Excerpt
III. TABLE OF CONTENTS
I. SUMMARY
II. ACKNOWLEDGEMENTS
IV. ABBREVIATIONS
V. LIST OF TABLES
VI. LIST OF FIGURES
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
7 REFERENCES
8 APPENDIX
I. SUMMARY
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.
II. ACKNOWLEDGEMENTS
First of all, many thanks to my supervisor from the ESB Reutlingen - Business School for her excellent academic support, her encouragement as well as her patience during the “storming and forming” phase at the beginning of the research project. Furthermore, many thanks to the Statistic Professor at the Northeastern University in Boston, for introducing me into the world of MINITAB and multiple regression analysis. I would also like to acknowledge Mr. Braxton for his guidance and perseverance to correct my lacking in grammatical genius.
Many thanks to FitchRatings, for providing me with all rating histories, relevant automotive documents and answering any questions. Also many thanks to Standard & Poor's for discussing my research objectives and providing me with all S&P´s rating histories of automotive manufacturers.
Last but not least, special thanks to my parents, my friends and relatives for their never ending and far reaching care and support.
IV. ABBREVIATIONS
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V. LIST OF TABLES
Table 1: Key Issues for automotive ratings according to (Moody´s Investors Service 2007)
Table 2: Rating Scale according to (FitchRatings 2010, 8)
Table 3: Financial Ratio Variables and Descriptions
Table 4: Descriptive Statistics for the Ford Motor Company
Table 5: Correlation of all independent variables (financial ratios) and all dependent variables (credit ratings) for Ford Motor Company
Table 6: Correlation of all independent variables (financial ratios) and all dependent variables (credit ratings) for Daimler Group
Table 7: Correlation of all independent variables (financial ratios) and all dependent variables (credit ratings) for BMW Group
Table 8: Correlation of Independent Variables (financial ratios) with credit Ratings
Table 9: Stepwise Regression Ford Motor Company
Table 10: Stepwise Regression Daimler Group
Table 11: Stepwise Regression BMW Group
Table 12: Stepwise Regression for all three OEMs (only illustrative use)
Table 13: Overview of OEMs regression equations
Table 14 Descriptive Statistics for the BMW Group
Table 15 Descriptive Statistics for the Daimler Group
Table 16: Output of the stepwise regression for the Daimler Group with an alpha value of 0.25
Table 17: Output of the stepwise regression for the BMW Group with an alpha value of 0.25
VI. LIST OF FIGURES
Figure 1: Scoring and rating Determination Process according to (Standard & Poor´s 2009)
Figure 2: Analysis Plan, Research Approach
Figure 3: Histogram “Rating” for the Ford Motor Company
Figure 4: Histogram “S/AR” for the Ford Motor Company
Figure 5: Histogram “EBIT/ TA” for the Ford Motor Company
Figure 6: Selection of Scatterplot “WC/S” (strong positive relation)
Figure 7: Selection of Scatterplot “S/AR” (strong negative relation)
Figure 8: Strength and direction of the coefficient of correlation according to (Lind, Marchal und Wathen 2008, 462)
Figure 9: Transformation of rating scale into numerical values
Figure 10 Histograms of financial ratio variables and rating from BMW Group, Part I
Figure 11Histograms of financial ratio variables from BMW Group, Part II
Figure 12 Histograms of financial ratio variables from BMW Group, Part III
Figure 13 Histograms of financial ratio variables and rating from Daimler Group, Part I
Figure 14 Histograms of financial ratio variables from Daimler Group, Part II
Figure 15 Histograms of financial ratio variables from Daimler Group, Part III
Figure 16 Histograms of financial ratio variables and rating from Ford Motor Company, Part I
Figure 17 Histograms of financial ratio variables from Ford Motor Company, Part II
Figure 18 Histograms of financial ratio variables from Ford Motor Company, Part III
Figure 19 Scatterplot diagrams with regression line for the total data set; Part I
Figure 20 Scatterplot diagrams with regression line for the total data set; Part II
Figure 21 Scatterplot diagrams with regression line for the total data set; Part III
1 INTRODUCTION AND STRUCTURE OF THE STUDY
External credit ratings, mainly determined by the three major rating agencies Fitch, S&P and Moody´s, play a very essential role in international capital markets. Simply speaking, a credit rating assesses the credit worthiness of an individual, a corporation, or even a country. 1909 was basically the birth of the rating area, as John Moody started to analyze the first corporations and writing about them in a small rating book. Today this market has become a multi-billion dollar industry, dominated by the three American rating agencies.
