# The effect of increased competition on the quality of credit ratings

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1 FACULTEIT ECONOMIE EN BEDRIJFSWETENSCHAPPEN FACULTEIT RECHTSGELEERDHEID The effect of increased competition on the quality of credit ratings De toegevoegde waarde van credit rating agencies bij de berekening van minimum kapitaalvereisten Anne Goor S Masterproef aangeboden tot het behalen van de graad MASTER IN DE ECONOMIE, HET RECHT EN DE BEDRIJFSKUNDE Optie financieel recht en economie Promotor: Prof. Dr. Paolo Casini Co-promotor: Prof.Dr. Veerle Colaert Academiejaar

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3 Summary This master thesis is a critical analysis of the functioning of credit rating agencies and the quality of their credit ratings. We start with a short introduction about the background and the evolution in the regulation of those agencies. Further the paper gives more insights in the advantages and disadvantages which are related to credit raters. The emergence of an oligopolistic rating market and the reasons for this existence are discussed afterwards and highlight the importance of our research question, which is whether the quality of credit ratings would increase in a situation with more competition. According to the reputational capital view, credit raters publish truthful ratings in order to receive a good reputation and hence to attract more clients. We argue that this view fails to explain market behavior in situations of increased competition because in that case the value of a good reputation declines. By means of an economical model we do research after the effect of the entrance of a third rating agency, named Fitch. We end our thesis with an introduction to some alternative models which could serve as a solution to the conflicts of interest related to credit rating agencies.

4 Acknowledgements This way we would like to thank a few people who helped us with the accomplishment of our master thesis. First of all we would like to thank our promotors professor Paolo Casini and professor Veerle Colaert who stimulated us in the subject and helped us find the right direction for our research question. By encouraging us to work out a plan and to do some beginning research in the second semester of the previous year, the workload in the second year became less heavy and more achievable. We would also like to thank Werner Thiels and Lies Gevaert working at Record bank and Belfius respectively for the time they spend introducing the subject to us. Professor De Croux deserves our extra attention for guiding us through Eviews and helping us setting up a regression model using panel data. A lot of credit goes out to Gianni Pauwels, who spend an incredible amount of time helping us retrieve the data. This enabled us to do the regression tests with credit ratings, which are hard to retrieve. Last but not least, we would like to thank everybody in our close environment. Our parents and sister, who motivated us in the difficult moments. And of course our friends who were there with some pep talk after a disappointing day ACKNOWLEDGEMENTS III

5 Contents Acknowledgements... iii Contents... iv List of abbreviations... iv Introduction Literature review History of credit rating agencies concept Evolution of the regulation on CRA's The functioning of credit rating agencies Information asymmetry problem Coordination function Advantages & Disadvantages of the use of CRA's Oligopolistic market Entrance barrier Entrance to ratings markets EMPERICAL STUDY Model setup Data Results Test 1: Regression with firm credit ratings Test 2: Correlation between ratings and bond yields Test 3: Predicting default Interpretation of the results Alternative interpretation ALTERNATIVE MODELS Investor pays model Government pays model Alternatives Conclusion Bibliography Attachments CONTENTS IV

6 List of abbreviations SEC NRSRO CRA IOSCO CRD BIS S&P CESR ESMA BW NAICS MSCI FISD = Securities and Exchange Commission = Nationally Recognized Statistical Ratings Organizations = Credit Rating Agency = International Organization of Securities Commissions = Capital Requirements Directive = Bank for International Settlements = Standard & Poor s = Committee of European Securities Regulators = Europese Autoriteit voor Effecten en Markten = Burgerlijk Wetboek = North American Industry Classification System = Morgan Stanley Capital International = Fixed Income Securities Database LIST OF ABBREVIATIONS IV

7 Introduction In our master thesis we focused on the role external credit rating agencies play in the global financial system and the effect on those credit ratings when there is an increase of competition. A credit rating is defined as an opinion regarding the creditworthiness of an entity, a debt or financial obligation, debt security, preferred share or other financial instrument, or of an issuer of such a debt or financial obligation, debt security, preferred share or other financial instrument, issued using an established and defined ranking system of rating categories (Regulation 1060/2009) 1. A credit raters main function is to produce valuable information about the creditworthiness of a corporation or a financial instrument and make this available to the public. Those credit rating agencies play a crucial role in the decision-making process of investors, most of them blindly make their decision based on a published credit rating. On the other hand are credit ratings an important factor in the regulation of financial institutions by determining the minimum capital requirement. When banks supply financial services and provide clients with the opportunity to invest their money with them, a maturity transformation will occur in which the bank will have to turn short term investments into long term loans. There is a potential problem when such credit takers are insufficient liquid to fulfill the retrievals of those loans, which will cause financial institutions to be unable to satisfy the retrievals of their creditors. This may lead to a domino effect which will make the financial system collapse. Therefore, banks are required to hold a minimal amount of capital which functions as a buffer for unexpected retrievals. To accomplish this, credit ratings are given per financial transaction and are a reflection of the creditability of the investments. The process of providing ratings comes with some issues, partly due to the oligopolistic market structure of the rating industry. The three main credit rating agencies, which are recognized by the SEC and make together 90% of the whole rating market, are Standard & Poor s, Moody s and Fitch. This oligopoly position 1 Article 3(1) (a) Regulation (EC) 1060/2009 of the European Parliament and of the Council of credit rating agencies INTRODUCTION 6

8 can be the cause of negative effects. When a rating agency produces ratings, it are the issuers who pay for those services and who prefer favorable ratings. The investors, on the other hand, are the ones who rely on those ratings for free and who prefer accurate ratings. Because an issuer depends on the services of these agencies for their ratings, those agencies might take advantage of this position. The huge increase in power and collective dependency of the rating agencies have contributed a lot to the weakening of the financial system and potentially to the financial crisis. The main reasons for this must be found at the level of the regulation which is enforced on the credit raters. In 1988 the First Basel accord came into action. These are international standards which main goal is to assure the stability of the financial system. The Baselcommittee mainly contains standards and guidelines which must be followed by member states but there still exists a lack of formal entitlements. This has generated the question whether the system has a potential for improvement and how this can be done. We investigate whether it is the big dependency which must be avoided by creating more competition, or whether it is better to look for alternative solutions. The main goal of our research exists in determining whether increased competition can offer a solution for the perverse effects which come with the use of credit rating agencies or whether it is better to use alternative models like the investor pays model, government pays model or other models. In a system with more competition, the opportunity is offered for the market to be self regulating in a way perverse effects can be avoided. This brings us to our research question, which is: What is the effect of increased competition on the quality of credit ratings?. In part one of our thesis, we critically review the debate on credit rating agencies, with special attention for the particular form of the rating market which leads to an oligopoly position for the three major credit rating agencies. Part two contains an empirical study in which we do research after the effect of increased competition on the quality of credit ratings by setting up three different tests and by analyzing the outcomes. Thereafter, part three focuses on alternative models INTRODUCTION 7

9 and their potential to increase the quality of credit ratings. We end our master thesis with an overall conclusion. INTRODUCTION 8

10 1 Literature review 1.1 History of credit rating agencies concept The market for credit ratings was born in 1909 with the attendance of the first credit rating agency, named Moody s (Richard, 2002). It was in the year 1900 that John Moody established his former company John Moody & Company and published manuals which enabled to analyze the credibility of financial instruments (Partnoy, 1999). It was until 1909 that John Moody decided to enlarge its trading activities and to make itself able to judge the credibility of an instrument. The market for credit ratings has increased a lot during the following years and has opened itself for the entrance of competing credit rating agencies as Standard & Poor s and Fitch. The European directive describes CRA s as follows: Credit rating agencies publish advice about the creditability of a certain issuer or the quality of a certain financial instrument at a certain date. These advices on their own do not form recommendations within the meaning of this directive. Rating agencies must take into account intern behavioral guidelines and procedures to make sure that the published ratings are a correct reflection and that they publish potential interests or conflicts of interests which are related to the financial instruments or the issuers who are the subject of the ratings" (Regulation 2003/125/EG). Hence, credit rating agencies make it possible to get rid of the information asymmetry, which exists between the investor and the issuer of an investment and which is discussed further in the paper. Because of the importance of credit ratings in the financial markets, the regulation of these ratings is crucial. In the past, confidence was given to the fact that credit rating agencies put much weight to the value of a good reputation and that building such a reputation would cause the perverse incentives to be avoided. This theory is also called the reputational capital view (Richard, 2002). The credit LITERATURE REVIEW 9

11 rater knows that he depends on the orders of its clients for his future income and hence that he must create confidence to maintain a reputation of reliability. This made the reputational capital theory an excellent system for self regulation in the rating market and seemed to work until the arrival of the financial crisis (Hunt, 2009). Credit rating agencies and the sometimes biased credit ratings played a major role in the financial crisis and created the need for more regulation concerning credit rating agencies Evolution of the regulation on CRA's S&P, Moody s and Fitch published in the beginning of the nineteenth century bond ratings. These were the first publicly disclosed ratings on the market and were soon sold to investors (Partnoy, 1999). At that time it was still an investorpays model in which it was the investor who paid the credit rater for the rating. The financial world began to trust more and more on those credit raters (White, 2009). In 1936 the bank regulators drew up a decree whereby it was prohibited for banks to invest in speculative investment securities. These investments are rated BB+ or lower (according to S&P and Fitch) or Ba1 or lower (according to Moody s). This way the investment behavior and hence decisions of banks were dependent on the opinion of the three big rating agencies, providing S&P, Moody s and Fitch an oligopoly power. The United States Securities and Exchange Commission introduced in 1975 the new American concept Nationally Recognized Statistical Rating Organizations (NRSRO). 2 The three biggest raters were immediately classified under this statute. The purpose of the SEC was not to let banks keep the same amount of reserve, but to make the amount dependent on the risk of the investment. This risk 2 U.S. Securities and Exchange Commission, "Report on the Role and Function of Credit Rating Agencies in the Operation of the Securities Markets, As Required by Section 702(b) of the Sarbanes-Oxley Act of 2002", January 2003, See also sec3(62) of the "CREDIT RATING AGENCY REFORM ACT OF 2006" for a definition of NRSRO LITERATURE REVIEW 10

