Comparative analysis of stocks and bonds

Abstract : This research  concentrates on the comparison of  bonds which are called liabilities and stocks which are also called equity using analysis of variance (ANOVA) tool. This tool is cam be effectively used to measure and examine if there is a significant relationship between stocks and bonds. This paper handpicked 20 companies under iTraxx Europe Sector informational data.

The descriptive method of research  was used for it employs randomize published data for this paper.The researcher proposed here that the common and traditional useful credit model such as default discrimination and relative value analysis. We were deeply in the study of the connection of bonds and stocks in credit risk to determine the defaulting and un-defaulting company. The researcher studied the debt and equity through commutative distribution analysis, distribution of default probability, multiple regression and analysis of variance (ANOVA).

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I found out that the total stocks have a complete impact to the credit risk arena. And there is no need for the investors or companies to determine what model or approach will help them to identify the level of credit risk.I have to advise the investors to get he financial statement of the creditor to identify the credit risk. This is because we found out that if the total stocks are greater than the total bonds there is no credit risk.

The objective of this paper is to determine the significant relationship between bonds  and stocks from the selected 20 companies under itraxx Europe informational data I arranged this paper as follows: Section 1. Introduction, Section 2. Full Model and Reduced form approach, Section 3. Related Literature, Section 4.

Parallel Framework, Section 5. Data and Empirical Methodology, Section 6. Results, Section 7. Conclusion , and Section 8.

Appendices I. IntroductionThe aim of this paper is to determine the significant relationship between bonds and stocks in the selected twenty companies under iTraxx Europe sector. We believed that basic knowledge in accounting is the tool in determining the differences of the two variables. Consequently, the building interest in calculating the comparison of stocks and bonds was by no means then limited to the highly industrialized countries like the United States, Europe, Thailand, Australia to name a few.

  On the other hand, it is now mushrooming all over the world at a rate that surprises even the third world countries.  People have developed great enthusiasm in these tools to protect their interest in the form of investments.iTraxx is the brand name for the family of Credit Default Swap Index products covering regions of Europe, Japan and non- Japan Asia. They form a large sector of the overall credit derivative market.

Their indices are constructed on a set of rules with the overriding criterion being that of liquidity of the underlying Credit Default Swaps, (CDS). The I Traxx suite of indices are owned, managed, compiled and published by International Index Company (IIC), who also licensed market makers.Credit Risk is why businesses are always on the look-out, like the recent bankruptcies of several airlines, Delphi car parts, and the talk of whether GM or Ford will follow at risk, commercial banks, and institutions holding portfolios of corporate bonds.II.

Related LiteratureThe following ideas that were explicit in this part are the related topics concerning on bonds (long term Liabilities) and Stocks.Empirical research has often shown that the degree of mean reversion is not enough to generate a fall in the investment insurance costs. And, the article Why business fail: and strategies for a successful turnaround (Gardner,2006) clarifies that  a turnaround expert can make a difference  as he or she continues helping companies in different industries big and small and from start to finish. The finish portion includes when to invest in bonds, stocks and other money market placements.

And, this expert is a big help for many neophyte investors do not know when to get out of the stock market. Getting out of the market is best when the historical trend of the company investee shows that there is a continuing loss for the past year or years of operations. The expert can translate into layman’s language the voluminous stock market data so that the plain man will know whether the company he or she is interested to invest in is a failure or simply stagnate.Historically, many bankers and other financing companies end up transporting their troubled loans to the troubled loans department.

This department is primarily focused on collecting of non -performing assets and investments like bonds and other credit instruments. This special forces department  is mandated to exhaust all efforts  to cure the bank’s loan credit risks. One remedy that the special forces department gives is to enter into a credit risk rehabilitation program with the loan clients. Consequently, many of the distressed loan client will enter into the unavoidable end of company life stage called liquidation.

The distressed company  at this final stage sells all its assets to pay for the debts owing the creditor banks and other interested parties. The assets of the company includes inventories of items for sale in the merchandising business, office supplies inventory and others. And the assets of the company include the office equipments like calculators, computers, adding machines and the like. Other assets include furniture and fixtures, buildings, delivery equipment, land as well as other assets.

