Corporate default prediction has acquired prime importance in academic research, business practice and government regulation. As a result of internal and external economic shocks, unexpected corporate insolvencies had lead to severe damage to the economy. This highlights the crucial importance of an accurate corporate default prediction model.
This study aims to obtain new insights on how to establish and validate predictive probability of default models.
The first section examines the industry impact on default prediction. It is plausible that probability of default may differ for firms, due to distinctive nature of each industry. The second section deals with proposed key idea to assess and compare different default prediction models among Iranian listed firms across industries to examine how good a real-life probability of default can be predicted. Finally, in order to mitigate the severe consequences of the global financial and economic crisis, the current study underlines the differences of default prediction determinants during different economic periods (pre- and during global financial crisis periods).
In order to achieve the proposed objectives, logistic regression and four supervised models were employed. This includes Decision Tree, Neural network, Support Vector Machine and ensemble Adaboost classifiers. A variety of different performance metrics were applied to investigate the models accuracy.
The findings of the current study are as follows: In the course of investigating the industry’s characteristics, the indirect impact is clearly traceable due to changing of sign and magnitude of determinants across industries. The results reveal that supervised models yield higher performances than traditional linear techniques. The use of different data mining techniques improves the prediction power of the models to forecast probability of default across industries. The mechanism between variables and the probability of default is dependent on economic conditions of the country. The results indicate that the impact of different economic periods varies across industries. In addition, the results of this study may be valuable for any financial institution performing credit risk models to estimate their minimal capital requirements and to reduce the costs of risk management.
Table of Contents
1 INTRODUCTION
1.1 General overview
1.2 Background of study
1.3 Background of problem
1.3.1 Economic groups of Iran
1.4 Problem Statement
1.5 Research Objectives
1.6 Research Questions
1.7 Significance of the research
1.8 Scope of Study
1.9 Thesis Organization
1.10 Summary of the Chapter
2 LITERATURE REVIEW
2.1 Introduction
2.2 Bankruptcy and Financial Theory
2.2.1 M-M Theory
2.2.2 Signalling theory
2.3 Credit Risk and Capital Requirement
2.3.1 Probability of Default
2.3.2 Loss given Default
2.3.3 Exposure at Default
2.3.4 The distribution of potential loss
2.4 Global Financial Crisis and Default Prediction
2.5 Probability of default: a literature on concepts and models
2.5.1 Classification of corporate default prediction models
2.5.1.1 Statistical Models
2.5.1.2 Theoretical Models
2.5.1.3 Artificial Intelligence Models
2.6 Empirical Evidences of the Developed and Developing Countries
2.7 Determinants of Probability of default
2.7.1 Firm-specific Determinants
2.7.1.1 Financial ratios
2.7.2 Macro-economic Determinants
2.7.3 Industry determinants
2.8 Theoretical framework
2.9 Summary of the chapter
3 RESEARCH METHODOLOGY
3.1 Introduction
3.2 Research Design
3.3 Source of Data and Sample Population
3.3.1 Formulation of Variables
3.3.1.1 Dependent Measure of the study
3.3.1.2 Independent measure of the Study
3.3.1.3 Hypothesis
3.3.1.4 Data pre-processing (Data Cleaning)
3.3.1.5 Diagnostic Test
3.3.1.6 Implementation and Tools
3.4 Data Mining for Corporate Default Prediction
3.5 Data Mining Algorithms
3.5.1 Logistic Regression
3.5.1.1 Advantages
3.5.2 Decision Tree
3.5.2.1 Advantages
3.5.3 Artificial Neural Networks
3.5.3.1 Advantages
3.5.4 Support Vector Machines
3.5.4.1 Advantages
3.6 Multiple Classifiers Framework
3.7 Summary of Previous Studies
3.8 Comparing Classification Methods
3.9 Predictive performance metrics
3.9.1 Root Mean Squared Error
3.9.2 Mean Absolute Error (MAE)
3.9.3 Cross validation
3.9.4 Contingency table
3.9.5 ROC curve
3.9.6 Kappa Statistic- Measure of Agreement
3.10 Evaluating the Sign of Model Coefficients
3.10.1 Faulty Theory or Domain knowledge
3.10.2 Interpretation Errors
3.10.3 Model Development Problems
3.10.4 Data Problems
3.11 Summary of the Chapter
4 DATA ANALYSIS
4.1 Introduction
4.2 Descriptive Summary and Correlation Matrix analysis
4.2.1 Descriptive summary for Firm-specific and Industry Determinants
4.2.2 Descriptive summary for macroeconomic Determinants
4.2.3 Correlation Matrix
4.2.4 Data Visualizing
4.2.5 Attribute Selection
4.3 Probability of default modeling
4.3.1 Models building without industry effect
4.3.2 Model building with industry effect
4.3.2.1 Profitability
4.3.2.2 Liquidity
4.3.2.3 Leverage
4.3.2.4 Activity
4.3.2.5 Gross Domestic Product (GDP)
4.3.2.6 Lending Interest Rate
4.3.2.7 Inflation
4.3.2.8 Munificence
4.3.2.9 Dynamism
4.3.2.10 HH Index
4.3.3 Model building across industries
4.4 Benchmark Classification Techniques
4.4.1 Model Development Procedure
4.4.2 Artificial Intelligence Models Analysis
4.4.2.1 CART Classification Tree
4.4.2.2 Neural Network-Multilayer Perceptron
4.4.2.3 Support Vector Machine
4.4.3 Multiple classifiers
4.4.4 Different Economics Periods Effect on Firm’s Probability of Default
4.5 Conclusion
5 CONCLUSION AND RECOMMENDATION
5.1 Introduction
5.2 Findings and Discussions
5.2.1 Research Objective 1: To investigate the significant determinants of probability of default
5.2.2 Research Objective 2: To explore the effects of industry on probability of default
5.2.3 Research Objective 3: To compare the accuracy of statistical and data mining techniques to predict default among Iranian companies
5.2.4 Research Objective 4: To examine the effects of different economic periods (pre- and post-global financial crisis) on the determinants of the probability of default of Iranian firms
5.3 Contribution of study
5.4 Research Limitation
5.5 Future Research Recommendations
5.6 Conclusion
Research Objectives and Themes
This study focuses on corporate probability of default prediction, aiming to enhance existing models by incorporating industry-specific and macroeconomic variables. The primary research question addresses how industry characteristics and diverse economic conditions affect the predictive accuracy of default models for Iranian companies using statistical and advanced data mining techniques.