12 was determined by the level of the credit rating, which in turn was determined by a credit rating agency. Only ratings coming from NRSRO s could be used to calculate the capital requirements, whereby the oligopoly position of S&P, Moody s and Fitch was only intensified. By implementing the NRSRO-statute the entrance to the rating market is being hindered even more. Furthermore, it is very difficult for an entrant to build up so much information, reputation and connections as the three big incumbents have already done in a period of 100 years. Currently there are 10 rating agencies that enjoy the NRSRO statute (Hunt, 2009). However, the competition in the market is quickly tempered if we know that S&P, Fitch and Moody s together have a market share of more than 90%. 3 In 2004, the IOSCO (International Organization of Securities Commissions) already implemented some kind of guidelines the CRAs must stick to when giving a rating, named the IOSCO code. Unlike the Rating Agency Reform act, the code of conduct fundamentals for credit rating agencies is also applicable outside America. As described by article 4.1, comply or explain is the guiding principle which states that rating bureaus have to publish their code of conduct and indicate how the IOSCO-principles are implemented in their own code (Sommer, 1996). When they do not implement the IOSCO fundamentals, they have to explain how and why their code deviates from the IOSCO-principles. 4 The code can be divided in four important parts. First the rating agencies must guarantee the quality and integrity of the rating process. Next, CRAs must take measures to assure the independency and to avoid conflicts of interest. The third part requires transparency and clarity of the ratings and in the final part rating bureaus are required to work out procedures which guarantee the confidentiality in handling dossiers. 3 U.S. Securities and Exchange Commission, "Speech by SEC Commissioner: Remarks at the Commission Open Meeting, by Commissioner Kathleen L. Casey", 3 December 2008, 4 The Technical Committee of the International Organization of Securities Commissions, Code of Conduct Fundamentals for Credit Rating Agencies, December 2004, 11p. LITERATURE REVIEW 11

13 Because of the big responsibility of credit rating agencies when giving credit ratings, it is important that strict supervision also exists at European level. The European commission carefully analyses the way the rating agencies function. On 16 September 2009, a first European directive came into action, which aimed at assuring credit ratings used in the community to be independent, objective and of adequate quality (Regulation 1060/2009). An amendment to the CRA Directive entered into force, on 1 June 2011, giving exclusive supervisory power to the European Securities and Markets Authority (Regulation 513/2011). Because a lot of flaws and disadvantages which are caused by rating agencies are not dealt with in the existing European regulation concerning credit rating agencies, the European Parliament is working on a proposal to enhance the regulatory framework. 5 This has generated the desire of an increase in entrance possibilities, which will result in issuers no longer being dependent on only three credit rating agencies, named Moody s, Standard & Poor s and Fitch. Such an increase in competition could have a positive effect on the existence of potential conflicting interests. The economical effects which are generated by an increase in competition is analyzed in our economical model, further in the paper. 1.2 The functioning of credit rating agencies Credit rating agencies play a crucial role in the functioning of the financial markets as they determine the credibility of an investment and the risk for default. On the other hand, credit raters offer monitoring services by which they are able to force issuers to take corrective measures to avoid a downgrade (De Haan & Amtenbrink, 2011). This is called the watch procedure and will be explained in more detail. The three main credit rating agencies which are usually very comparable in structure and method are all making use of the issuer pays model (Deryn, 2009). Credit ratings can be defined on a scale of numbers and letters and differ according to the credit rater who published them. Within those rating 5 Proposal for a regulation of the European parliament and of the council amending regulation (EC) N 1060/2009 on credit rating agencies LITERATURE REVIEW 12

14 categories, modifiers are used to make a further diversification. As you can see from the table, both Fitch and S&P make use of pluses and minuses, where Moody s makes use of numbers. Moody s S&P, Fitch Numerical value assigned * 6 Investment AAA AAA 28 grade Aa AA 24,25,26 A A 21,22,23 Baa BBB 18,19,20 Ba BB 15,16,17 B B 12,13,14 Caa CCC 9,10,11 Ca CC 7 C C 4 default D D 2 A credit rater is an intermediary between the issuing entity and the investor by making valuable information available to the public and reducing the information asymmetry this way (Tang, 2008). On the other hand, a credit rating agency is able to change an existing credit rating when an event occurs which has a significant effect on a given rating. Most of the time, the intention to change a rating will be notified by a credit rater via outlooks and watchlists (De Haan & Amtenbrink, 2011). A rating outlook is an indication of the opinion of a credit rater about the direction a rating is likely to move over a medium period, more specific a one- or two- year period (Hamilton, 2004). A watchlist, on the other hand focuses more on the short time. The introduction of an outlook or a watchlist makes it possible for the credit rater to coordinate the behavior of both the investor and the issuer and to engage in an implicit contract with the rated firm (Leenaars, 2003). Below, both the information-transformation function and the coordination function are discussed in more detail. 6 *Multiple numerical values for a single rating level represents ratings with a + qualifier, no qualifier and a qualifier respectively for ratings from S&P and Fitch. For ratings from Moody s, the numerical values represent ratings with numerical modifiers 1, 2 and 3 respectively. LITERATURE REVIEW 13

15 1.2.1 Information asymmetry problem The economic function of a credit rater is defined as getting rid of existing information asymmetries by analyzing inside information and making this public in the outside-world, also known as the information-transformation function (Boot, 2008). According to this view, the use of external credit raters does have added value in the calculation process of credit risk and hence is able to reduce the existing information asymmetry. This view can also be understood in the following citation of Moody s: Generally, institutional borrowers know more about their companies than do their lenders. Moody s helps to reduce this asymmetry of information. Accordingly, credit ratings, in aggregate, lower the costs of borrowing and lending and increase overall market efficiency for both issuers and investors (Boot, 2006, P.3). The asymmetry problem can best be understood as a situation in which the issuer has information about the value of the firm which the investor doesn t have and which the issuer can not communicate in a credible way to the market (Bruyninckx, 2008). In those scenarios, a credit rating agency can function as an intermediary between the issuer and the market by analyzing the issuer and by reporting the gained information in an objective way to the public. Information- asymmetry does not only exist at the level of issuer investor, but also between the rated entity and the credit rater. This problem can be seen as an agent-principal relation between on the one side the agent or CRA and on the other side the issuer or the principal (Boot, 2006).The credit rater calculates a rating in request of his principal, the issuer. Doing so, the agent has to take into account the interests of his principal and respect those, but very often his assignment will be inspired by its own conflicting interests (Tang 2008).Between both parties, information asymmetry could occur; this means that both of them own information which the other party doesn t have (Donkers, 2010).This reciprocal information asymmetry will cause both 1 ex ante as 2 ex post effects, which will be explained further in this paper. LITERATURE REVIEW 14

16 1 Ex ante effects: Before contracting with the credit rater, the issuer will have at his disposal some information which he thinks is valuable and which he will only release to the credit rater when he is convinced that he can gain an economical benefit this way (Jorion, 2005).The credit rater in his turn will have information about the methods and procedures to come to an optimal rating, something he will use tactically during the negotiations with the issuer to optimize its income. Here the risk exists that the credit rater will only aim at generating loyalty with the potential client, the issuer, and less at creating a qualitative good rating (bruyninckx, 2008).These ex ante effects are a form of adverse selection, which attracts bad risks because the one who keeps the most information hidden, will often have the greatest interest in negotiating a rating agreement. 2 Ex post effects: Because of the information benefit, the risk exists that the credit rater will change its attitude towards the issuer once the agreement is formed and will not behave in a way that was agreed or which may be expected from him (White, 2010; Donkers, 2010). Because the credit rater has the possibility to keep his rating methods, the information and the processes to determine the rating secret to the public, he has a certain amount of freedom to make his decisions. He can give a personal influence to the rating, which makes the rating more positive than in reality, to please the issuer but which also leads to the rating losing its objectivity. Because the credit rater is dependent on the assignments of the issuer, this danger will be very present. This is an illustration of the concept moral hazard, which enters because the issuer can not observe every activity of the credit rater and so will be dependent on the reliability of this last one (Leenaars, 2003) Coordination function Investors depend for their decision whether they should or should not invest mainly on a given rating. On the other hand is the credit rater able to put pressure on a firm by threatening to change a given credit rating. This system of interactions can be described as the coordination function of credit ratings (Boot, LITERATURE REVIEW 15