And, the article Credit Risk Watch (Espersen, 2002) stated that the chances are slim for a person to glance over the Wall Street Journal and not be touched by stories of some stock market companies that under crisis and under liquidation proceedings for bankruptcy. One major reason for this debacle is related to the mishandling of credit risks. For, many borrowers are considered credit risks for they are not able to pay their loan obligations on time. Currently, many debtor companies have been included in the credit risks category.

Frankly speaking, the number of obligation defaulters are currently at an all time high.Barbara Davison, the president of Investment Training and Consulting Institute Inc., in Auburn Kan., has even outsourced derivative audits with their internal auditing team for the past few years now.

She, an author and authority on credit risks stated upon interview that  credit risk, which is also known as credit default risk  is caused the  possibility that the long term bonds issuer or borrower to default on the payment of the periodic installments. She announced that her internal audit staff  focus on the probability of increasing the percentage of borrowers willing to pay the maturing obligations. Further, she stated that derivative audit is centered on contract audits and  accounts receivable audits as well. In addition, derivative audits concentrate on the scope of counterparty  transactions.

  She discussed that  “Derivatives are financial arrangements between parties whose payment or value is derived from the performance of some agreed upon, underlying benchmark. They can be issued on such underlying instruments as currencies, commodities, government or corporate debt, home mortgages, stocks, interest rates, weather, or any combination. Derivatives can mitigate risk by transferring “unwanted” risk to the market. However, using derivatives to hedge areas such as interest rate or price risk comes with its own concerns.

Derivative risk can be classified in five major risk classifications: market, legal, operational, management, and counterparty risk. Over-the-counter derivative transactions are exposed to counterparty risks due to their deal terms”.Based on the above expert opinion of Davison, derivatives can lessen their losses by transferring some of its headaches to the stock market in a volatile buy and sell situation. Thus, she recommended that  aggregation risk and continuous underwriting should be the best way to get out of a  cash –tight situation.

In fact, the debacle that happened to Enron is a very good example of credit risk.  For, Enron connived with its external auditor, Arthur Andersen to present financial statements that fraudulently presented financial statements. For, the balance sheet showed that the company had large amounts of assets which over overstated to the detriment of the stakeholders of Enron. The discovery of this fraudulent practice caused the closure of Enron and the deletion of Arthur Andersen from the list of the big five auditing firms.

To counteract credit risk,  many underwriting and other insurance companies  analyze the debtor company’s credit risk. After the credit risk study is done, the credit committee gives a nod to companies that have the capacity to their obligations when the periodic need to pay the installments fall due.The article My firm, Business Resources Services has developed a process that is called “Profit Mastery”(2007).  The current trend in the credit risk market is concentrated on the credit derivatives.

However, this new credit risk scenario has still to be tested.And, another article entitled Fitch Incorporated into iTraxx Credit Default Swap Indices (2005) Credit Default Swap Indices shows that International Index Company has already changed its policies and procedures in relation to the IItraxx Europe and Crossover Credit Default Swap Indices. For, the object is to infuse and reflect changes in the credit market environment.  Further, the current rules include the implementation of the Fitch Ratings and the Moody’s investors service and the Standard and Poor’s (S& P) interpretations in determining the eligibility issues.

Also, the report stated that “ iTraxx CDS indices are managed and administered by IIC and were launched in 2004 in conjunction with the leading global investment banks. They comprise the most liquid names in the European and Asian markets, and have become established as the leading CDS indices.  ‘We are very pleased with IIC’s decision to add Fitch’s ratings to the portfolio rules of construction for both the iTraxx Europe and iTraxx Crossover indices,’ said Kimberly Slawek, Group Managing Director, Fitch Ratings. ‘This is further testament to the acceptance of Fitch’s ratings by the largest capital market participants spanning across all sectors and geographies.

‘”. Evidently, Fitch Ratings is one of the recognized global rating agencies in the field of credit risk marketing. Fitch started on the organic growth and excellent strategy for analyzing, interpreting and projecting the credit risk probabilities of companies generally listed in the stock markets. Fitch Ratings has grown immensely because of its reputation in giving very valid and very relevant research findings or a few companies.

