The relationship between the country ratings and stock market performances

Financial Institutions and MarketsFIN501 – Research PaperThe Relationship Between the Country Ratings and Stock Markets Performance

The Relationship between the Credit Ratings and Stock Market Performances

Financial Institutions and Markets

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Emp. Results

Conclusion

References

Financial Institutions and MarketsFIN501 – Research PaperThe Relationship Between the Country Ratings and Stock Markets Performance 3 4

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SOVEREIGN CREDIT RATINGS

Sovereign credit ratings are widely used measures of the creditworthiness of a particular country's government.

Changes in credit ratings, whether real or potential, indicate not only that the credit risk of the government increases or decreases, but also that the macroeconomic environment changes, and thus it may have an impact on the performance of the companies which operate in that country.

An approach to examine the effect of the sovereign credit rating announcements is to investigate their impact on stock prices.

While credit ratings indicate future default probability, they are not absolute measures of credit risk. Instead, they provide an ordinal ranking, i.e. information on relative creditworthiness. All major rating agencies employ similar rating scales, which consist of 20 or more notches.

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SOVEREIGN CREDIT RATINGS

A change to a credit rating can signal to the market that the creditworthiness of the country has changed.

The importance assigned by the market to this signal of quality is such that a credit rating downgrade can provoke a strong reaction from the aggrieved country.

The informational value of credit rating agency is a controversial and inconclusive issue.

Do the credit rating announcements have any impact on the stock market?

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LITERATURE REVIEW

A number of researchers have explored the relationship between ratings and stock markets performance by different methods but the results are conflicting.

Some of the earlier studies have examined the credit rating announcements on the security returns and found no significant returns (Weinstein, 1977; Pinches and Singleton, 1978). Kaplan and Urwitz (1979) and Wakeman (1981) suggested that bond rating agencies only had access to public information and their ratings have no added value to the investors.

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LITERATURE REVIEW

However, other researchers argued that the rating agencies had information that was not available in the public domain and that the stock market reacted significantly to the relevant information.

Ederington and Yawitz (1991) indicated that the rating agencies are the low cost providers of such information. Danos, Holt, and Imhoff (1984) concluded that bond rating agencies possess expert judgment and are specialists at processing information related to country’s condition. Cornell, Landsman and Shapiro (1989) argued that revisions in bond ratings may have information content.

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HISTORICAL RATINGS & INDEXES DATA

The sample consists of rating changes for both developed and developing countries rated by Fitch for the period 1994-2012.

However, mostly developing countries are used because of higher rate of change in terms of ratings. The ratings and official rating dates were supplied directly by Fitch.

Also the stock market indexes for both developed and developing countries collected within the range of 1989-2012 on daily basis from Yahoo Finance and Google Finance.

The countries used within the study are Greece, India, Brazil, Italy, Spain, Mexico, Malaysia, Indonesia, and Japan.

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QUANTIFICATION OF RATINGS

In order to apply tests ratings needed to be converted into numerical value; therefore, starting with C- rating as 1 the ratings are changed with the numerical values; such as C=1, C+=2, CC-=3.

During the study, main stock market index of each country is used referring to their stock market performances. Also, in order to make their frequency equal both indexes’ and ratings’ frequency decreased to annual.

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DESCRIPTIVE STATISTICS

Stock Market Performances

Greece India Brazil Italy Spain Mexico Malaysia Indonesia Japan

Mean 3071.399 10100.62 36145.56 27766.41 8869.033 16268.06 1003.503 312.4638 14099.98

Median 2609.395 9424.240 31367.00 27774.00 8866.550 11262.95 915.1400 262.5600 13843.55

Maximum 5747.910 18327.76 79647.00 42197.00 14553.20 37422.68 1521.290 692.1600 20813.00

Minimum 766.7400 3250.380 8172.000 15828.00 3040.100 3647.100 569.5100 82.70000 7994.050

Std. Dev. 1660.275 5865.371 24181.63 9000.485 3020.093 12290.08 334.8662 214.1593 4338.584

Skewness 0.393518 0.248328 0.392726 0.178457 -0.220728 0.558238 0.367539 0.510497 0.085103

Kurtosis 1.865726 1.425818 1.691534 1.787726 2.667494 1.780994 1.701717 1.815953 1.565132

