The global financial and economic crises resulted for many corporations in a downgraded credit rating within the last 2 to 3 years. Even a large percentage of them defaulted on their credit obligations due to inherent operational factors. The importance of credit ratings will play an even more central role under the currently discussed New Basel Capital Accord (Basel III) (Standard & Poor´s 2010; Basel III For Global Banks).
The purpose of this research is to explore the relationship between long term credit ratings and selected financial ratios that can be derived by public information. Such information can be very valuable for companies in order to have a slight control over their credit rating obtained by rating agencies as well as in negotiations with banks and other outside creditors.
The research design is based on three automotive manufacturers and involves their credit rating over the last decade. The data for the financial ratios was collected from respective financial statements.
The study is based on a correlation and multiple regression analysis using the MINITAB (Minitab Data Analysis Software, Pennsylvania USA) software as a statistical platform. A step wise approach determined the regression equation with the highest significance. The equations were used to detect those variables that have the strongest impact on the credit rating.
The results for automotive companies with a solid statistical data set are surprisingly high in significance with an adjusted coefficient of determination of over 90%. Overall it is not feasible to mention which one of the seventeen financial ratios explains the variation in credit rating most reliable, because such a statement depends always on the individual company. For example to explain the changes in the rating for the Ford Motor Company, the following six ratios turned out to be the most significant ones: total equity to total assets; sales to fixed assets; sales to inventory; net income to total equity; total equity to long term liabilities and EBIT to sales.
Each regression equation consisted mostly of different financial ratios. Apart from the fact that financial information is only one aspect of the credit rating determination process, the attained results are valid and valuable insights for all external and internal rating analysts.
The global financial and economic crises resulted for many corporations in a downgraded credit rating within the last 2 to 3 years. [...]
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
2.2 Fundamentals Automotive Industry - a rating relevant outlook
The work is focusing on three relevant players in the automotive industry – BMW Group, Daimler Group and the Ford Motor Company. The author chose Daimler and BMW in order to have the option to compare the results between those two German premium manufacturers with quite stable credit ratings and a financially distressed American corporation that offers more extreme changes in its credit ratings. The focus on pure automotive manufacturers makes it easier to compare the final results and develop recommendations based on the statistical analysis, since many external circumstances are roughly the same for all OEM´s. Furthermore comparing financial figures between different industries is not meaningful as it does not consider different capital structures, product lifecycles and other characteristics that are typical for certain industries.
Nevertheless at the very beginning, many possible companies across all industries that are rated by major rating companies were considered. However, during the further research process it became obvious, that it is advantageous to concentrate on comparable companies within the same industry. Because of the numerous fascinating innovations driven within this highly competitive industry, previous work experience and a strong affiliation towards future oriented technology and mobility, the author decided to use the automotive industry for the research analysis. This absolutely global, competitive and dynamic industry faces an especially strong competition between all major original equipment manufacturers (OEMs).
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.
- Quote paper
- Swen Beyer (Author), 2010, International Corporate Finance - Impact of financial ratios on long term credit ratings, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/162227