18 rating agencies enable investors to make a well balanced trade-off between risk and interest rate by providing a risk-return ratio. Since not all investors are opposed to risky investments, they are now able to be rewarded in a correct manner for taking on such high levels of risk. Finally it is important to notice that credit raters can function as a warning for underperforming firms which have low levels of credithwothiness because they hold on too much debt or because they are not able to pay back in time (White, 2010). On the other hand, a lot of disadvantages and flaws are linked to the use of credit rating agencies. The methods those rating agencies use to calculate those ratings became more and more subject to discussion and critics, because they did not always generate optimal results (Cantor & Packer, 1994). By this, a credit rating agency will be confronted with all kinds of unwanted side effects of the rating process which cause the ratings to lose their objectivity (Boot, 2006). A credit rating agency who functions based on the issuer pays model, generates ratings when they are asked for by the issuer, most of the time this is the firm in which people can invest. It is the issuer who pays for the rating and who creates a source of income for the credit rater (Kudva, 2010). This causes the credit rater to lose its independency and will lead to ratings which are more positive for the issuer, but which is not always a true reflection of the reality. Another potential side effect can be found in the fact that credit raters have a limited liability. They can not be held responsible for false ratings they have published. This generates a certain amount of quasi-immunity (Bruyninckx, 2008). Knowing that only a limited number of recognized rating agencies are present in the market, named Moody s, Standard & Poor s and Fitch, the market mechanism will not reach a complete functioning. The quality of the ratings will suffer from this because the credit rater knows that the issuer only has limited possibilities to switch to another credit rater. The goal is to find models which aim at increased transparency between the investor and the investment by informing the investor in a better way. Another LITERATURE REVIEW 17

19 working point is to avoid entrance obstacles which limit competition in a way that has a negative effect on the quality of the ratings. The potential of increased competition to improve the quality of the rating is investigated in this paper. 1.3 Oligopolistic market The credit rating market has been characterized for years as a duopolistic market, dominated by Standard & Poor s and Moody s. In 1990, a third player, named Fitch, made its entrance by increasing its market share through various acquisitions. At present, the rating market consists of three major players, which are together in the possession of a market share of 90% (Smant & Van Der Ent, 1994).This high degree of concentration creates a noncompetitive market in which only a limited number of players have market power. This leaves the issuers with a limited possibility to switch to a competitor (Becker & Milbourne, 2011). The proposed measure resulting from the European proposal n 2011/0361, indicating that the issuer must consult two different credit rating agencies in order to become an objective rating, only worsens the effect of limited competition (proposal 2011/0361). Up until recently, the possibility to entrance in the rating market in America was disturbed by a regulatory barrier to entry, created by the SEC (The Securities and Exchange Commission). In 1975 the process for recognizing credit rating agencies, Nationally Recognized Statistical Rating Organizations (NRSRO), came into action. 7 This NRSRO statute formed a significant entry barrier because only NRSRO s could issue ratings which are officially recognized by the SEC. The European regulation undertakes all kinds of measures to increase competition and the proposal for a new European regulation intends to go even further to achieve this goal (De Haan & Amtenbrink, 2009). Yet the increase in the number of players and hence in competition does not guarantee qualitative ratings. The 7 U.S. Securities and Exchange Commission, "Report on the Role and Function of Credit Rating Agencies in the Operation of the Securities Markets, As Required by Section 702(b) of the Sarbanes-Oxley Act of 2002", January 2003, See also sec3(62) of the "CREDIT RATING AGENCY REFORM ACT OF 2006" for a definition of NRSRO LITERATURE REVIEW 18

20 assumption of increased quality only holds when there is a factor which causes high quality raters to benefit and low quality raters to be punished (Hunt, 2009) Entrance barrier An entrance barrier is an obstacle which avoids that a credit rater will have automatically entrance to the rating market. The credit rating market is characterized by all kinds of entry barriers. Further in this paper, a distinction will be made between natural ( 1) and institutional ( 2) entrance barriers. 1 Natural entrance barriers: A credit rater is dependent on the issuer for the rating assignment and to generate an income this way. The prospects that he will attract new clients in the future, makes that the credit rater will have attention for the way he is seen at the public (White, 2010). He will profit from building a reliable and credible reputation, to assure the amount of clients for the future. This reliability demands ratings to be formed independent from the issuer and to contain a minimum amount of objective information (Hunt, 2009). Besides these natural barriers, the entrance is also disturbed by law, by all kinds of rules and institutional restrictions which make that not every credit rater is a nationally recognized entity. A NRSRO statute gives a credit rater the exclusive status of recognized credit rating agency (Partnoy, 2006). 2 Institutional entrance barriers: The credit rating industry has been dominated during a long time by two rating agencies, forming a duopolistic market (Camanho & Deb, 2011). Those two agencies were Moody s and Standard & Poors. This was mainly due to the entrance of the NRSRO Nationally Recognized Statistical Rating Organizations. Since 1975 rating agencies could only be in the possession of such a certificate when they complied with all the different aspects, and which is granted by the Securities and Exchange Commission (SEC) (Partnoy, 2006). LITERATURE REVIEW 19

21 Because investors prefer credit rating agencies who own such a certificate, this will have a limiting effect on the entrance of new credit rating agencies, without a certificate (White, 2008). Paul S. Atkins, 2008, argumented following quote: The unintended consequence of the SEC s approach to credit rating agencies was to limit competition and information flowing to investors. The legislative history reflects a genuine concern that the SEC facilitated the creation of and perpetuated an oligopoly in the credit rating business. 8 These entrance barriers gave credit rating agencies a prominent place in the financial market. The high amount of concentration which gives an oligopoly power to the three main players, draws our attention to the stability of the credit rating market and the negative consequences that follow. Therefore we can ask the question whether a system of self regulation, which is made possible by more competition on the market, is able to offer a solution Entrance to ratings markets The entrance of the third rating agency, named Fitch, allows us to analyze the effect of increased competition in the rating market. Where issuers could previously only ask ratings and services from Standard & Poor s or Moody s, now the possibility is created to switch to a third agency, Fitch. Fitch s roots go back to 1913, when it was founded by John Knowles Fitch under the name Fitch Publishing Company (Wolfson & Crawford, 2010). But at that time Fitch was nothing more than a small player which had no or only a small effect on the existing oligopoly. It was in 1989 that Fitch became a noticeable player in the credit rating market, because of the recapitalization of Fitch (Packer & Cantor, 1995). This recapitalization was followed by a merger with IBCA limited in From that time on, Fitch continued to grow and became a serious competitor of the two existing agencies (Becker & Milbourne, 2010). 8 U.S Securities and Exchange Commission, "Speech by SEC commissioner: Remarks to the institute of international bankers by SEC commissioner Paul S. Atkins", 3 maart 2008, LITERATURE REVIEW 20

22 This way the amount of competition increased, which led to all kinds of effects. Further in this paper we try to analyze these effects and determine how this has influenced the quality of the ratings. More specific we put up a regression model to measure the effect of increased competition presented by the entrance of Fitch on the quality of the ratings published by the incumbent players, Standard & Poor s and Moody s. The quality of a rating is an important factor in our research, that s why it is crucial that this term is clearly defined. Quality is defined as the difference between the published rating and reality. A qualitative rating has to be informative, precise and most important; it must be a clear representation of the truth. Ratings with low quality, most of the time high ratings, will be in favor of the issuer but will be detrimental for the investor, because the rating is influenced by preferences and will be subjective because it reflects other things than the probability of repayment (Huang, Chen, Hsu & Wu, 2004). The income of a rating agency depends on the fees being paid for the ratings. It is the issuer, most of the time the company that is rated, that will have to pay for those fees and the investor can consult these ratings for free (Deryn, 2009). It is advantageous for the issuer to receive a rating which is as good as possible, while the investor has more interest in a rating being a correct reflection of the reality. It is up to the credit rater to balance those two interests and to create a trade-off between current incomes and reputation Reputational, market-sharing and disciplining effect In a credit rating market where the possibilities to enter are limited, the reputation of the credit rating agencies will be of major importance (Becker & Milbourne, 2011). Therefore the rating agency will try to publish ratings which are very reliable and which reflect the truth, because this will determine whether the issuer will or will not ask again for his services in the future (Partnoy, 1999). It is important that the investor has confidence in the rating, because if not, the issuer would no longer believe that it is profitable for him to ask for a rating. This could LITERATURE REVIEW 21

23 be defined as follows: Every time a rating is assigned, the agency s name, integrity and credibility are on the line and subject to inspection by the whole investment community (Wilson, 1994). When the possibility to enter is offered and finally competition is allowed, the importance of reputation will decrease. This because increasing competition will cause expected future rents for the incumbents to fall (Becker & Milbourne, 2010). This way, competition will lead to ratings that try to favor the issuer by giving ratings which do no longer reflect reality. A potential effect of increased competition will exist in the fact that the ratings of the existing rating agencies will improve (Allen, Carletti & Marquez, 2009). So a rating which was a B-rating before, might after the entrance of a third player, be transformed into an A-rating. The main idea, which was also the result of the research by Bar-Isaac and Shapiro (2010), is that there are greater incentives to provide accurate ratings when reputation losses are higher, because this way there s a lot to gain from informative ratings (Bar-Isaac & Shapiro, 2010). The reputational theory states that competition can undermine the future rents for the incumbents, but we must argue whether this is also the case when there is a situation of massive increase of the credit rating industry, which generates an increase in total rents (Mathis, Mcandrews & Rochet, 2009). This is why we must conclude that the reputational theory only makes sense when more competition corresponds to lower future rents, holding market size constant. The seminal study of Mathis et al. (2009), shows that reputation by itself is an insufficient mechanism to discipline the reporting of objective ratings. In a situation of increased competition, credit rating agencies tend to inflate ratings and this can only be avoided when a major fraction of the credit rater s income stems from other sources than rating financial instruments (Schultz-Larsen & Hasling Kyed, 2012). The decreasing importance of reputation can be found in a number of statements which fall under the theory of market-sharing effect (Pragyan, 2012). The investigation of Klein and Leffer s (1983) shows that building reputation will be of big importance when this generates the possibility to create more income in the future. Because there are more players in the market in a situation of increased LITERATURE REVIEW 22

