Financial Research Seminar

14 Pages   |   3,516 Words
Table of Contents
1.      Introduction. 2
1.1 Problem Statement. 2
1.2 Objectives. 2
2.      Literature Review.. 2
3.      Methodology. 5
3.1 Data and Sources. 5
3.2 Model and Technique. 5
4.      Results and Discussion. 6
4.1 Pooled OLS. 6
4.2 Fixed Effects (FE). 7
4.3 Random Effects (RE). 8
4.4 Comparing FE and RE. 9
5.      Concluding Remarks. 10
References. 12
Appendix A.. 13
 

1.Introduction

Sovereign ratings play a vital role in the financial condition and reputation of a country. There are three major credit agencies e.g. Moody’s, Fitch and S&P. These companies rate the countries, individual institutions and economic agents from highest quality to default. Various macroeconomic and political variables also play a vibrant role in determining these credit ratings (Afonso, et al., 2011). These ratings do not only portray the financial condition of the country but can also be used to assess the efficiency of the government of the country, determine interest rates, borrowing rates of the banks and can help assess the investment risks (Afonso, et al., 2011). The essay is divided into five sections. The first section includes the problem statement and the objectives. The second section includes a brief literature review. The third section includes the methodology, techniques, and details of variables. The fourth section discusses the results, and the last section includes concluding remarks. The appendix at the end of the essay includes necessary detail about the data, sources and also reports the summary stats.

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1.1 Problem Statement

The main focus of this study is to see how the rating agencies e.g. Moody’s, Fitch Solutions, etc. play a role in the financial markets and various factors playing in the market. For the analysis, Fitch Solutions database has been used.

1.2 Objectives

The objectives of the study are as follows:
  • To define the role of rating agencies and the credit ratings in the financial market.
  • To study the credit ratings and various economic factors of five countries over the past ten years.
  • To form a model of the variables and analyze how the credit is affected by these determinants.

1.Literature Review

Credit ratings of governments and financial agents are very significant in assessing the performance of the financial bodies in a country. These ratings are used to see where the institution or the agent stand in the financial market and how the future actions would be planned. These rates are also important in determining the default rate of the agent, and that’s why, the role of these rating agencies is very vibrant in any body that operates in the financial market (Alsakka & Gwilym, 2012). (Packer, 2000), who studied the role of these financial rating agencies in Japan reported that these ratings are essential in determining the credit spread in the country and can also repor (Alsakka, et al., 2014)t the corporate risk. If the ratings of the Japanese companies are analyzed combined with the ratings of the foreign companies, they can potentially help in predicting the credit risk for the future in a more precise way.
(Afonso, et al., 2011), studied the factors that are potentially responsible for determining these credit ratings of any company. They used the credit ratings of three major credit companies including Moody’s, S&P and Fitch from 1995-2005. The main edge of this paper was that they used two methods for studying the credit rating determinants that also helped in dividing the long run and short term determinants. For the analysis, various macroeconomic and fiscal variables were used. The results proved that major macroeconomic factors like GDP and its growth, public debt, and budget balance can affect these ratings in short-term and other actors e.g. foreign reserves and effectiveness of the government has the potential to affect the ratings for a longer term. These results are very significant for larger cooperations and governments that need to put a check and balance on their every action and foresight the effects way before they actually occur (Alsakka, et al., 2014).
(Cantor & Packer, 2006), studied the credit ratings provided by Moody’s and S&P and analyzed the effects of these ratings on the financial activities and other factors in the United States. The study was focused to see how fast these rating announcements affect the market actions and if they affect the actions at all. To check the sensitivity of the market and economic factors to these credit ratings, they used various economic, political and social factors including GDP per capita, growth rate of GDP, inflation rate, fiscal balance, public debt, and industrialization level and default history of the country. The study concluded that the economic and political factors mentioned earlier significantly affect these ratings. On the other side of the coin, these ratings can potentially summarize and represent the economic, social, fiscal and political condition of a country. That’s why, these market credit ratings hold a significant role in any financial domain.
(Mellios & Paget-blanc, 2006), on the other hand, used the ratings of all three major financial institutions including Moody’s, S&P, Fitch. The main objective of the study was also the same; analyze the effects of various economic factors on the determination of these credit ratings using the ordered logistic model. The study proved the factors that are mainly affecting these credit ratings include the GDP per capita, GDP growth, exchange rate, inflation rate and the default history of the country or institution under analysis. A unique point of the study was that it also incorporated the economic development, good governance and political factors in the study and for that, they used the proxy of corruption. 
While doing trade with one another and borrowing money, two countries always pay different amount of interest rates. The practice still continues even if the countries involved in borrowing and lending share the similar economies and same currency. It is true because even if the two countries are economically similar, they both have different financial indicators and risk factors that make both countries have different probabilities of default. This is where credit ratings play their part. Countries use these credit ratings for judging the financial performance of the country and determine the interest rate of the country on that basis; lower credit rating means higher risk and thus higher interest rate and risk premium and vice versa (Arefjves & Braslins, 2013). These credit ratings are becoming very important in the emerging economies and are somehow playing the part of the determining bodies of the financial performance and future prospects of a company, financial agent or even a country. This need is becoming even more important because of the globalization and fierce financial competition on a global level as they lead to financial diversification of the institutions and open up doors for new ideas and predictions (Alaskka & Gwilym, 2010).
(Rowland, 2004), used the panel data to study the effects of economic and political and economic factors on the credit ratings provided by the financial companies. They used 16 countries as cross sections for the analysis, and the study was presented as a follow-up for a previously conducted study on the same focus area. The techniques used was the simple OLS, and seven variables were used as explanatory variables to study the effects. The study proved that the factors play a significant role in determining the credit ratings and any government, and financial company should play keen attention to these factors because of their incredibly vibrant role in the financial world.