- Investigation of significant determinants of corporate default in Iranian firms.
- Analysis of the impact of industry characteristics on default probability.
- Benchmarking and comparison of statistical versus machine learning/data mining techniques.
- Assessment of how default prediction determinants shift across different economic periods (pre- vs. during the global financial crisis).
Excerpt from the Book
1.1 General overview
In the face of internal and external economic shocks, the issue of credit risk modelling has become a crucial topic among financial institutions, banks and regulators. These crises have severe impact on the banking sector of developed, emerging and developing markets (World bank, 2010). Recalling that risk is sum of two parts, namely; volatility and sensitivity. However, these are not under discretion of banks. Many banks went bankrupt and many are in distress due to their sensitivities to financial risks enlarged by the crisis (Eken, et al., 2012). In line with economic and financial turmoil, the issue of credit risk has received serious attention in the banking sector across the world.
As the corporate firms are mostly based on external financing, one of the key decisions, which lending institutions have to make is, how to establish the assessment metrics while issuing loan to firms. Suffice to say, the firms losses and financial distress is an important issue among all businesses. Consequently, its rate of occurrence in the aggregate have attentive impact on the outcomes of economic growth. Evaluating the probability of a company being able to pay back its financial obligations is of fundamental significance to providers of capital, academics and economists as well. In this context, it is important for banks to predict the potential loss of new loans in order to determine the minimal required capital to act as a safety cushion in case of companies’ defaults.
Summary of Chapters
1 INTRODUCTION: This chapter introduces the research context, highlighting the importance of credit risk modelling in the face of economic shocks and the specific need for industry-focused default prediction in Iran.
2 LITERATURE REVIEW: This chapter reviews bankruptcy theories, credit risk concepts, and existing empirical evidence from developed and developing markets to establish the theoretical framework.
3 RESEARCH METHODOLOGY: This chapter outlines the research design, detailing the variables, data mining algorithms, and validation techniques used to evaluate model performance.
4 DATA ANALYSIS: This chapter presents the empirical results, including descriptive statistics, correlation analysis, and the performance of various models across different industries and economic periods.
5 CONCLUSION AND RECOMMENDATION: This chapter summarizes the study's findings, contributions, and limitations, while offering suggestions for future research in credit risk and default prediction.
Keywords
Probability of Default (PD), Corporate Default, Data Mining, Logistic Regression, Artificial Neural Networks, Support Vector Machines, Adaboost, Industry Effects, Global Financial Crisis, Credit Risk Management, Iran, Financial Ratios, Macroeconomic Determinants, Corporate Finance, Classification Models
Frequently Asked Questions
What is the core focus of this research?
The work focuses on improving the accuracy of corporate probability of default prediction models by integrating industry-specific and macroeconomic factors alongside traditional firm-specific financial ratios.
Which thematic fields are central to the study?
The study covers corporate finance, risk management, data mining (machine learning), and macroeconomic analysis within the specific context of the Iranian economy.
What is the primary objective of this thesis?
The main goal is to examine how industry characteristics influence the probability of default and to compare the effectiveness of various statistical and data mining techniques in predicting firm failure.
Which scientific methods are employed?
The research employs logistic regression, Decision Trees (CART), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and ensemble methods like Adaboost, utilizing stratified 10-fold cross-validation for evaluation.
What does the main part of the study cover?
The main part of the thesis (data analysis) focuses on sector-wise analysis of 97 Iranian firms, identifying the most significant determinants of default and assessing model performance across stable and crisis periods.
Which keywords best characterize this work?
The work is characterized by terms like Probability of Default, Data Mining, Corporate Finance, Industry Effects, and Credit Risk Management.
How does the industry effect influence default prediction in Iran?
The study finds that industry-specific factors such as munificence and dynamism impact default probability differently across sectors, indicating that a uniform model is less effective than industry-tailored approaches.
How did the global financial crisis affect the study's findings?
The research demonstrates that the impact of default determinants is highly dependent on economic conditions; the global financial crisis altered the significance and magnitude of variables compared to the stable economic period.
- Citar trabajo
- Maryam Mirzaei (Autor), 2015, Corporate Probability Default Prediction With Industry Effects Using Data Mining Techniques, Múnich, GRIN Verlag, https://www.grin.com/document/306404