24 competition, the incomes per player will be limited and so the effect of reputation will decrease, and because of this effect, the importance of the reputational view will decrease as well (Becker & Milbourne, 2009). Another consequence is that rating agencies will, especially when situated in a competitive market with an elastic demand side which is very price sensitive, opt to decrease their price to attract more issuers (Hill, 2009). Before the entrance of the third rating agency, Fitch, credit raters had a kind of oligopoly position, which offered them the possibility to count high prices. Credit raters tried to maintain this oligopoly position after the entrance by charging extremely low prices and maintaining this way a large part of the market share. This is called the disciplining effect (Mathis, Mcandrews & Rochet, 2009). As a conclusion we can say that the reputational effect might be weaker in situations of high competition, which means that the incentives to maintain high quality ratings declines (Becker & Milbourne, 2011). In our economical model we provide evidence for this conclusion Rating shopping Another effect we must take into account in a situation of increased competition is rating shopping. This phenomenon describes the situation in which the issuers look around for the best rating from the different rating agencies and choose only to publish the best one (Skreta & Veldkamp, 2009). When the barriers to entry are dismissed and competition is allowed, the issuers can choose between more parties to rate their products (Sangiorgy, Sokobin & Spatt, 2009). Theoretical models by Skreta and Veldkamp (2009) show the effect of rating shopping on the quality of the ratings. Results show that more complex financial products are a trigger for more rating shopping, which has as consequence that the ratings are more biased. The effect of rating shopping will be bigger when one rating agency uses criteria which are more lax than the criteria the competitors use (Adelson, 2006). Although increased competition worsens the effect of rating shopping, this isn t as much the case for corporate securities (Becker & Milbourne, 2010). Because in LITERATURE REVIEW 23

25 test 2 we use ratings of corporate bonds, the problem of rating shopping will be mainly avoided for that test. This can be explained because corporate issuers can not choose whether or not to publish a given rating because Moody s and Standard&Poor s have a policy which states that all taxable corporate bonds publicly issued in the US must be rated and published. So it doesn t matter when an issuer doesn t want to pay for a rating, the rating will be published anyway. This phenomenon is also called unsolicited ratings and describes the situation in which the evaluation of the creditworthiness of an instrument or an issuer is granted without any involvement of the issuer (Cohen, 2011). In our research, after the entry of Fitch, some issuers might ask for the ratings of this credit rating agency, but they do not have the possibility to eliminate the ratings she already received from the incumbents, Moody s and Standard & Poor s, this way the variable number of ratings issued by incumbents won t be affected. However, our sample for the empirical study consists of both bond and firm ratings, for this reason an alternative interpretation of the results, which includes the effect of rating shopping, will be added to the empirical study. LITERATURE REVIEW 24

26 2 EMPERICAL STUDY 2.1 Model setup We wish to examine the potential effect of increased competition on the quality of the credit ratings by doing three different tests which are mainly based on previous research of Becker and Milbourne (2011). Increased competition is determined by the entrance of Fitch, which was in Our sample ranges from 1996 to 2006, this because Fitch only became a remarkable player since the late 90 s caused by, as already mentioned in this paper, a major recapitalization. This entrance resulted in a significant increase in the market share of Fitch. This will be calculated by the fraction of the total number of bond ratings issued by Fitch in a particular year over the total number of bond ratings issued by Standard & Poor s and Moody s in the same year in a specific industry. To classify the industries we used the North American Industry Classification System (NAICS), see table 1 and the Morgan Stanley Capital International World (MSCI World), see table 2. In the figures below you can see the graphical evolution of the market shares of Fitch over our sample period in both industry classification systems, starting in 1996 until ,4 0,3 0,2 0,1 0 Fitch's market shares NAICS Market shares EMPERICAL STUDY 25

27 0,3 0,25 0,2 0,15 0,1 0,05 0 Fitch's marketshare MSCI World Marketshare The graphs represent the mean of the market shares over the different industries per year and are a reflection of the level of competition in the rating market. In 2000 and 2005 we notice a peak, due to the acquisition of BankWatch and the acquisition of Algorithmics respectively. Table 1 shows the market presence of Fitch over the different industries according to the NAICS, which indicates that by the end of our sample period, Fitch was especially present in Real Estate, Rental And Leasing with an average market share of 0,42 (NAICS 53) followed by Retail Trade with an average market share of 0,39 (NAICS 45). Also Public Administration, Management of Companies and Enterprises and Utilities are characterized by a prominent presence of Fitch. In table 2, you can see the market presence of Fitch over the different industries according to the MSCI World index. This table shows that by the end of our sample period, Fitch was especially present in Finance with an average market share of 0,38, followed by the Communication industry with an average market share of 0,29. We analyze how the quality of ratings corresponds to the increased competition, caused by the entrance of Fitch. Quality is defined as the difference between the rating, which was determined by the rating agency, and the true credibility of the financial instrument or entity. It is important to notice that the rating quality is not always objective. This means that the failure of a firm which received a high rating, is not automatically due to a false rating/low quality, but is influenced by a lot of other external factors. EMPERICAL STUDY 26

28 To measure our research question, we set up two regression models using panel data and one correlation model. In our first test, we analyze the regression between the firm ratings and the evolution of the market shares, in which credit rating is the dependent variable. One possible outcome is that when there is more competition, the level of the ratings will be higher (e.g. AA), meaning that the quality decreased. This can be explained because of the decrease in reputational effect, which causes the rating agencies to favor the issuer by giving him a high rating. In that case the high rating is not due to an increase in creditworthiness of the entity but solely to enhanced competition. Another possible outcome is that competition does not decrease the quality of the ratings. In that event we can not assume that there is a decrease in the reputational effect and that even with increased competition, credit ratings are reliable. We expect to come to an outcome which confirms our idea that a general increase in the level of the ratings is a direct effect of competition, meaning that competition leads to credit raters favoring the issuer instead of giving credible ratings. The problem with these increases in ratings is that decrees can be circumvented by this. In 1933 a decree came into action which contained a prohibition for banks to invest in speculative bonds, which do not reach to investment grade level. These investments are rated BB+ or lower (according to S&P and Fitch) or Ba1 or lower (according to Moody s). When the level of a rating increases only because of competition, the creditworthiness of the instrument will stay the same, and this way banks are allowed to invest in an instrument in which they would not be allowed to invest without competition. In our second test we analyze whether a rating is qualitative. Quality is measured by analyzing the correlation between the bond yield and the bond rating. The intuition behind this measure is that a bond rating, when qualitative, must reflect nothing but the prediction of a future default. A bond rating that has a high correlation with the bond yield is a rating which transforms information in a correct manner and can be seen as a qualitative rating. EMPERICAL STUDY 27

29 We expect the correlation to be lower when there is more competition in the rating market, this because of the decrease in quality as explained above. Finally we test the predictive power of the default of ratings when the level of competition changes. This will be done by comparing situations of default with the rating that was given one year before this default event. Since ratings are supposed to predict the likelihood of default, we can measure whether a rating becomes less credible when there is more competition and will lose its predictability. In our research, situations of default will be defined as a rating with level D. 2.2 Data For the firm ratings in our sample we made use of the Compustat North America Database. There we found the monthly evolution of the ratings for the different firms from 1996 to 2006, rated by Standard & Poor s. To make the test more relevant, we duplicated the test with another industry classification system, namely MSCI World, which gives us the evolution of the ratings from Standard & Poor s, Moody s and Fitch per year. For the analysis we transformed the rating scale into a numerical variable, in the table below you can find an overview of the transformation process. EMPERICAL STUDY 28

30 Moody s S&P, Fitch Numerical value assigned * 9 Investment AAA AAA 28 grade Aa AA 24,25,26 A A 21,22,23 Baa BBB 18,19,20 Ba BB 15,16,17 B B 12,13,14 Caa CCC 9,10,11 Ca CC 7 C C 4 default D D 2 For the duplication of test 1 we took the average of the three ratings. For the market shares, we used the total number of bond ratings issued by S&P, Moody s and Fitch, which we drew from the Mergent Fixed Income Securities Database (FISD). As previously mentioned, we divided the market share of Fitch by the market share of the three credit raters over the different industries. The industries refer to the 2-digit NAICS and for these codes we relied on the Oriana Database. Via compustat we could only generate ratings from firms of three industries, namely Utilities, Manufacturing-Wood, Paper, Printing and Manufacturing- Metals, Machinery, Computers. This will not bias our results because we still have a substantial number of observations. The remaining sample consists of observations. We retrieved bond yields from Bloomberg to do the correlation analysis between yield spreads and ratings. Yield spreads are defined as the difference between yield to maturity of a bond and the yield to closet maturity treasury bond. Because the total number of companies according to the NAICS was 24416, we only used firms from the industries Accomodation, Real Estate and Construction. Because every firm issues a number of different bonds, we only took one random bond transaction per firm, assuring that the different bonds we use have the same maturity. 9 *Multiple numerical values for a single rating level represents ratings with a + qualifier, no qualifier and a qualifier respectively for ratings from S&P and Fitch. For ratings from Moody s, the numerical values represent ratings with numerical modifiers 1, 2 and 3 respectively. EMPERICAL STUDY 29