2.Methodology

3.1 Data and Sources

For the analysis, the dependent variable is the credit rating that was collected from the sovereign ratings of Fitch solutions only. As these ratings are not in numerical form and therefore could not be used in the analysis, numerical values are assigned to these ratings as AAA+ being 16 and CCC being the worst condition and numerical value of 1.
Three variables are used as the independent variables namely GDP per capita, Inflation, and the budget surplus. For these explanatory variables, GDP per capita at current US $, Inflation rate, GDP deflator annual percentage, Cash surplus/deficit as percentage of GDP are used as proxies respectively. All the data for explanatory variables is collected from the World Bank. Table A1 in the appendix reports the data description and there sources in the form of a summarized table. The time span under analysis is from 2003-2012 because it was the latest data available. The countries under analysis are Italy, Portugal, Greece, Romania, and Belgium. These countries were chosen on the basis of random choice and data availability.

3.2 Model and Technique

Using the variables in the panel data analysis, the multi linear regression model would look like the following:
Credit Ratingi,t = αi,t + β1iGDP,t + β2iInflationi,t + β3iSurplusi,t + uit
Where i represents the cross sections and t represents the time. α is the intercept or constant. β’s are the coefficients or structural parameters and u is the error term. 
The panel data is a useful way to deal with this kind of data as it does not only help analyze the data in broader and generalize it more easily but can also be used to judge and anticipate the further actions and decision of the institution, company, agents or government under analysis. Main techniques applied are the pooled OLD, random effects and fixed effects. Pooled OLS, although an essential and ‘initial’ step in the analysis, is not as valid as the FE and RE because it cannot capture the data as panel and the results are not in the best form in terms of robustness. The random effects are preferred upon the fixed effects, because they can easily capture the normality and are better in assessing this type of data as the data of credits does not vary to a major extent every year (Afonso, et al., 2011).

3.Results and Discussion

After collecting the data from the Fitch solutions and the World Bank, it was put and organized in the excel sheet the way panel data is organized. STATA 11 was used for the analysis. As STATA cannot read the string variables, the name of countries needed to be given numerical values as codes that can be used as a proxy for the countries. So, the coding was done in the excel sheet. Moreover, the Fitch ratings were also in string form and could not be read by STATA and could neither be analyzed in that form. That’s why the credit ratings were also given numerical values staring from the highest as 16 for AAA+ and the lowest being 1 for CCC. After providing codes for the countries and the credit ratings, the data of explanatory variables was analyzed for any missing values or string entries but no such issue was observed, and the data was ready for analysis.