The company has grown in leaps and bounds across most of the fixed income markets.  Fitch Ratings is strategically located in New York and London. It has offices and also has formed business partnerships with over fifty locations and covering entities in over seventy nine countries. Its website is www., the  book The Politics of International Credit: Private Finance and Foreign Policy in Germany and Japan (Spindler, 1984: p 37 -50) in its government and bank research that Germany’s global banking and  official government policies are  focused on  achieving their own goals and objectives resulting in complementary credit risk management behavior. For the Laissez Fair market economy is used to strengthen the creditor’s interest income generating activities.

  Thus, there is  a strongly mutual relationship between the credit granting companies and their money borrowing business community partners. Thus, the German banks and the German government have different ways to helping the financial market there. The government is responsible for making laws that will stir the business of borrowing and lending faster and better for both the borrowers and the lenders themselves. Furthermore, the government’s interest in helping the creditor banks make a profitable business out of lending money is evidenced by its maintenance of a safe and economically viable business environment.

The government takes care of the political and  financial health of the credit management industry.   Currently, the German banks’ global lending and deposit transactions have little transactions.According to the book  Experts in Uncertainty: Opinion and Subjective Probability in Science (Cooke, 1991, p 61 -68) stated that  it is the responsibility of creditors to be focus on the management of credit risks as well as increasing the uncertainty of the future in the creditors’ favor.  Naturally, banks hire experts in the business, credit risk and banking sectors.

Often, the financial analysts differ in their future credit risk projections  because the bottom line here is credit risk. Obviously, the experts themselves are uncertain whether the other experts are very comfortable with their credit investigation.Thus, we often here the comment “expert A provides a better representation of the uncertainty in this case than expert B.” These credit risk uncertainties are often identified as  ‘fuzzy truths’ or as ‘members in a fuzzy st’.

In the ordinary street language, fuzzy means that these companies have high probability of defaulting on their payments when the time to unpocket their payables arrive. Or, we can call these loans as bad or good loans.  Here, uncertainly is often translated to default probability.  Often times, logical thinking in the credit risk industry is not as easy as the textbooks seem to say.

For, credit risk companies resort to statistical tools like the iTraxx, the full method and the reduced method to be their guiding north star.Admittedly, many people like us would daringly make elementary logical mistakes  if we do not use statistical tools as our maps as we wiggle in and out of each credit risk situation.  Other people term this probabilistic thinking, Definitely, logical thinking should be premised on statistical reliance. Admittedly, our logic teachers often reiterate that the usefulness of probabilistic ergos or predictions should be based on performing mathematical computations.

  Thus, designers of artificial intelligence such as the computers always permeate logic and statistical tools like ANOVA, Chi Square, Pearson Test, Mean, Average and others as the basis for generating a conclusion to their research.According to the book The Risk Management Process: Business Strategy and Tactics (Culp, 2001, p.60-75), there is a kind of irrelevance  between corporate financing and management decision making risks. For, the corporate financing industry have often entered in hedging transactions where one financial instrument will take care of  the unprofitable investment in another financial instrument.

For this hedging theory is used by some companies in order to avert any credit losses from the volatile credit risk industry.  The article further stated that “the company was assumed to behave like a risk-averse investor maximizing expected utility and minimizing variance, and departures from variance-minimizing hedge ratios were defined as speculation”. However, this can unsatisfactorily backfire in some instances. For, some companies treat credit risk as a simple engagement  in the collection of  scarce accounts receivables.

For  the credit risk system encompasses the creditor and shareholder as well as the manager and shareholder relationship.;The term stocks  is the amount fixed in the articles of incorporation to be subscribed and paid in or secured to be paid by the stockholders of the corporation, either in money or property or services. At the organization of the  corporation or afterwards when the stockholders can invest their money in stocks in the stock market in London and the United States. The amount fixed in the minutes of the meeting are called authorized capital stocks.