31 For our third test we used firm ratings and default events (a D rating) and drew those from the Compustat Database. As mentioned before, a future default event is defined as having a D rating within the next year. 2.3 Results In this chapter we discuss the results of our test which aim at discovering the effect of increased competition in the rating market on the quality of the ratings Test 1: Regression with firm credit ratings In the first test, we regress firm credit ratings on Fitch s market share. We start with a regression where we use the level of the ratings as a dependent and market share as independent variable. Regression model: RAT t = α + β 1 MAR t + ε Dependent variable: RAT = rating level issued by the incumbents in year t Independent variable: MARt = market share in period t (with t ranking from 1996 to 2006) In the table below you can see the results for the dataset with industries from NAICS. variable Coefficient t-statistic probability R Squared C 20,79 203,93 0,000 0,004 Market share -2,27-7,32 0,000 When we do not include control variables, we see that the coefficient on Fitch s market share is negative, with a probability factor of 0,000, which indicates that market share is a significant variable in the model. The negative coefficient EMPERICAL STUDY 30

32 AAA AA+ AA AA- A+ A A- BBB+ BBB BBB- BB+ BB BB- B+ B B- CCC+ CCC CCC- CC C D suggests that more competition pushes ratings towards the lower end of the rating levels (for example toward C). This pattern is also presented in the graph which plots the frequency of each level of ratings for high and low levels of competition. We define low levels of competition as the period before 2000 and high levels of competition as the period from 2000 to before 2000 From From this graph we can see that higher level ratings are less common under high competition and lower level ratings are more common under high competition. As you can see for example the frequency of ratings of level A is 13,7% under low competition and only 9,7% under high competition. When we look at the R^2 of our model, we notice that this is very low (0,004). This low R^2 indicates that the proportion of variation in the dependent variable which is explained by the independent variable is redundant. The estimated coefficient in this model could be unreliable because we did not add controls. We now turn to a model in which we control for year- and firm fixed effects. Variable Coefficient t-statistic probability R Squared C 18,97 85,94 0,000 0,64 Market share 3,51 5,03 0,000 This pushes up the R^2 to 0,641 and makes the coefficient on market share positive (3,51) and significant. EMPERICAL STUDY 31

33 When we control for the fixed effects of the lagged ratings, we see that the R^2 even goes up to 0,97, meaning that the proportion of variation which is explained by the independent variable has increased to 97 percent. The coefficient of market share is positive (0,05) and significant. regression model: RAT t = α + β 1 MAR t + RAT t-1 + ε Dependent variable: RAT = rating level issued by the incumbents in year t Independent variable: MARt = market share in period t (with t ranking from 1996 to 2006) RAT t-1 = lagged effects of ratings Variable Coefficient t-statistic Probability R squared C 0,28 7,46 0,000 0,97 Marketshare 0,05 3,88 0,000 Rating(-1) 0,98 620,45 0,000 This positive correlation between Fitch s market share and the level of credit ratings issued by Standard & Poor s suggests that with more competition, the levels of credit ratings increases, as we assumed before. To make sure that these results were not due to our sample choice, we duplicated the test with an industry classification system from another database, namely MSCI World. These results show that the coefficient on market share is slightly positive (0,38) and significant when we do not control for fixed effects. Variable Coefficient t-statistic Probability R Squared C 20,61 785,71 0,000 0,0004 Market share 0,38 3,94 0,0001 This weak effect can be illustrated by the graph which shows small differences between the levels of ratings with high and low competition. EMPERICAL STUDY 32

34 AAA AA AA+ AA- A A+ A- BBB BBB+ BBB- BB BB+ BB- B B+ B- CCC CCC+ CCC- CC C D Before 2000 from As you can see for example all the A rating levels are comparable under high and low competition. When we have a look at R^2, which is again very low (0,0004), we can conclude that the predictive power of the model without controlling for fixed effects is again redundant. When we do control for firm- and year fixed effects, to make the model more reliable, we see that this pushes up the R^2 to 0,73 and hence increases the predictive power of the model. The coefficient of Fitch s market share remains positive (0,34) and significant. Variable Coefficient t-statistic Probability R Squared C 20, ,27 0,000 0,73 Market share 0,34-5,49 0,000 When we control for fixed effects of lagged ratings, we see that this pushes up R squared to 0,98 and makes the coefficient on the market share positive and significant (0,23). From which we can again conclude that in situations of increased competition, the levels of credit ratings are significant higher. EMPERICAL STUDY 33

35 Variable Coefficient t-statistic Probability R squared C 0,18 12,06 0,000 0,98 Market share 0,23 3,51 0,000 Rating (-1) 0, ,69 0,000 These results diminish the value of the use of credit ratings in the financial system because the increase in the level of these ratings are not a representation of the change in creditworthiness of the firms and do only reflect the increase in competition. Drawbacks We must draw attention to some issues related to our sample. First, there might be some selection in our sample of market shares of Fitch. This lack of randomness is due to the fact that Fitch will be more likely to enter into industries in which firms receive low ratings and hence are more likely to ask for a third opinion. The higher market shares in those industries can be explained by the assumption that it was easier for Fitch to grow its presence in booming sectors, where credit demand was high. Second, our findings could be biased because of the effect of firm heterogeneity. This could be avoided by including accounting-based firm controls like firm size, profitability, indebtedness, etc. However, this was almost impossible for our research because our data originates from different databases, which requires transforming any extra control variable manually to link them to the correct time period. The test could be enhanced by analyzing whether the effect of competition is felt stronger for firms with higher levels of indebtedness and hence are more concerned about their ratings. This could be done by including several levels of leverage in the model and measure how they interact with Fitch s market share. For the same reason as mentioned before, this was impossible with the data we had available. EMPERICAL STUDY 34

36 2.3.2 Test 2: Correlation between ratings and bond yields In the second test we examine the level of information contained in a credit rating under different levels of competition. This is done by measuring the correlation between ratings and bond yield spreads. In this approach we assume that ratings are able to predict the value of bonds, whereby we use the evolution of bond yields to assess the informativeness of ratings. The bond yields are calculated using bonds from MSCI World industry firms whereby we only included bonds with maturity of ten years. We chose to only use bonds with the same maturity to increase the comparability. The correlation between bond yield spreads and ratings will be lower when the ratings are less qualitative and less informative. Because in that case, ratings will predict other things than the chance for repayment. Yield spread is defined as the difference between the yield for the government bond with closest maturity and the yield to maturity of the bonds. For the government bond data with maturity of ten years, we made use of the Federal Reserve s H15 report. The result of the correlation test between ratings and yield spreads is shown in the table below. rating yield rating yield From the table we can see that ratings and yield spreads are negatively correlated, indicating that when rating levels increase, yield spreads decrease. We now do a regression to measure the interaction between the credit ratings and Fitch s market share to include the effect of competition. Regression model: RAT t = α + β 1 MAR t + ε Dependent variable: RAT = rating level issued by the incumbents in year t EMPERICAL STUDY 35

37 Independent variable: MARt = market share in period t (with t ranking from 1996 to 2006) As you can see from the table below, the coefficient on credit ratings is positive (0,38) and significant, indicating that credit ratings increase with higher levels of competition. As a consequence we can derive that when there is more competition, the level of credit ratings is higher and the bond yields are lower. Variable Coefficient t-statistic Probability C 20,61 785,71 0,000 Market share 0,38 3,94 0,0001 Next we measure the effect on the correlation between credit ratings and bond yields when the level of competition increases. We do this by splitting up our sample. In one part we measure the correlation in a period of low competition (1996 to 2000) and in the other part we measure the correlation in a period of high competition ( ). The results are shown below and indicate that credit ratings and bond yield spreads are less correlated in a period of high competition rating yield rating yield rating yield rating yield The overall results of the above tests show that correlation decreases as competition increases, meaning that a high market share of Fitch implies bond yields to be less related to credit ratings. In terms of information transmission we can conclude that credit ratings are less informative about bond yields in periods of high competition. This is in line with our above results that lower quality ratings are a consequence of higher competition. EMPERICAL STUDY 36

38 Drawbacks By analyzing the results, we must take into account that when bonds come closer to maturity, the price of the instruments will increase because the remaining maturity drops and hence the risk decreases. This causes the yield to decrease as well and is in line with our results that an increase in rating levels (decrease in risk) is negatively correlated with the yield level. Another point of interest is the fact that we only used bonds with maturity of ten years and only one random bond per firm. Our results are probably not biased by the previously mentioned effect of rating shopping because there is limited evidence for rating shopping among corporate bond issuers, see Cantor and Packer (1997) and Jewell and Livingston (1999) Test 3: Predicting default Our final test concerns a measure of informativeness of credit ratings, which is examined by their ability to predict future defaults. The ability to predict default is one of the major functions of credit ratings, which makes the relevance of this test very high. On the other hand we must notice that corporate defaults are rare which decreases the explanatory power of this test. An ideal rating should be able to predict default by taking only accounting data about the rated firm into account, and go beyond what easily measurable data can predict. We regress future default in one year on market share, a dummy for investment grade ratings and an interaction of market share and the dummy for investment grade ratings. Regression model: DEF t = α + β 1 INV t + β 2 MAR t + β 3 INV t *MAR t + ε Dependent variable: DEF t = Default in year t Independent variable: INV t = Investment grade rating in year t EMPERICAL STUDY 37