4.1 Pooled OLS

After putting the data into STATA 11, it could not be analyzed right away because of its nature; the panel data. STATA needs to be told that the nature of data is a panel, not time series because the software considers the data as being the time series by default. So, after outing the command of panel data, STATA considered the data to be panel and showed that it is a balanced panel with five cross sections and observations starting from the years 2003 till 2012. To start the analysis rationally, the first step was to apply the simple OLS on the dataset. Below is the direct output results of the pooled OLS in STATA.
The results of OLS presents the coefficient values of GDP inflation and cash surplus. It shows that GDP effects the credit ratings in a positive way as the value of the coefficient is 0.0001446 and is also significant at 1% level of significance. Inflation, on the other hand, affects the ratings in a negative way. It also makes sense because higher inflation can negatively affect the economic, financial and political factors of the countries and is also supported by theory (Gartner, et al., 2011). While cash surplus coefficient is 0.4497199, that is also significant at 1% level of significance. R2 is 0.5129 that shows that the model is moderately fit.
Figure 1: Pooled OLS

4.2 Fixed Effects (FE)

OLS is not sufficient to provide the results of a panel data analysis and might lead to misleading results. That is why the next reasonable step is to test the model by fixed and random effects respectively.
Figure 2: Fixed Effects

Figure 2 shows the results of fixed effects in STATA. These results are to some extent different than the results of the pooled OLS. GDP has the same positive effect on credit ratings, but the coefficient is not significant at any level of significance. Inflation, on the other hand, does not have a negative effect on the credit ratings as explained by the OLS results. Inflation coefficient is 0.0610216. The coefficient of cash surplus is 0.3669969 that is significant at 5% level of significance.
The R2 value of this model is 0.4347 that also shows that the model is moderately fit. The benefits of the fixed effects is that it has the potential to minimize the bias normally generated by the pooled OLS and can also study the individual effects more precisely than the pooled OLS method (Clarke, et al., 2010).

4.3 Random Effects (RE)

After running the fixed effects model, the next step is to sun the random effects model and see if the results of random effects vary from the fixed effects and have some different implication as well. The figure below shows the results of random effects.
Figure 3: Random Effects

The coefficients of random effects have the same signs as the pooled OLS and not like fixed effects. GDP has a positive effect on credit rating, and the coefficient has the value of 0.0001446 that is significant at 5% level of significance. Inflation, on the other hand, seems to have a negative effect on these ratings with -0.0466057. Cash surplus coefficient is 0.4497199 that is significant at 1% level of significance.
The value of R2 is 0.5129 that shows that the model is a moderately fit one. It is important to note that the random effect model shows almost the same results as the pooled OLS model, and that’s why can be used as a justification for each other. But a final decision can not be made without a proper comparison of the three mentioned above models and it needs to be scrutinized which model is its best form and can be used with validity to support the theory. From the first look, random effects seem more rational than the fixed effects because the signs of the coefficients in the random effects model are more supported by the theory than the coefficients of the fixed effects. 

4.4 Comparing FE and RE

After having all three main models computed and analyzed, the next step is to see which one of them is the most efficient one. And the main choice here is between the random and fixed effects models. It is also important because both models have different values of the coefficients, and the selected model would play a crucial in deciding the role of the factors in credit ratings and the role of the institutions. The figure below shows the results of Hausman test that is used to decide between fixed and random effects.
Figure 4: Hausman

The decision rule for the Hausman test is that if the value of the Hausman test is above 0.05, it is preferable to go with the random effects and if the value is below or at least equal to 0.05, fixed effects is a reasonable choice. As the value of the Hausman test is 0.1692 that is above 0.05, it is rational to go with the random effects. The table below summarizes the results of all three models and also reports the value of the Hausman test.
Table 1: Comparing OLS, FE and RE
Variables Pooled OLS FE RE  
GDP pc   0.0001446** 0.0001005 0.0001446***
Inflation   -0.04660057 0.0610216 -0.0466057
Surplus   0.4497199*** 0.3669969** 0.4497199***
Constant   9.310568*** 9.686628*** 9.310568***
Countries   5 5 5
Observations   50 50 50
R2 Overall   0.5129 0.4347 0.5129
Hausman   0.1692
*, ** and *** indicate significance at 10%, 5% and 1% level of significance respectively.
Hausman H0: RE is better. ( Decision rule: If P-value>0.05, go with RE)
 
                 
 
 