   The capital stock is often divided into common share and preferred shares of stocks.  The advantage of the owner of a share of stock is that he or she can get  a certain share of the total earnings of the company. Also, the person having stocks can participate in the election of officers and in the approval and disapproval of the future actions or inactions (plans) of the board of directors and other key officers running the company. Furthermore, the stockholders also have the priority to invest in new stock issues of the companies in proportion to the shares of stocks he owns.

For example, if he owns 1,000 shares of stocks which is equivalent to ten percent of the total stockholders’ equity, then he is authorized to invest in ten percent of new shares of  the company.  And, the stockholders are authorized to be given first priority in the distribution of assets in case the company will file for bankruptcy and the company breaks up into many pieces(Larson, 1995; p459 -480).;Whereas, a company can resort to borrowing large amounts of cash whenever it needs large amounts of cash. The company may borrow large amounts of cash from the general public such as the stock market.

  This is what is called bonds. For, a bond is a formal  unconditional promise, made under seal, to pay a specified  sum of money at a determinable future date, and to make periodic interest payments to the bond creditors  at a stated rate until the principal sum is paid.  A bond is evidenced a certificate and a contractual legal agreement between the issuer and the investor called the bond indenture(Larson, 1995; p376-418).;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;IV.

Parallel Framework;;;;;;;Figure 1. This paradigm was used to determine if there is a significant difference between Bonds  and Stocks of twenty companies which is a credit risk using iTraxx Europe Sector Informational Data.;;;;;;;;;;;;V. Data and methodologyV.

i. DataI used randomization processes in publishing statistical abstract from the informational data of iTraxx Europe sector . bonds, and stocks is set as the informational data. I also used US dollar denomination for the computation this is based on prevailing price.

From 125 companies under iTraxx Europe we choose 20 companies for this paper. Of companies 6 of them are not on the run so we decided to replace them. I then laid down the constraint to twenty (20) companies for the analysis of the informational data. I got the informational data from the website www. .The bonds and stocks of the twenty selected companies under iTraxx Europe informational data was gathered through website,V. ii .

MethodologyI investigate the significant relationship between debt (total liabilities) and total equity of the selected 20 companies under iTraxx Europe informational data. I also use commutative distribution function, distribution frequency of probability and analysis of variance (ANOVA) .;VI. RESULTSThis section presents the analysis of the data on the study to find out if there is significant relationship between bond, and stocks of companies using iTraxx Europe Sector informational data.

The commutative distribution function was used to single out the defaulter from the non defaulter companies based on their bonds and stocks. Tables 1 to 2 will show the significant effect of the bonds and stocks in the stock market.From table 1 , we found out that out of 20 companies there is only 3 companies which has good bonds and this gives as hint that this company has no credit risk. These companies are Company 1, 2, and 3.

Company 1 has the highest probability value which yields a score of 0.45 or 45 percent. This was followed by company 2, which has a value of 0.32.

Company 3 has a probability of 0.19 and lastly we also found out that one company has slight probability of no credit risk this company is company 12. Company 12 which has 0.04.

This implies that greater part of the companies under this selection has extreme credit risk factor. This builds a signification influence in the stock market but some of the investor is not sensitive enough to the flaws of  such market.From table 2 , we found out that out of 20 companies there is only 5 companies which has a good stocks and this gives as hint that this company can pay their debt. These companies are Company 1, 2, 3, 9 and 12.

Company 2 has the highest probability value which yields a score of 0.45 or 45 percent. This was followed by company 1, which has a value of 0.31.

Company 12 has a probability of 0.18, Company 3 has probability of 0.01 and Company 9 has probability of 0.06 or 6 percent.

Company 3 and 9 has low probability of no credit risk.This implies that there is slight difference in such the bonds and stocks in determining the level of credit risk factor.From Diagram 3, we found out that there is only one company who has a related liability but large stocks. This means that this company afraid to take the credit risk or they might be some financial reason around him or we can say that this failed to balance the financial statement of his company.

It is notable that two companies balance their financial statement (company 11 and 18). Their bonds and stocks are more or less equal. We also found out that there are two companies who has slightly balance in terms of bond and stocks. These companies are company 15 and 16.

