.5.6 Confidence intervals
4.6 Goodness-of-Fit (GOF)
4.6.1 Mean loglikelihood
4.6.2 Saturated model and deviance
4.6.3 Categorical independents: Pearson and Deviance GOF
4.6.4 Hosmer-Lemeshow GOF test
4.6.5 Box-Tidwell nonlinearity test
4.6.6 Quasi-R² goodness-of-fit measures
4.6.7 Overdispersion
4.7 Variable Selection
4.8 Sampling Considerations
4.9 Extensions
4.9.1 Interaction and quadratic terms
4.9.2 Multinomial models
4.9.3 Mixed logit model
4.10 Collinearity Issues
4.11 Misclassification Costs
4.12 Strengths vs. Weaknesses
4.13 Applications for Bankruptcy Prediction
5. Survival Analysis
6. CUSUM Charts
7. Artificial Neural Networks
8. Some Other Techniques
9. Bankruptcy Prediction Models in Germany
10. Bankruptcy Prediction in Ukraine
11. Summary and Conclusions
Research Objectives and Themes
This work aims to examine the statistical underpinnings of bankruptcy prediction models, primarily those utilizing financial ratios. The research focuses on the mathematical and statistical theory, model estimation, and performance evaluation associated with standard techniques, as well as emerging methods for predicting financial distress in corporate environments.
- Discriminant Analysis and its Bayesian and functional approaches.
- Conditional Probability Models, with an emphasis on logit regression.
- Survival Analysis as a potential new standard for failure prediction.
- Advanced techniques including Artificial Neural Networks, CUSUM charts, and Decision Trees.
- Comparative analysis of these methodologies regarding their statistical suitability and predictive accuracy.
Excerpt from the Book
2.3 Collinearity Issues
Collinearity among explanatory variables, i.e. strong linear interrelationships among these variables, is a common problematic phenomenon in classification context. Various classification methods differ in how sensitive they are to collinearity. Normally, collinearity only becomes dangerous if the so-called variance inflation factor (VIF) exceeds 10, a VIF under 4 is considered unproblematic. The VIF is calculated for each independent variable as 1/(1 - R^2) where R^2 is the determination coefficient from the regression of that independent variable on other independent variables. Some other sources state that collinearity becomes problematic if two independent variables have a correlation coefficient exceeding 70%.
Generally, collinearity results in unstable (inefficient) parameter estimates. Moreover, collinearity can result in parameter estimates with theoretically wrong sign, which can cause difficulties with interpretation of these parameters. Stepwise variables selection also works poor when collinearity problems persist. Also, collinearity can result in additional model instability: the model performance deteriorates in new, unseen datasets, in particular if the correlation structure changes.
Summary of Chapters
1. Introduction: Discusses the academic and practical importance of bankruptcy prediction, highlighting the reliance of private firm models on accounting-based financial ratios.
2. Bankruptcy Prediction as a Classification Problem: Formulates the prediction problem mathematically and addresses common issues such as variable selection, collinearity, sampling, and model performance metrics.
3. Discriminant Analysis: Details the theory and application of multivariate discriminant analysis, covering Bayesian and function-based approaches along with relevant significance testing.
4. Conditional Probability Models: Examines logit and probit models, focusing on maximum likelihood estimation, goodness-of-fit, and extensions for categorical or multi-state data.
5. Survival Analysis: Explores duration analysis techniques, which focus on the time to failure rather than binary classification, introducing hazard and survivor functions.
6. CUSUM Charts: Analyzes the use of cumulative sum control charts as a dynamic extension for bankruptcy prediction, specifically addressing serial correlation in financial data.
7. Artificial Neural Networks: Briefly reviews the application of neural architectures in bankruptcy modeling, noting their ability to handle non-linear relationships despite challenges in significance testing.
8. Some Other Techniques: Provides an overview of alternative methods, including decision trees, multidimensional scaling, and linear goal programming.
9. Bankruptcy Prediction Models in Germany: Offers a historical and critical overview of empirical bankruptcy research conducted within Germany.
10. Bankruptcy Prediction in Ukraine: Assesses the current state and specific challenges of bankruptcy prediction modeling in the emerging Ukrainian market.
11. Summary and Conclusions: Synthesizes the discussed methodologies and provides a comparative perspective on their suitability for practical bankruptcy prediction.
Keywords
Bankruptcy Prediction, Classification Problem, Financial Ratios, Discriminant Analysis, Logit Models, Survival Analysis, CUSUM Charts, Artificial Neural Networks, Collinearity, Model Performance, Maximum Likelihood, Goodness-of-Fit, Variable Selection, Sampling Bias, Risk Assessment.
Frequently Asked Questions
What is the primary focus of this work?
The thesis focuses on the statistical foundations of bankruptcy prediction models that use financial ratios, covering both well-established methods like discriminant analysis and modern approaches like conditional probability models.
Which specific modeling techniques are analyzed in detail?
The work provides in-depth analysis of multivariate discriminant analysis, various conditional probability models (logit/probit), and survival analysis as a growing field in bankruptcy prediction.
What is the core research goal?
The goal is to provide a comprehensive statistical framework for bankruptcy prediction, helping researchers understand model estimation, validation, and the handling of practical issues like variable selection and sample bias.
Which statistical methods are used to determine variable importance?
The text discusses standardized discriminatory coefficients and structure coefficients for discriminant analysis, as well as the use of stepwise procedures and likelihood-based tests in conditional probability models.
What topics are covered in the main section of the book?
The main sections cover problem formulation, specific statistical modeling techniques, methods for handling model instability (collinearity), and strategies for model validation and performance evaluation.
What are the key descriptive keywords for this research?
Key terms include bankruptcy prediction, discriminant analysis, logit models, survival analysis, financial ratios, and multicollinearity.
How do "Structural" and "Reduced" models differ in this context?
Structural models are based on fundamental economic theories like option pricing but often require inaccessible market data. Reduced models, which are more common for private firms, rely on empirical statistical analysis of financial ratios.
Why is multicollinearity considered a significant problem?
It leads to unstable and inefficient parameter estimates, which can result in variables having theoretically incorrect signs, thereby complicating model interpretation and degrading prediction performance in unseen data.
What is the relevance of "Survivorship Bias"?
It occurs when failing firms stop reporting financial statements before bankruptcy, leading to an over-representation of successful companies in the estimation sample, which distorts the model's predictive accuracy.
- Quote paper
- Volodymyr Perederiy (Author), 2005, Statistical Techniques for Bankruptcy Prediction, Munich, GRIN Verlag, https://www.grin.com/document/299765
