Corporate Probability Default Prediction With Industry Effects Using Data Mining Techniques

Inhaltsverzeichnis (Table of Contents)

• Chapter 1: Introduction
• 1.1: Background and Motivation
• 1.2: Problem Statement
• 1.3: Research Objectives
• 1.4: Research Questions
• 1.5: Significance of the Study
• 1.6: Scope and Limitations
• 1.7: Organization of the Study
• Chapter 2: Literature Review
• 2.1: Corporate Default Prediction Models
• 2.1.1: Traditional Statistical Models
• 2.1.2: Data Mining Techniques
• 2.2: Industry Impact on Default Prediction
• 2.3: Economic Conditions and Default Prediction
• Chapter 3: Methodology
• 3.1: Data Collection and Sources
• 3.2: Data Preprocessing and Preparation
• 3.3: Model Selection and Development
• 3.3.1: Logistic Regression
• 3.3.2: Decision Tree
• 3.3.3: Neural Network
• 3.3.4: Support Vector Machine
• 3.3.5: Adaboost Ensemble Classifier
• 3.4: Model Evaluation and Validation
• Chapter 4: Results and Discussion
• 4.1: Industry Effects on Default Prediction
• 4.2: Performance Comparison of Default Prediction Models
• 4.3: Impact of Economic Conditions on Default Prediction
• Chapter 5: Conclusion and Recommendations

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

The study aims to investigate the impact of industry characteristics and economic conditions on corporate default prediction, using a combination of traditional statistical models and data mining techniques. The study seeks to develop and validate predictive probability of default models, comparing their performance in predicting default across different industries and economic periods.

• Industry impact on default prediction
• Performance comparison of different default prediction models
• Impact of economic conditions on default prediction determinants
• Evaluation of model accuracy using performance metrics
• Practical implications for financial institutions in risk management and capital requirements

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1 provides an introduction to the study, outlining the background, motivation, problem statement, research objectives, significance, scope, and limitations. Chapter 2 reviews relevant literature on corporate default prediction models, industry impact, and economic conditions. Chapter 3 details the methodology, including data collection, preprocessing, model selection, development, and evaluation. Chapter 4 presents the results of the study, analyzing the industry effects, model performance, and economic impact on default prediction. Finally, Chapter 5 concludes the study, summarizing the findings, discussing the limitations, and proposing future research directions.

Schlüsselwörter (Keywords)

Corporate default prediction, industry effects, data mining techniques, logistic regression, decision tree, neural network, support vector machine, adaboost, performance metrics, economic conditions, financial institutions, risk management, capital requirements.