In accord with the new Basel III criteria’s which are based on the one hand on the agreements from the 2007 Basel II guidelines and on the other hand on the experiences obtained through the financial and worldwide economic crises since 2008/ 2010, credit rating agencies will play an even more central role than they have so far. One already quite visible effect is that banks and other financial institutions will especially increase their focus on the risk–return profile in commercial lending. Apart from the failures to predict fundamental crises at companies like Lehman Brothers, Enron or WorldCom which has taken some gloss and glimmer from the shining presence of credit rating agencies, the economic relevance of those agencies is tremendous (Hwang, Chung und Chu 2010, 120).
It is also important to mention, that justified concerns from investors and regulators about the quality of external credit ratings are more and more rising, given for example the fact that the agencies are getting paid by the issuers and of course considering the mentioned oligopolistic structure of the market for external credit ratings. Some voices even share the hypothesis that it is less costly to follow the competitors’ rating changes for a certain corporation than doing its own research, especially since credit rating agencies have principally the freedom to do so because the market for credit ratings is not regulated or very competitive. Additional criticism is coming from Shin and Moore who have analyzed for example the home preference hypothesis, meaning that American corporations are often preferred in terms of rating classifications compared to similar firms in Japan or Europe (Shin und Moore 2003, 328).
But in spite of the mentioned weaknesses, ratings can still be seen as “light houses” to navigate investors, creditors, auditors, government regulators or other stakeholders. The advantage of using credit ratings is that more or less every firm which is looking for either bank loans or supplier credits is classified in a certain rating which offers a virtual market transparency.
The eligibility of financial factors as inputs for external credit ratings is widely accepted, but specific information or research on which factors are in particular important remains ambiguous. The underlying research paper examines the credit ratings and financial ratios in just a single industry, auto manufacturers. The general goal of the thesis is to investigate the degree of utility of accounting data in regard to long-term credit rating. Such utility is examined by an evaluation of the statistical power of financial ratios in regard to the credit rating using a multiple regression analysis. Both, the importance of external credit ratings as well as the limited usability and restrictions of accounting data will be considered in the following chapters.
The remainder of the study is structured as follows: Chapter 1: Introduction and Structure of the Thesis Introduces the reader into the topic.
Chapter 2: Statement of the Problem and Fundamentals
After a short description of the research problem, the author presents background information about the automotive industry in respect to rating relevant issues and current trends. Secondly, fundamentals in financial statement analysis are introduced with special attention to very general procedures and ratio analysis. Furthermore, rating fundamentals are introduced that are focusing on the scoring and the rating determination process. All of this background information is necessary in order to develop a meaningful regression model which fits the needs of the research scope and overcomes shortfalls in the final interpretation of the results. The fundamentals chapter is designed to combine all the key topics which are relevant to follow the analysis without being an expert in each field.
Chapter 3: Literature Review
The third chapter is a review of relevant literature relating to credit or bond rating prediction models, especially on mathematical applications as regression or discriminant models, to name just a few. The review is conducted in order to determine the most suitable approaches and model specifications.
Chapter 4: Research Approach
In Chapter four, the data set is selected and the multiple regression model and its specifications are determined in order to conduct the analysis. This process is done in accordance with findings of the two preceding chapters. Special focus is drawn to technical problems of the selected model specifications.
Chapter 5: Analysis and Results
The outcomes of the multiple regression model and the previous conducted correlation analysis are analyzed and presented in order to show the usefulness of such a model to find the most relevant financial ratios that are influencing the credit rating.
Chapter 6: Conclusion and Outlook
This chapter evaluates the presented model as an optional analytic tool to control credit ratings. The chapter closes with areas for further research and suggestions.
2 STATEMENT OF THE PROBLEM AND FUNDAMENTALS
2.1 Problem description
The central question of the underlying analysis is, if accounting data in the form of financial ratios can be used to control and assist in credit-administration decisions as well as in credit rating manipulations.
After building a sufficient fundamental knowledge base in order to deal with credit rating and financial ratio issues, the first aim of this study is the development of a suitable multiple regression model in the rating prediction context as a control and analysis tool. The model specifications are thereby mainly determined by an analysis of the industry, academic literature for classical ratios and statistical investigations.
The second aim is to evaluate the managerial usefulness of such a multiple regression model as a financial performance measurement system and control platform in order to answer the question if financial ratios are correlated with the respective credit ratings and if “yes” which financial ratios are the most significant and important ones.
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).