The majority of the companies have large bonds and small amounts of stocks. This implies that this companies have large level of credit risk.The Analysis of Variance was used to determine the relationship between bonds and stocks of the twenty selected companies under iTraxx Europe data. The result of the “Analysis of Variance” (ANOVA) shows that I have to accept Ho because the computed value of F statistics is less than its tabular value.

This means that the bonds and stocks has no bearing on each other. This research recommend the expansion of the study like adding some financial statements indicator or factors like short term liabilities, bonds, capital surplus, and total assets. We used 0.05 as our degree of freedom.

Summary of findings of the study, to measure the relationship of the bond and stocks I used analysis of variance to between group sum of squares (SSbet  ) is equal to 2895769616791827.000, within group sum of squares (SSwit  ) is equal to 1172055783111243.000 and has total sum squares of 4067825399903070.000.

The between groups  degree of freedom of 1, and within groups of degree of freedom 18 and has a total degree of freedom of 19. The between groups mean sum square of 2895769616791827.000, and within groups mean sum square of  65114210172846.800 The computed F value of 44.

7216 and the tabular value of F statistics is 248;vii. conclusion:In this paper, we empirically examined the success of the comparison between bonds and stocks to measure credit risk. .This paper examined the two factors or indicators that probably affect the financial statement and flaws of the stock market exchange.

The factors that are under this study are bonds and stocks.I discovered out that there is no significant relationship between bonds and stocks. This means that the higher the stocks the lesser the credit risk. The financial statement of the different companies was more effective in its ability to explain the credit risk.

The large exposure of financial statement (negative or positive) to a branch out pool of credit risk is now much easier to gain and the liquidity of the iTraxx market has catch the attention of  new participants such as hedge funds and capital structure arbitrageurs.I suggest that the company or investor ought to look forward to the number of stockholders and the amount of stocks to identify the credit risk.;;;;;;;;;;;;VIII. Appendicesdiagram 1Table 1:Table 1.

Commutative Distribution Function of  Bondsof 20 selected companies from iTraxxCompanyP ( x )F ( x )10.450.4520.320.










00Total1.00;;diagram 2Table 2Table 2. Commutative Distribution Function of stocksof 20 selected companies from iTraxxCompanyP ( x )F ( x )10.310.










00200.001.00Total1.00diagram 3:table 3:Table 3.

Case Summary of 20 Selected CompaniesCompanyBondsStocks11272065480.00370740242908714959.00540319573520813483.00127821241748.





0070001919917.0042382082695.0046740Total2811903328121012268.1;Regression;Variables Entered/Removed(b);ModelVariables EnteredVariables RemovedMethod1Liabilities(a).

Entera  All requested variables entered.b  Dependent Variable: Equity;Model Summary;ModelRR SquareAdjusted R SquareStd. Error of the Estimate1.844(a).

712.6968069337.65391a  Predictors: (Constant), Liabilities;ANOVA(b);ModelSum of SquaresdfMean SquareFSig.1Regression2895769616791827.


0001865114210172846.800Total4067825399903070.00019a  Predictors: (Constant), Liabilitiesb  Dependent Variable: Equity;Coefficients(a);ModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd.



669.000a  Dependent Variable: Equity;;;;;;;;The computation proper for the analysis of varianceSSbet  = 2895769616791827.000SStot  = 4067825399903070.000SSwit  = 1172055783111243.

000dfbet  = c – 1 = 2 -1 = 1dfwit  = N – C = 20 – 2 = 18dftot  = N – 1 = 20 – 1 = 19mSSbet  =  SSbet   =  2895769616791827.000 = 2895769616791827.000dfbet                       1;mSSwit  =  SSwit   =  1172055783111243.000  = 65114210172846.

800dfwit                       18;fc  = mSSbet  =  2895769616791827.000 = 44.7216mSSwit         65114210172846.800;f0.

05(1,18)  = 248;;;;;;;;;;;;;;;;BIBLIOGRAPHY;Aczel,Amir D., Complete Business Statistics, 3rd Ed., Irwin/ McGraw-HillCompanies.,USA.

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