39 MAR t = market share in year t INV t *MAR t = interaction effect The results of the test, which shows the prediction that a firm will receive a D level rating within 1 year are shown below. Variable Coefficient t-statistic Probability R Squared C 0,22 20,58 0,000 0,18 Investment -0,22-18,67 0,000 Market share -0,04-1,06 0,288 Investment*market share 0,03 0,87 0,385 The dummy for investment grade ratings is significant and has a negative coefficient (-0,22) meaning that firms which received an investment grade rating, are less likely to default within one year. The interaction between the market share and the dummy variable is positive (0,03), which means that when there is more competition, the difference between a default rate predicted by an investment grade level rating and a rating with a level beneath investment grade (speculative grade) 10, decreases. When we do the test again but replace the dummy variable with the rating variable, we notice that the results are comparable. Regression model: DEF t = α + β 1 RAT t + β 2 MAR t + β 3 RAT t *MAR t + ε Dependent variable: DEF t = Default in year t Independent variable: RAT t = rating level in year t MAR t = market share in year t RAT t *MAR t = interaction effect Variable Coefficient t-statistic Probability R Squared C 0,56 21,42 0,000 0,08 rating -0,03-20,25 0, Speculative grade is a rating with a level beneath BBB- for S&P and Fitch and beneath Baa3 for Moody s EMPERICAL STUDY 38

40 Market share -0,95-11,68 0,000 rating*market share 0,05 11,43 0,000 The coefficients on rating and market share are both negative (-0,03) and (-0,95) respectively and significant. The coefficient on the interaction between rating and market share is again positive (0,05) and significant. By looking at the R^2, which is only about 0,09, we notice that the predicting power of the model decreased. By analyzing the results of both models, we can conclude that higher competition does coincide with lower quality ratings. This because the interaction in both models was positive, meaning that the power to predict defaults of speculative rating levels decreases when competition increases and hence the quality of the ratings decreases. The results of the above mentioned models might be biased because of the absence of controls. In the next model we will examine the predictive power of ratings after controlling for year and firm fixed effects. Results show that the overall fit of the model is better (R^2 of model 1 increases from 0,18 to 0,22 and the R^2 of model 2 goes up as well from 0,08 to 0,14) and that the introduction of fixed effects does not change the overall conclusion about the predictiveness of ratings. Variable Coefficient t-statistic Probability R Squared C 0,22 16,75 0,000 0,22 Investment -0,01-0,22 0,827 Market share -0,22-17,52 0,000 Investment*market share 0,03 0,91 0,360 Variable Coefficient t-statistic Probability R Squared C 0,55 19,99 0,000 0,14 rating -0,90-10,42 0,000 Market share -0,02-17,09 0,000 rating*market share 0,03 8,15 0,000 EMPERICAL STUDY 39

41 We must draw attention to the fact that industries vary in the possibility to predict defaults. This could bias our results because it might lead to smaller differences in default predictions between investment and non-investment grade firms in those industries where it is harder to predict defaults. We can control for this by including industry fixed effects and an interaction of ratings with those industry dummies. Results show that by controlling for industry fixed effects, the predictive power of the model slightly increases (R^2 goes up from 0,08 to 0,09). Regression model: DEF t = α + β 1 RAT t + β 2 MAR t + β 3 RAT t *MAR t + β 3 IND22 + β 3 IND32 + β 3 IND31 + β 3 IND33+ ε Dependent variable: DEF t = Default in year t Independent variable: RAT t = rating level in year t MAR t = market share in year t RAT t *MAR t = interaction effect IND22 = if the firm is present in industry 22 = 1, if not = 0 (22: utilities) IND32 = if the firm is present in industry 32 = 1, if not = 0 (32: chemicals) IND31 = if the firm is present in industry 31 = 1, if not = 0 (31: manufacturing) IND33 = if the firm is present in industry 33 = 1, if not = 0 (33: furniture) Variable Coefficient t-statistic Probability R Squared rating -0,02-18,76 0,000 0,09 Market share -0,77-9,36 0,000 Rating*market 0,04 9,80 0,000 share I_22 0,50 19,00 0,000 I_32 0,55 20,83 0,000 I_31 0,48 15,49 0,000 I_33 0,53 20,40 0,000 EMPERICAL STUDY 40

42 The coefficients on the different industry dummies show that the default prediction does not differ significantly across the different industries (all coefficients are about 0,5), which can also be seen from the interactions between rating- and industry variables. Drawbacks A major disadvantage related to this test is that corporate defaults are rare, which has a limiting effect on the results of the test. Our sample consists of 139 firms ranking from 1996 to 2006 and 105 events of default at the one year horizon after a rating was given Interpretation of the results The overall conclusion we can draw from the three above mentioned tests is that the entry of the third rating agency, Fitch, had a significant negative impact on the quality of the ratings which is in line with the existing literature. This decrease in quality can be defined both as an increase in rating levels and a decrease in default predictability as a consequence of increased competition. These conclusions are consistent with the previously mentioned reputational capital view. According to the reputational capital view, efforts of credit raters to maintain a credible reputation would decrease when there was a drop in the potential gains of a good reputation. The decrease in those reputation building efforts would cause the rating quality to diverge as well. As also mentioned before, entrance of Fitch is more likely in situations where low ratings are published because of the effect of a third opinion. This excludes the potential danger that our findings are biased because of a reverse causality effect in which entrance is more likely in situations where high ratings are given, and hence as a consequence when market share increases, rating levels increase as well. Our findings are in line with those from Camanho et al. (2011), which showed as well that increased competition weakens the disciplining effect of reputation as opposed to a situation with fewer players. When there are more players on the rating market, credit rating agencies are not EMPERICAL STUDY 41

43 concerned enough about their reputation to prevent them from lying about the level of the ratings. As previously mentioned, competition generates two effects, named the disciplining and the market-sharing effect. The results of our tests show that the disciplining effect will be larger compared with the market-sharing effect as a consequence of increased competition which generates concerns for their reputation. The overall conclusions of our three tests may formulate a caveat to the European proposal of 15 October 2011 for a new CRA regulation in which one of the proposed measures is that of increased competition. (Proposal 2011/0361) One must however keep in mind that besides these negative consequences, competition obviously also generates benefits, by creating additional information because of the extra ratings published, which has a positive effect on the financial market. In our economic model we made use of the issuer-pays model, in which there exists a lack of independency between the credit rater and the rated entity and hence the incentives of both converge. This problem can never be solved by increased competition, as this will only cause the disciplining effect to increase in order to favor the issued entity. A possible solution can be to reintroduce the investor-pays model. Another possible solution is the government-pays model. Two alternative solutions - the platform-pays model and the payment-upon results model - and their positive and negative factors will be discussed in the next part of our thesis. 2.4 Alternative interpretation The general conclusion of the three tests was that an increased level of credit ratings due to an increased level of competition was explained by a decline in reputation building efforts. Another possible explanation for our observations, being the increase in rating levels, is that with the entrance of Fitch, the low-quality firms had an incentive to switch to the entrant firm. Fitch, doing efforts to increase its market share, tried to attract firms by giving them a favorable rating and the low-quality firms saw an EMPERICAL STUDY 42

44 opportunity for an upgrade of their ratings. As a consequence, most of the highquality and hence high-rated firms, are dedicated to the incumbent firms, which caused the general rating level of these firms to increase. To affirm this assumption, we drew a graph which reflects the evolution in the solvability of the clients of S&P, the incumbent firm. Solvability is measured as total assets over total liabilities. Our dataset consists of the NAICS firms we used in the above tests. Evolution solvability clients of S&P 4 3,8 3,6 3,4 3,2 3 Total assets/total liabilities As you can see from the line graph, there is a general increase in the solvability of the clients of S&P, meaning that either the equity has increased or the liabilities have decreased. These are the previously defined high-quality firms, which receive higher ratings and cause the general rating level of S&P to increase. This view can serve as an alternative interpretation for our results of increased rating levels. EMPERICAL STUDY 43

45 3 ALTERNATIVE MODELS 3.1 Investor pays model Before 1970, an investor pays model was applicable in the rating industry. This means that it is the investor who pays for obtaining a rating. This enterprise model could be an alternative for the ruling issuer pays model, but neither is this model free from critic and problems (Seaborn, 2010). A comparison between both models definitely reveals that two different interests are being served and that the rating results could differ according to whose preference is being granted. In the issuer pays model, the issuers pay for the ratings, so they will benefit from their offer being rated as high as possible so they have a good chance to attract investors. This way issuers could shop for the highest rating. Contrary, in the investor pays model, it are the investors who pay and they want a rating as accurate as possible. Here the opposite may occur, and ratings may be lower-than-warranted so investor can achieve a higher yield (Kudva, 2010). Although,it is too easy to impute the rating bias completely to the choice of business model. Also other factors, like experience, market power etc. play an important role. In the issuer pays model, the rating agencies often receive private information about the enterprise. The rating process implies that the rating bureaus conduct an in-depth research in the issuing company and obtain a lot of information regarding that company that is not publicly known. In contrast, the investor pays model remains focused on publicly available information because there is little or no interaction with the company that is issuing an obligation (Caprio, 2012). One of the most important differences is that the ratings in the investor pays model are no longer distributed among the public, but only available for the investor who paid for them. So a lot of small potential investors will be bypassed because they can not afford a rating. But a problem of free-riding could emerge ALTERNATIVE MODELS 44