There is no single definition of “industry” that is universally accepted. Widely common approaches include the focus on one or more of the following attributes (Foster 1986, 187):
- Similarity in raw material usage
- Similarity in production process
- Similarity in end product as perceived by consumers
- Similarity in end consumer group
After the worldwide recent financial crises, thinking not only at the collapse of Lehmann Brothers, but rather at the numerous automotive suppliers and even OEM´s that declared insolvency (General Motors, Chrysler), the experts from Standard & Poor's Ratings Services believe that the automotive industry will continue to stay in a state of transition, caused largely by ongoing weak economic conditions (excluding China) and revolutionary changes in technology (Standard & Poor´s 2010, 3).
The guarantees and direct funds that many governments offered in the past year (cash for clunkers program or part time work) have provided the needed liquidity injections in many cases and the reason why such a huge variety of different OEM´s is still existing in the markets1. But these actions did not solve the industry's structural problems (Standard & Poor´s 2010, 6). Macroeconomic instabilities, poor long-term profitability and the change from the classical powertrain with combustion engines to electric vehicles (E-Mobility)2 makes it very fascinating to look at the major automotive players and how they will handle these and many more challenges. How each company is dealing with those issues is not relevant for the analysis, but it shows how all market participants are facing the same issues.
The business risk factors for the automotive industry include intense competition/pricing pressures, high fixed costs, cyclical exposure, the inability to raise prices, and the need for continual cost-reduction and productivity improvements (Standard & Poor´s 2010, 3). These factors are leading the automotive players towards a high-risk operating profile that is often magnified on the financial side of the equation. This becomes especially visible through the highly leveraged company capital structures and a very high volatility of cash flows that negatively affect the debt service quality (Standard & Poor´s 2009). How immense the magnitude of those accounting figures on the credit rating really is, will be answered in the final chapters. A list of the most important key rating issues that are especially critical for the automotive industry and have to be analyzed in detail when determine a comprehensive credit ratings can be found in the following table:
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Table 1: Key Issues for automotive ratings according to (Moody´s Investors Service 2007)
Due to the turbulent market conditions (credit crunch, numerous bankruptcies, increased unemployment etc.); it is not a secret to mention that most OEM´s struggled within their business. This becomes visible when looking at the lowered credit ratings for BMW, Daimler and Ford as a result of change in rating assumptions for this asset class like:
- Greater difficulty in securing debtor-in-possession (DIP) financing,
- The scarcity of financing and demand for new vehicles, and
- The excess number of dealers and inventory.
Furthermore, the banking sector is likely to be more risk averse and not as supportive to growth as in past recoveries due to a lower equity base and higher regulatory requirements. Worsening business expectations in some industries are already reflecting these developments and therefore influence the expectations for the automotive industry, as it is highly dependable on the general economic forecast as mentioned earlier. However, at the moment experts forecast a deceleration of growth in the next few quarters and no severe double-dip for the automotive industry. Chief economist at Daimler, J. W. Müller expects that the world economy is likely to stay on the recovery slope of the business cycle and 2010 is likely to turn out significantly better than expected for the car manufacturer3.
This makes it obvious that the automotive industry is a very interesting industry regarding the analysis of the dependency of rating and the financial statement.
2.3 Fundamentals financial statement analysis
The analysis within this work tries to explain if there is a significant relation between the development of accounting figures and the credit rating of a single company. Therefore it is necessary to have a sufficient understanding of the financial statements and the possibilities to interpret them.
The general aim of the financial statement analysis is to detect relevant information of the current economic situation and also on the future economic development of a company that cannot be taken directly from standard or pure financial statements that consist of balance sheet, income statement, statement of retained earnings and statement of cash flows (Baetge 1998, 2). The financial analyst pursues his analysis with two main information objectives, one to determine the earnings (profitability analysis) and second, to determine the asset and financial situation (financial management analysis).
Concept of statement analysis
A standard balance sheet reports company´s assets on the right side and its liabilities and equity on the left side (Chacko, et al. 2006, 67).
The financial statement analysis can be clustered into six steps. In a first step the financial analyst has to obtain an overview of the constitutive facts and the general economic conditions of the company in order to interpret the results correct later on. In a second step, the comprehensive and distorted positions within financial statements due to different accounting policy and balance sheet manipulations have to be covered and translated into a unified scheme, so that a better comparison is achieved. In a third step, the data is transformed into ratios, which allow an analysis of the company's situation. For each indicator a hypothesis is formulated which specifies whether a high value of the index tends to be assessed as positive or negative. In the fourth step, certain ratios are selected from the catalogue for further analysis. The fifth step is used for a comparison of the expected and actual performance. The last step serves mainly for the interpretation of the results and is the most important one (Baetge 1998, 16).
Boundaries of the statement analysis:
The goal of the statement analysis is to gain crucial information on current economic situation and the future economic development of a company from the financial statements and the management report. The question is, whether and how far the balance sheet analysis can meet these goals. The meaningfulness of the balance sheet analysis is of course closely linked to the informational value of the financial statements. The difficulty of the balance sheet analysis is therefore based primarily on the following deficiencies in the commercial statements (Baetge 1998, 45).