4.Concluding Remarks

Sovereign ratings or credit ratings are used by various financial agents as a tool to assess the economic, social, financial and political condition of a country. There are three major financial agencies that provide valid and authentic credit ratings for governments, financial agents and bodies namely Moody’s, S&P and Fitch. Most of the times, all three ratings are used as a measurement tool for the financial credibility of a country.
There are various factors these financial agencies keep in mind while drawing the credit ratings. According to critics and financial analysts, there are several factors that play a major role in determining these ratings. These factors include a wide variety of variables ranging from the field of economic, politics, finance and social capacity. Major variables include GDP per capita, the growth rate of GDP, inflation rate, level of transparency such as the incidence of corruption, external debt, history of default of the country or body, budget balance or the surplus or deficit ratios and many other factors. These ratings include all these economic, financial, political and social factors so that the ratings do not just cover the financial condition only and also envelops the rick factors in the country.
According to analysts, these credit ratings do not only help do judge where the company stands in terms of financial condition but they also help in predicting the default rate of the country or body and can also be used to decide the future credit stream and action plan. In this way, these ratings and the financial agencies providing these ratings play a vital role in the financial world.
The study was focused to study the effects and influence of these financial ratings and the sensitivity of the economic factors in terms of credit ratings. For the analysis, five countries, Italy, Belgium, Romania, Greece, and Portugal from 2003-2012. Credit rating is the dependent variable while GDP per capita, inflation and cash surplus are the independent variables. The analysis showed that GDP and cash surplus affect the credit ratings in a positive way while inflation affects the ratings negatively. 
 
 
 
 
 
 
 

References

Afonso, A., Furceri, D. & Gomes, P., 2011. Sovereign Credit Ratings and Financial Market Linkages: Application to European Data. Macroprudential Research Network: Working Paper Series No 1347, June, Issue 1347, p. 45.

Afonso, A., Gomes, P. & Rother, P., 2011. Short and Long Run Determinants of Sovereign Debt Credit Ratings. International Journal of Finance and Economics, January, 16(1), pp. 1-15.
Alaskka, R. & Gwilym, O. a., 2010. Sovereign Ratings and Migration: Emerging Markets. Bangor Business School Working Paper, pp. 1-33.
Alsakka, R. & Gwilym, O. a., 2012. Rating Agencies' Credit Signals: An Analysis of Sovereign Watch and Outlook. International Review of Financial Analysis, 21(1), pp. 45-55.
Alsakka, R., Gwilym, O. a. & Vu, T. N., 2014. The Sovereign-bank Rating Channel and Rating Agencies' Downgrades during the European Debt Crisis. Journal of International Money and Finance, 49(3), pp. 235-257.
Arefjves, I. & Braslins, G., 2013. Determinants of Sovereign Credit Ratings - Example of Latvia. New Challanges of Economic and Business Development, Volume 4, pp. 1-12.
Cantor , R. & Packer, F., 2006. Determinants and Impact of Sovereign Credit Ratings. Economic Policy Review, 2(7), pp. 1-8.
Clarke, P., Crawford, C., Steele, F. & Vignoles, A., 2010. The Choice Betwen Fixed and Random Effects Models: Some Considerations for Educational research. IZA Discussion Paper No. 5287, p. 36.
Gartner, M., Griesbach, B. & Jung, F., 2011. PIGS or Lambs? The European Sovereign Debt Crisis and the Role of Rating Agencies. International Atlantic Economic Society, March.p. 22.
Mellios, C. & Paget-blanc, E., 2006. Which Factors Determine Sovereign Credit Ratings?. The European Journal of Finance, 12(4), pp. 361-377.
Packer, F., 2000. Credit Ratings and the Japanese Corporate Bond Market. IMES Discussion Paper Series, 9(8), pp. 139-158.
Rowland, P., 2004. Determinants of Spread, Credit ratings and Creditworthiness for Emerging Market Sovereign Debt: A Follow-up Study Using Pooled Data Analysis. Borradores de Economia: Working Paper no 296, Issue 296, pp. 1-37.
 
 
 
 

Appendix A

Table A1: Variable Description and Data Sources
Variable Proxy Source
Sovereign rating Fitch sovereign ratings Fitch Solutions
GDP GDP per capita (Current US$) The World Bank
Inflation Inflation, GDP deflator (annual %) The World Bank
Budget Surplus Cash surplus/ deficit (% of GDP) The World Bank
     
 
 
Table A2: Summary Stats
  Years Credit rating (Numerical) GDP Inflation Surplus
Mean   2007.5 10.94 25907.23 3.786000 -4.312000
Median   2007.5 12 25389 2.400000 -3.450000
Maximum   2012 15 48561.4 23.50000 0.200000
Minimum   2003 1 2756.3 -0.40000 -15.20000
Std. Dev.   2.901442 3.672346 12701.35 4.516998 3.194360
Skewness   0.00000 -0.913282 -0.144670 2.582100 -1.082479
Kurtosis   1.775758 3.106619 2.110299 9.829696 4.210739
Jarque-Bera   3.122436 6.974389 1.758677 152.7369 12.81860
Probability   0.209880 0.030587 0.415057 0.000000 0.001646
Sum   100375 547 1295361 189.3000 -215.6000
Obs.   50 50 50 50 50
               
 

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