46 (Fennell & Medvedev, 2011). Thanks to modern technology, information can be disseminated way faster and so it does not take long before ratings paid for by one investor, leak out to the wider public. This way there will be investors who never paid for a rating themselves, but do profit from the availability. The willingness to pay of investors decreases, leading to declining revenues for the rating agencies, which does not benefit the quality of information. However, this problem is being weakened by the fact that there is always a delay on the leaking of information, and some investors though are willing to pay to avoid the delay (Deb & Murphy, 2009). As already mentioned, before 1970 rating bureaus made use of the investor pays model, whereafter they transferred to the issuer pays model. Reasons for this transfer could be found in the fact that the emergence of the photo copy machine enlarged the free-riding problem, whereby rating agencies feared losing part of their revenues (White, 2010). Also more complex financial products asked for a more in-depth analysis, which causes the need for CRAs to collect more financial resources by charging the issuer (Jiang, 2012). Another reason can be found in the bankruptcy of Penn-Central Railroad on This triggered a wake-up call for bond issuers to ensure that their bonds were low risk and so they were prepared to pay the rating agencies for this (Hill, 2004). It needs to be noted that the size of the fee is not regulated. 11 It depends on the size and nature of the instrumented being rated. Another remarkable point is that a small part of the revenue of the three big rating agencies still stems from subscriber-based services (Fennell & Medvedev, 2011). Also, while most of the NRSRO s are employing an issuer-pays model, still a few other use a subscriberbased business model. In figure 1, you can find the ten NRSRO s together with the business models they use U.S. Securities and Exchange Commission, "Report on the Role and Function of Credit Rating Agencies in the Operation of the Securities Markets, As Required by Section 702(b) of the Sarbanes-Oxley Act of 2002", January 2003, U.S Securities and Exchange Commission, "Summary Report of Commission Staff s Examinations of Each Nationally ecognized Statistical R Rating Organization, As Required by Section 15E(p)(3)(C) of the Securities Exchange Act of 1934", November 2012, 22p. ALTERNATIVE MODELS 45

47 3.2 Government pays model Another possible model is the government pays model. The EU Commission called this a public utility model. 13 This model is sometimes put forward because the services of rating agencies perform a public function. The government creates the rating agency and closely monitors it. It does not have an exclusive rating responsibility, but the ratings can be used to be compared with those of the private rating agencies. Finance could come from various resources: taxes, fees paid by the issuers etc. The advantage, just like the issuer-pays model, is that the ratings would be publicly available, free of charge. But also this model has its own disadvantages. A possible conflict of interest may arise. It is not inconceivable that the government may exercise some pressure on the ratings when a company needs to be rated in which the government has a particular interest (Sweeney, 2010). It is also possible that people perceive the ratings of a public entity very reliable and of high quality. An over-reliance on those ratings can exist, even more than with the private ratings. Another possible danger is that the private rating agencies are crowed out and barriers to enter may be reinforced. This is not to the benefit of market competition. Today, no rating agency is based on the government-pays model. However, with the necessary resources, time, and alignment of all the governments, a high quality business model may be introduced (Alcubilla & Del Pozo, 2012). 3.3 Alternatives Apart from the three models described above, other suggestions are possible too. One suggestion of the Financial Services Authority is to create a platform model. 14 The platform would work as an intermediary between the issuer and the 13 European Commission, "Public Consultation on Credit Rating Agencies", November 2010, FENNELL, D. and MEDVEDEV, A., An economic analysis of credit rating agency business models and ratings accuracy, Financial Services Authority Occasional paper, November 2011, This platform model is also suggested in the following paper: J.MATHIS, J.MCANDREWS and J.C.ROCHET, Rating the raters: Are reputation concerns powerful enough to discipline rating agencies?, Journal of monetary economics 2009, ALTERNATIVE MODELS 46

48 rating agency. The issuer can not choose which CRA he wants to use, but it is the platform that selects a rating agency. The issuers pay the fees to the platform, and the platform pays them to the selected CRA. This way rating shopping is eliminated. However, a lot of issues may arise. For example, which CRA is chosen for which transaction? Will there only be one platform or multiple platforms? Does the fee has to be based on the quality of the rating or a flat fee? It is clear that there needs to be a lot of thinking and developing before this platform can be introduced. Recently such a platform has been created. End 2011 "Wikirating" went online. Wikirating is an online credit rating platform whereby everyone can contribute. Any individual can issue ratings, based on a method described by Wikirating. An experts board provides their expertise to assess the methods. All input is published and can be modified by others. A forum is available to discuss the ratings. The aim is to provide a more reliable risk assessment (Greenwood, 2012). Momentarily there are 742 registered users and Wikirating aims at reaching a whole community of active users and even at being an alternative for the private agencies. As the table, illustrated in figure 2, indicates 15, the ratings of S&P, Moody's, Fitch and Dagong tends to be higher, compared to the ratings of Wikirating. This could be an indication of the already mentioned criticism that ratings of the three big CRA's were downgraded too slowly in the years before the crisis. This because the CRA's were too lax and they wanted to please the issuers. Another solution put forward by the European Commission is the payment-uponresults model. 16 Here, the fees of the rating agencies are based on future performance. If a bond they rated performs well, the agencies should receive a higher fee. This could be realized by putting a substantial proportion of the 15 Printscreen of a few countries, from In total 198 countries are listed. Polling and SWI are 2 methods used by Wikirating for calculating the rating. The period of the polling runs from November 18th, December 30th 2011, period for the ratings of S&P, Moody's, Fitch and Dagong runs from March to November European Commission, "Public Consultation on Credit Rating Agencies", November 2010, 27. ALTERNATIVE MODELS 47

49 payment into an escrow account. When the bond effectively performs well, the money can be returned to the CRA's. Rating agencies can also borrow from that fund so they can finance their activities. It is also required to publish the schemes on which the deferred payment is based. This way transparency is created and investors can determine themselves which scheme is preferred. But the downside of this model is the question who assesses the results and in what way (Alcubilla & Del Pozo, 2012). Rating agencies might have an incentive to manipulate their methodology to reach the desired result. Section 939F of the Dodd Frank Act already required the Securities and Exchange Commission to evaluate alternative models. 17 It is clear that there are a lot of opportunities available to circumvent the conflicts of interest that are inherent in the issuer-pays model. But as described above, creating another compensation model would come with its own benefits and adversities and might introduce other conflicts of interest. It would also take a lot of effort, time and financial resources to switch to a complete different model. 17 U.S. Securities and Exchange 939F of the Dodd-Frank Wall Street Reform and Consumer Protection Act", December 2012, 83p. Commission, "Report to Congress on Assigned Credit Ratings, As Required by Section ALTERNATIVE MODELS 48

50 4 Conclusion We measured the effect of increased competition on the quality of credit ratings by analyzing the effect of the entrance of a third rating agency, Fitch. Results show that this coincides with lower overall quality, which is defined as an increase in the level of credit ratings and a decrease in informativeness of the ratings. However, we must note that this increase in level of credit ratings could not be exclusively assigned to the effect of increased competition. One must take into account that a credit rater is able to review and upgrade a credit rating when a firm is actually doing better. An alternative explanation for the increase in the level of credit ratings could be the fact that with the entrance of Fitch, the lowquality firms had an incentive to switch to the entrant to receive a higher rating and hence most of the high-quality firms are dedicated to the incumbents, which causes the level of ratings of those incumbents to increase. Our thesis is based on the research by Becker and Milbourne (2010), where they focused on North American firms, we enlarged the sample to worldwide industries. Our results were in line with those from Becker and Milbourne, which is that increased competition could not serve as a solution to increase the quality of credit ratings. We did a literature study to find alternative solutions to solve the previously mentioned conflicts of interest and came to the conclusion that there are alternatives which each have the power to solve the independency problem, but other side effects might appear. Stronger regulation concerning credit rating agencies can contribute to diminishing the conflicts of interest related to credit raters, especially when this regulation is harmonized on European level. Currently a proposal for a new European directive, proposal N 2011/ 0361, is introduced but is not implemented yet. The evolution in the regulation concerning credit rating agencies is examined in more detail in the legal thesis of Anne Goor. An interesting continuation to our research could be to investigate the behavior of the clients and to consider whether they could be categorized based on their switching behavior. A possible outcome is that the low-quality firms have an incentive to switch to the entrant to receive a higher rating, leaving the incumbents with the high-quality firms, and hence the higher ratings. CONCLUSION 49

51

52 Bibliography LEGISLATION Article 3(1) (a) Regulation (EC) 1060/2009 of the European Parliament and of the Council of credit rating agencies Regulation 2003/125/EG of the European parliament and of the board, as regards the fair presentation of investment recommendations and the disclosure of conflicts of interest Regulation (EC) N 1060/2009 of the European parliament and of the council of 16 September 2009 concerning credit rating agencies Regulation (EU) N 513/2011 an amendment to the CRA Regulation of 1 June 2011 Proposal for a regulation of the European parliament and of the council amending regulation (EC) N 1060/2009 on credit rating agencies The Technical Committee of the International Organization of Securities Commissions, Code of Conduct Fundamentals for Credit Rating Agencies, December 2004, 11p. Text books and collections ALCUBILLA, R. & DEL POZO, J. (2012). Credit Rating Agencies on the Watch List, Analsis of European Regulation. Oxford: University Press. BRIGO, D. & MERCURIO, F. (2006). Interest rate models- theorie and practice: with smile inflation and credit. Berlijn: Springer. BIBLIOGRAPHY 51

53 CAPRIO, G. (2012). Handbook of Key Global Financial Markets, Institutions, and Infrastructure. New York: Academic Press. RICHARD, S. (2002). Rating, rating agencies and the global financial system. New York: Publisher US. Contributions in journals ADELSON, M. (2006). Rating shopping, now the consequences. ASR Daily 2006, ALLEN, F., CARLETTI, E. & MARQUEZ, R. (2009).Credit market competition and Capital regulation. Oxford journals Economics and Social sciences, BANNIER, C. & HIRSCH, C. (2010).The economic function of credit rating agencies: what does the watchlist tell us?. Journal of Banking & Finance, BECKER, B. & MILBOURNE, T. (2011).How did increased competition affect credit ratings?. Journal of Financial Economics, BECKER,B. & MILBOURNE, T. (2011).Reputation and competition: evidence from the credit rating industry. Journal of Financial Economics, BOOT, A. (2008).Credit ratingbureaus en de stabiliteit van de financiële sector. Maandblad voor accountancy en Bedrijfseconomie, BOOT, A. (2006).De toegevoegde waarde van credit ratings. the review of financiel studies, BRUYNINCKX, T. (2008).Rating agencies: inhoud, regelementering en aansprakelijkheid. Jura Falconis, BIBLIOGRAPHY 52