- Financial statements are past performance oriented and low in detail
- distortion of financial statements by the generally accepted protection of creditors
- distortion of financial statements through different valuation options
- distortion of financial statements by tax driven accounting policies
- Distortion of financial statements because of incomplete data base of financial statement
- Specific limits of the consolidated financial statements analysis
The presented thesis focuses on the financial ratio analysis (step three and four of financial statement analysis according to Baetge), that helps to overcome the boundaries of financial statement analysis (see also Chapter 5.4).
Financial ratio analysis:
The analysis of financial ratios can be done by using cross-sectional and time series analysis. The most widely discussed cross-sectional technique is a comparison of ratios across firms (Foster 1986, 60).
In the subsequent research, the ratios are neither compared between firms nor against each other. The study is designed in a way that the ratios are analyzed over a certain time frame parallel to the credit rating at that point in time. This so called analysis of time-series trends in financial ratios is another technique frequently used in financial statement analysis (Foster 1986, 71). The specific ratios that are used in the following analysis to address the research objective are presented in Chapter 4.2; Table 3: Financial Ratio Variables and Descriptions.
Problems of financial ratio analysis
Foster mentioned already 20 years ago, that an important trend in financial reporting is the significant increase in the amount of information included in annual reports but not incorporated in the primary financial statements, for instance an increasing amount of information disclosed in footnotes or separate documents (Foster 1986, 76). Today there is an even greater diversity across firms in the format and detail of these disclosures.
Many classification and definitional issues need to be addressed when computing financial ratios. Difficulties arise on the one hand when comparing financial statements that underlie different accounting statements and on the other hand by comparing financial statements that use different possibilities for interpretations of the same accounting standards.
These issues are especially difficult when computing debt-to-equity ratios. Even when a firm has only one class of equity and one class of long term debt, the classification of deferred taxes arises. Also the growing complexity of hybrid securities has increased the gray areas when computing debt-to-equity ratios. External analysts have to employ very heuristic adjustments when estimating the debt-to-equity ratios (Foster 1986, 76). A different example is, when the analyst may prefer to derive profitability measures from LIFO-based inventory accounting - while retaining FIFO-based measures when looking at the valuation of balance sheet assets. Similarly, in some cases, the analyst has to evaluate financial information on an adjusted and an unadjusted basis when collecting and preparing the data. Another issue is the growing diversity of - off balance sheet financing methods, for example, captive and project financing arrangements.
In order to avoid these discussions and issues addressing different accounting methods (US GAAP versus IFRS and HGB) or different interpretation approaches, the author will not compare the selected samples of automotive companies or combine the data set. Each company will be analyzed separately to avoid mistakes due to accounting data that is not always directly comparable.
Furthermore the researcher was not in the comfortable situation as rating agencies are to be able to request confidential information from firms. This puts them in a better position than many external analysts by incorporating off-balance sheet items and - more important - qualitative issues into their decisions.
International comparison of financial ratios
In many situations, analysts are concerned with comparing the financial statements of companies from different countries. Issues that arise in international comparisons of financial ratios include (Foster 1986, 190):
- Differences in the set of accounting principles adopted in each country
- Differences in taxation rules adopted in each country
- Differences in the financing, operation, and other business arrangements in each country
It is important to consider these factors before making inferences based on observed differences in the financial ratios of companies.
The presented analysis involves in fact companies from Germany (Daimler and BMW) and from the US (Ford) but will not compare the final results under each other.
Computation issues in calculating ratios:
This analysis concentrates only on calculated ratios out of the financial statement. During the set up of the ratio design and the actual calculation of the financial ratios, different issues had to be considered and discussed. For example in case a company has a negative shareholders equity position, the use of some ratios (earnings to shareholders equity) can result in a ratio that has no obvious interpretation and would be misleading in the analysis. Various possibilities to handle those outliers exist in this context.
- Delete the observation from the sample.
- Examine reasons for the negative denominator and make adjustments when possible.
- The use of an alternative ratio that captures similar aspects.
[...]
1 "These initiatives threw a liquidity lifeline to auto manufacturers at the height of the financial crisis, in our view, and in doing so supported their credit quality," Standard & Poor's credit analyst Tobias Mock. (Standard & Poor´s 2010)
2 Expert Interviews with Standard & Poor’s and Roland Berger Strategy Consultants analysts
3 Source: Roland Berger Research: Daimler World Economic & Market Briefing July, 2010; Jürgen W. Müller Chief Economist