This is a very instrumental section of the thesis because it provides an insight into the data used to make the associated deductions as well as the research methodologies employed to complete the research. The section is split into two subsections with the first subsections handling the data used and the second providing the highlights of the methodology used.
The research made use different categories of data as this was the only way to ensure that the domains within which the study was to be conducted was spread enough to give varieties of options for argument. The information regarding the parameters required was obtained from the WRDS and the analysis conducted in accordance with the trends observed. Since the analysis was going to require trends on both sides of the Cartesian orientation, it was necessary that we collected two types of information with one set of data portraying the downgrade trends and the other portraying the upgrade trends. Different upgrades and downgrade data were collected from several sets of companies and the corresponding trends used to perform the required analysis. Large numbers of organizations were selected for analysis with the aim of ensuring that the findings were spread enough to provide an actual impression of the trends prevailing in the actual scenario. There was the absolute need to ensure that an extensive analysis is conducted with the aim of revealing the firms capabilities to manage their debt obligations.
In order to achieve the above objective, the data selection was restricted to within a long period of time (10 years for this case) as this was approximated to be just enough to provide the trend which was necessary in making such deductions. The information collected about the organizations selected for the analysis was designed in such a way that they cover the period between the years 2005 and 2015. The data containing the information that was collected from the selected firms within the required period of time was in accordance with the ones appearing in the appendix section. The need to establish an appropriate benchmarking was achieved through the use of Europe MSCI indices, a step that made it possible achieve the accurate estimation of the normal returns of each of the selected firms during the period of research (2005 and 2015). This tool became useful because it is popularly known as the adjusted weighed index which has special design enabling it to become instrumental in the process of estimating the performance the developed European markets.
As it is known to the world of economics, MSCI has been helpful in the measurement of market performance in countries such as Denmark, Austria, Netherlands, Spain and other 12 European countries bringing the total number to 16 countries whose market performance are measured by this index. The index served as a benchmark due to the fact that the information it contains is pretty much the same as that contained in the dataset. According to the research arrangements, the mean market capitalization was used as the main criteria of classifying the firms in such a way that the firms whose market capitalization was found to exceed the mean market capitalization were categorized as the large firms. On the other hand, the firms whose market capitalizations were found to be lower than the mean market capitalization were grouped under the small firms category. All the information regarding the capitalization of each of firms used was of course obtained from the WRDS database before being manipulated to provide the required information. Just like the other sets of information, the multivariate regression for information regarding the market capitalization, date ratio and the financial variables were also obtained from the WRDS database.
The main sample used for this research consisted of 877 changes which were obtained from observations spread across a ten-year period. The sampling was very selective because of the need to ensure that the samples collected for analysis were characterized by historical stock prices. The stock prices were the main subjects of research and formed the principle variables hence the need to select only the samples which contained historical stock prices. The initial credit change rating was set in such a way that it was made up of the high number of changes of one firm within a given week. The absolute need for the process of market capitalization also made it necessary to ensure that the sampling revolved around activities which entailed market capitalization and were within the event dates.
As mentioned earlier, the data was categorized into downgrades and the upgrades with each of the categories having its responsibility in the research. The downgrade information was instrumental in the determination of the relationship (if any) between the stock returns and the other factors such as the size of the selected, leverage, the grade barrier for investment, initial rating, credit crisis and the ration between the assets and the debts of the firms under consideration. The downgrades were then categorized into two for the purpose of testing the impacts with respect to the size of the firms selected. The above brought the need to divide the downgrades into the high market capitalization and the low market capitalization firms.
The firms whose market capitalizations were found to be low were classified as the small firms while those with high market capitalization were classified as the large firms. In order to factor in the impact of the issuer of the sector, a further classification emerged in which the firms were grouped as either financial organizations or non-financial organizations with each category being characterized by its own category of downgrades. The choice to split the observations with respect to the relationship financial services offered by the selected firms was very necessary as it provided an insight into the rationale behind the differences in transparency which characterize the variations in the rules governing the firms at different lines of production. This is a very important decision because it incorporates the aspect of variation into the experiment thereby making the deductions arrived at more open minded and broad with respect to the real life implications.