54 CANTOR, R. & PACKER, F. (1994).The credit rating industry.federal Reserve Bank of New York Journal Quarterly review, DE HAAN, J. & AMTENBRINK, F. (2009).Regulating Credit Rating Agencies in the European Union: A Critical First Assessment of Regulation 1060/2009 on Credit Rating Agencies. Common market law review, DERYN, D. (2009).Credit Rating Agencies and the Credit Crisis: How the Issuer Pays Conflict Contributed and What Regulators Might Do about It. Columbia Business Law Review, DONKERS, K. (2010).Credit rating agencies: informative-asymmetrie en civielrechtelijke aansprakelijkheid.maandblad voor accountancy en bedrijfseconomie, FRIED, J. & HOWITT, P. (1980).Credit rationing and implicit contract theory.journal of money, credit and banking, GERRITS, A. (2011).Amerikanen uit hun droom gewekt.de Telegraaf, GREENWOOD, J. (2012).Move over S&P, Moody s :make way for Wikirating. Financial Post, Geraadpleegd via: HUNT, J.P. (2009).Credit Rating Agencies and the 'Worldwide Credit Crisis': The Limits of Reputation, the Insufficiency of Reform, and a Proposal for Improvement. Columbia Business Law Review, JIANG, J. et al. (2012).Does it matter who pays for bond ratings? Historical evidence. Journal of Financial Economics, Geraadpleegd via JORION, P. (2005).Informational effects of regulation FD: Evidence from rating agencies.journal of financial economics, BIBLIOGRAPHY 53

55 KUDVA, R (2010).Issuer-pays model ensures ratings are available to the entire market. The economic times,6-8. LEENAARS, H. (2003).Risicomanagement van banken. Maandblad voor accountancy en bedrijfseconomie, MATHIS, J., MCANDREWS, J. & ROCHET, J.C. (2009). Rating the raters: Are reputation concerns powerful enough to discipline rating agencies?.journal of monetary economics, PARTNOY, F. (1999).The Siskel and Ebert of Financial Markets? Two Thumbs Down for the Credit Rating Agencies.Washington University Law Quarterly 77, PACKER, F. & CANTOR, R. (1995).The credit rating industry. The journal of fixed income, SOMMER, A.A. (1996).IOSCO, Its mission and achievement. Northwestern University School of Law Northwestern Journal of International Law & Business, SKRETA,V. & VELDKAMP, L. (2009).Ratings shopping and asset complexity: A theory of assets inflation. Journal of monetary economics, TANG, T. (2008).Information asymmetry and firms credit market access: Evidence from Moody s credit rating format refinement. Journal of financial economics, WHITE, L.J. (2010).Markets: The credit rating agencies. Journal of economic perspectives, BIBLIOGRAPHY 54

56 WHITE, L.J. (2009).The Credit-rating Agencies and the Subprime Debacle. Critical Review 21 no. 2-3, WHITE, L.J. (2010). The Credit Rating Agencies: How Did We Get Here? Where Should We Go?. Journal of Economic Perspectives, WOLFSON, J. & CRAWFORD, J. (2010).Lessons From The Current Financial Crisis: Should Credit Rating Agencies Be Re-Structured?. Journal of Business & Economics Research, BAR-ISAAC, H. & SHAPIRO, J. (2010). Ratings quality over the business cycle (working paper NYU and University of Oxford), 30p. BECKER, B. & MILBOURNE, T.(2010).How did increased competition affect credit ratings? (Working paper Harvard Business School), 49p. CAMANHO, N., DEB, P. & LIU, Z. (2012).Credit rating and competition (Financial Markets Group London School of Economics and Political Science), 44p. COHEN, A. (2011).Rating shopping in the CMBS market (Federal Reserve board paper), 40p. DEB, G. & MURPHY, G. (2009).Credit Rating Agencies: An Alternative Model (London School of Economics and Financial Markets Group and Bank of England), 12p. DE HAAN, J.& AMTENBRINK, F. (2011).Credit rating agencies (De Nederlandsche Bank Working paper), 42p. European Commission. (2010).Public Consultation on Credit Rating Agencies (Working paper). FENNELL, D & MEDVEDEV, A. (2011). An economic analysis of credit rating agency business models and ratings accuracy (Financial Services Authority Occasional paper ), 24p. FROST, C.A. (2006). Credit rating agencies in capital markets: A review of research evidence on selected criticisms on the agencies (working paper University of North Texas) 45p. HAMILTON, D. (2004). Rating transitions and defaults conditional on watchlist, outlook and rating history (Working paper Moody s analytics New York) 24p. BIBLIOGRAPHY 55

57 HILL, C. (2004).Regulating the rating agencies (American law and economics association annual meetings), 54p. HUANG, Z.,CHEN, H., HSU, C., CHEN, W. &WU, S. (2004).Credit rating analysis with support vector machines and neural networks: a market comparative study (working paper from the university of Arizona), 558p. MATHIS, J. (2009).Rating the raters: Are reputation concerns powerful enough to discipline rating agencies? (Working paper T oulouse school of economics ), 12p. PARTNOY, F. (2006).How and why credit rating agencies are not like other gatekeepers (working paper University of San Diego School of Law), 45p. PRAGYAN, D. (2012). Essays on the impact of competition on financial intermediaries (working paper of the finance department of the London school of economics for the degree of doctor philosophy), 135p. SANGIORGY, F., SOKOBIN, J.& SPATT, C. (2009).Credit rating shopping, selection and the equilibrium structure of ratings (working paper Stockholm school of economics), 42p. SCHULTZ-LARSEN & T., HASLING KYED, J. (2012).The market for credit ratings: Competition and misalignment of interests (working paper department of economics, University of Copenhagen), 36p. SEABORN, P. (2010).Business models and incentives in rating markets: how 'who pays' matters (Working paper Rotman School of Management),18p. SWEENEY, E. (2010).An Examination of How Investor Needs are Served by Various Ratings Business Models: Ensuring Analytical Independence and Freedom from Conflicts of Interest at Credit Rating Firms (Standard's & Poors White paper, New York),8p. U.S. Securities and Exchange Commission (2003).Report on the Role and Function of Credit Rating Agencies in the Operation of the Securities Markets, As Required by Section 702(b) of the Sarbanes-Oxley Act of 2002 (working paper ), 45p. Commission (2012). Report to Congress on Assigned Credit Ratings, As U.S. Securities and Exchange Required by Section 939F of the Dodd-Frank Wall Street Reform and Consumer Protection Act, 83p. U.S. Securities and Exchange Commission, Speech by SEC Commissioner: Remarks at the Commission Open Meeting, by Commissioner Kathleen L. Casey, 3 December 2008, BIBLIOGRAPHY 56

58 U.S Securities and Exchange Commission, Speech by SEC commissioner: Remarks to the institute of international bankers by SEC commissioner Paul S. Atkins, 3 maart 2008, U.S Securities and Exchange Commission, Summary Report of Commission Staff s Examinations of Each Nationally ecognized Statistical Rating Organization, As Required by Section 15E(p)(3)(C) of the Securities Exchange Act of 1934, R November 2012, 22p. Sites [27/02/2013] [12/11/2012] [08/03/2013] [08/03/2013] [12/11/2012] [12/11/2012] [08/03/2013] [09/03/2013] BIBLIOGRAPHY 57

59 Attachments Table 1: Fitch market share per NAICS industry in periods and Fitch market NAICS Industry name share Agriculture, Forestry, Fishing and Huntin 0, , Mining, Quarrying, and Oil and Gas Extraction 0, , Utilities 0, , Construction 0, , Manufacturing: Food, Textile, Apparel 0, , Manufacturing: Wood, Paper, Printing, Petroleum, 32 Chemicals, plastics 0, , Manufacturing: Metals, Machinery, Computers, Electrical, Furniture 0, , Wholesale Trade 0, , Retail Trade: Motor Vehicles, Furniture, Electronics, Food, 44 Gas 0, , Retail Trade: Sporting goods, Books, Florists, Office Supplies, Mail-Order, Vending 0, , Transportation and Warehousing: Air Transport, Water Transport, Trucks, Pipelines 0, , Transportation and Warehousing: Messengers, Storage 0, , Information 0, , Finance and Insurance 0, , Real Estate and Rental and Leasing 0, , Professional, Scientific, and Technical Services 0, , Management of Companies and Enterprises 0, , Administrative and Support and Waste Management and 56 Remediation Services 0, , Educational Services 0, , Health Care and Social Assistance 0, , Arts, Entertainment, and Recreation 0, , Accommodation and Food Services 0, , Other Services (except Public Administration) 0, , Public Administration 0, , ATTACHEMENTS 58

60 Table 2: Fitch market share per MSCI World industry in periods and Industry Fitch marketshare Basic Materials 0, , Communication 0, , Consumer, cyclical 0,0812 0, Consumer, non-cyclical 0, , Diversified 0, , Energy 0,0695 0,1549 Financial 0,2067 0, Industrial 0,8132 0, Technologie 0, , Utilities 0, , Government 0, , Figure 1 ATTACHEMENTS 59

61 Figure 2 from ATTACHEMENTS 60

62

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