International Corporate Finance - Impact of financial ratios on long term credit ratings

Table of Contents

1 INTRODUCTION AND STRUCTURE OF THE STUDY

2 STATEMENT OF THE PROBLEM AND FUNDAMENTALS

2.1 PROBLEM DESCRIPTION

2.2 FUNDAMENTALS AUTOMOTIVE INDUSTRY - A RATING RELEVANT OUTLOOK

2.3 FUNDAMENTALS FINANCIAL STATEMENT ANALYSIS

2.4 FUNDAMENTALS RATING

3 LITERATURE REVIEW

4 RESEARCH APPROACH

4.1 INTRODUCTION

4.2 RESEARCH METHODOLOGY

4.3 CRITICAL REFLECTION OF THE METHODOLOGY

4.4 RESEARCH HYPOTHESES

5 RESEARCH ANALYSIS

5.1 BASIC STATISTICS

5.2 PREPARATION OF DATA

5.2.1 General Preparation

5.2.2 Missing Data

5.2.3 Outliers

5.3 TEST OF ASSUMPTIONS

5.3.1 Normality

5.3.2 Linearity

5.3.3 Homoscedasticity

5.3.4 Multicollinearity

5.4 DATA ANALYSIS AND INTERPRETATION OF RESULTS

5.4.1 Correlation Analysis

5.4.2 Multiple Regression Analysis

5.4.3 Presentation of the Research Results

5.4.4 Interpretation of Results

6 CONCLUSION AND OUTLOOK

Research Objectives and Topics

This thesis investigates the utility of financial ratios derived from accounting data in explaining and predicting long-term corporate credit ratings. The central objective is to determine if specific financial ratios can serve as effective analytical tools for companies to manage or control their credit ratings, utilizing a sample of major automotive manufacturers to test the statistical relationship between these accounting figures and agency ratings.

• The relationship between public financial statements and long-term credit ratings.
• Application of correlation and multiple regression analysis to identify significant financial predictors.
• Comparative analysis of automotive manufacturers (BMW Group, Daimler Group, Ford Motor Company).
• Evaluation of the limitations and predictive power of accounting data in rating determination.

Excerpt from the Book

Summary of Chapters

INTRODUCTION AND STRUCTURE OF THE STUDY: Provides an overview of the importance of credit ratings in international capital markets and outlines the structure and goals of the thesis.

STATEMENT OF THE PROBLEM AND FUNDAMENTALS: Defines the research problem regarding the utility of accounting data for credit rating management and introduces background knowledge on the automotive industry and financial statement analysis.

LITERATURE REVIEW: Critically analyzes existing literature on credit rating prediction models and the use of statistical techniques like regression and discriminant models.

RESEARCH APPROACH: Describes the methodology, including the selection of the sample, data collection procedures, and the definition of research hypotheses.

RESEARCH ANALYSIS: Performs a rigorous statistical examination including correlation analysis and multiple regression to test the hypotheses and evaluate the impact of financial ratios on ratings.

CONCLUSION AND OUTLOOK: Evaluates the effectiveness of the multiple regression model as an analytical tool and suggests areas for further research.

Keywords

Corporate Finance, Credit Rating, Financial Ratios, Automotive Industry, Multiple Regression Analysis, Statistical Modeling, Solvency, Liquidity, Profitability, Capital Structure, Credit Administration, Financial Statement Analysis, Performance Indicators, Rating Agencies, Basel III.

Frequently Asked Questions

What is the core focus of this research?

The research examines whether financial ratios derived from publicly available accounting data can be used to explain, control, and predict long-term corporate credit ratings within the automotive industry.

What are the primary thematic areas covered?

The study covers financial statement analysis, credit rating mechanisms, statistical modeling (specifically multiple regression), and industry-specific business risk factors for automotive manufacturers.

What is the primary objective of this thesis?

The main goal is to investigate the utility of accounting data in regard to long-term credit ratings and to determine which specific financial ratios significantly influence these ratings for major automotive OEMs.

Which scientific methods are employed?

The research utilizes a quantitative design employing descriptive statistics, correlation analysis, and stepwise multiple regression analysis using the MINITAB software platform.

What is covered in the main body of the work?

The main body includes a literature review of rating prediction models, a detailed research approach, the preparation of data, testing of statistical assumptions (normality, linearity, homoscedasticity, multicollinearity), and the execution of regression analyses.

Which keywords characterize this thesis?

Key terms include Corporate Finance, Credit Rating, Financial Ratios, Multiple Regression Analysis, Automotive Industry, and Solvency.

How does the author handle the problem of multicollinearity?

The author identifies redundant variables through correlation analysis and eliminates those that are highly intercorrelated (coefficient higher than 0.70 or lower than -0.70) to ensure the robustness of the regression models.

Why did the author choose to focus on automotive companies?

The focus on a single industry ensures comparability, as these companies operate under similar external economic conditions, product lifecycles, and competitive environments.