In order to simplify the research and to ensure that closer tabs were kept on the proceedings, the research decided to employ the three step event study technique which helped in achieving the research schedule and providing the required results. The fundamental steps employed here include the event identification as well, as the event timing, specification of the benchmark model for the purpose of inspecting the behavior of the firms stock returns which is one of the most fundamental variables under observation by this research. The final step involved the calculation and the corresponding analysis of the abnormal returns which are observed during the dates surrounding the research dates. Since the benchmark models established was to provide accounts for the variations that occurred on the b factor during the calculation of abnormal returns, it is safe to say that the term abnormal refers to the remnants of the market model. The returns that occur above the expected returns or simply the returns that are above the normally anticipated levels are said to be abnormal. The instantaneous return during a given point in time with respect to the event is given in accordance with the following expression.
Rit = a + biRMt + itFrom the above expression, the terms Rit, RMt, it, bi and a represent the instantaneous returns, the rates of returns of the market, a portion of the security returns that come from specific events of the firms associated, the degree of sensitivity of the returns of the market and the hypothetical average rates of returns that would be anticipated during a hypothetical period assumed to have zero returns in the market. The calculation of the stock prices as well as the computation of the index was achieved through the use of the logarithmic function below. Note that the factors Rit, i, t and Pt are the returns to the normal expectation, the security under consideration, the instantaneous time during which the study was conducted and the closing price on the day of investigation respectively. The function Pt-1 on the other hand is taken to represent the closing price during the day before the day under investigation.
Rit = ln PtPt-1Just as mentioned in the previous sections of this \chapter, the term abnormal return refers to the remnants of the market model. The link or relationship that occurs between the model and the function beta, it is valid to refer to the abnormal returns as the errors that characterize the prediction of the market model. This new development gives rise to the emergence of a new expression given as follows. The terms a and brepresent the estimates of the OLS for the regression coefficients.
NRit = ai + biRmt
The actual values for both the functions a and b are given in accordance with the following expression:
ai = R- biRm
bi = cov (RI,RM)var (Rm)The period during which the experiment was to be conducted is usually a very instrumental factor in researches such as this one and there was the absolute need to ensure that the best duration is arrived at. The research decided to select the most commonly used duration with respect to many previous researchers which is 250 days. It is necessary to have an elongated period before the date of study although the research made the observation that the date of investigation is not inclusive into this particular list. 250 days before the exact date of the research was set for the estimation of the market model. Out of this period, 230 days before the event date were used as the trading days for estimation window. The estimation window period was restricted to within 30 days to the exact event date.
Then period covering 29 days prior to the exact date of the event and 30 days into the event period was then assigned for the event window duration. A comparison was then conducted on the several event windows while there was also a calculation of the abnormal returns characterizing each of the days constituting the event windows under consideration. The implication of this is the fact that the research involved the monitoring of the trading activities occurring during the entire period constituting the event window. The observation collected daily data which was instrumental in the calculation of the abnormal returns which characterized the period. For the event period which had 29 days prior to the event and 30 days after the event date, there was a total of 59 calculations representing all the abnormal returns during this period. His logistics would however be relevant only under the assumption that each of the days making up the event window was characterized by at least an abnormal return.
The abnormal returns are the excesses that occur above the expected benchmark returns implying that the mathematical definition of the abnormal returns is in such a way that they are the actual experienced returns less the normally expected or benchmark returns. This statement can be expressed using simple equations as illustrated below. The factors ARit, Rit and NRit are the abnormal returns, the actual returns obtained and the benchmark returns respectively.
ARit = Rit NRit
The separate analysis of the returns information of each of the selected firms could have been a god idea of conducting an investigation into the changes that occurred in the stock prices during the study. The idea was however avoided because there were fears that it could not provide enough satisfactory information to help in achieving the objectives of the entire rese...
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