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DESCRIPTIVE STATISTICS

Ratings

Greece India Brazil Italy Spain Mexico Malaysia Indonesia Japan

Mean 15.80000 14.76923 13.06250 19.44444 22.72222 15.81250 18.00000 12.46154 21.26316

Median 18.00000 15.00000 12.00000 19.00000 24.00000 16.00000 18.00000 12.00000 20.00000

Maximum 19.00000 15.00000 16.00000 21.00000 24.00000 17.00000 19.00000 15.00000 24.00000

Minimum 2.000000 14.00000 11.00000 17.00000 18.00000 15.00000 16.00000 10.00000 19.00000

Std. Dev. 5.094660 0.438529 1.806239 1.333333 1.742397 0.543906 0.966092 1.330124 1.820931

Skewness -2.24771 -1.27801 0.465267 -0.20992 -1.27704 -0.17049 -0.45816 0.196281 0.676245

Kurtosis 6.693130 2.633333 1.696271 2.378906 3.980123 3.043245 2.122449 2.723850 1.775132

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A panel has the form

Where i is the individual dimension and t is the time dimension.

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PANEL DATA

In statistics and econometrics, the term panel data refers to multi-dimensional data. Panel data contains observations on multiple phenomena observed over multiple time periods for the same sections.

Time series and cross-sectional data are special cases of panel data that are in one-dimension only.

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A panel has the form

Where i is the individual dimension and t is the time dimension.

Introduction

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PANEL DATA

In statistics and econometrics, the term panel data refers to multi-dimensional data. Panel data contains observations on multiple phenomena observed over multiple time periods for the same sections.

Time series and cross-sectional data are special cases of panel data that are in one-dimension only.

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TESTING FOR INTEGRATION

Panel Unit Root Tests

ADF – Fisher Chi-Square

PP – Fisher Chi-Square

Im, Pesaran and Shin W-stat

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Introduction

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RELATION TESTING

Panel Granger Causality

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Introduction

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Panel Co-Integration

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UNIT ROOT TESTSPAGE

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Variable Method Decision


INDEX 9.50471 [3]

4.75630**[1]

2.56002 [1]

∆INDEX 41.6316* [2]

37.3582*[1]

25.3620*[1] I(1)

RATING 2.165336[0]

0.820650[0]

-1.051970[0]

∆RATING -3.761079*[0]

-4.081819*[0]

-4.267101*[0] I(1)


INDEX 13.4386** [3]

8.41179 [1]

0.42995 [1]

∆INDEX 58.3863*[2]

78.1425*[1]

39.8658**[1] I(1)

RATING 2.165336[0]

0.820650[0]

-1.051970[0]

∆RATING -3.761079*[0]

-4.081819*[0]

-4.267101*[0] I(1)

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UNIT ROOT TESTSPAGE

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INDEX ----

1.56651[1]

2.56002 [1]

∆INDEX ----

-4.93111*[1]

25.3620*[1] I(1)

RATING 2.165336[0]

0.820650**[0]

-1.051970[0]

∆RATING -3.761079*[0]

-4.081819*[0]

-4.267101*[0] I(1)

Levin, Lin & Chu t

INDEX 0.00095 [3]

0.46183 [1]

0.42995 [1]

∆INDEX -5.96823* [2]

-5.28770*[1]

39.8658**[1] I(1)

RATING 2.165336**[0]

0.820650[0]

-1.051970[0]

∆RATING -3.761079*[0]

-4.081819*[0]

-4.267101*[0] I(1)

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SAMPLE INDEX VALUESPAGE

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GRANGER CAUSALITY TESTSPAGE

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ReferencesN = 111K = 2 INDEXRATING RATINGINDEX

1.49615 2.63557 [0.2287] [0.0564]

𝐺𝐼𝑁𝐷𝐸𝑋=ln( 𝐼𝑁𝐷𝐸𝑋𝐼𝑁𝐷𝐸𝑋 (−1 ) ) ,𝐺𝑅𝐴𝑇𝐼𝑁𝐺=ln( 𝑅𝐴𝑇𝐼𝑁𝐺

𝑅𝐴𝑇𝐼𝑁𝐺 (−1 ) )

Before conducting Granger causality tests values of all variables are transformed into percentage amount, because of being I(1), according to the equation:

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CO-INTEGRATION TESTSPAGE

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𝐼𝑁𝐷𝐸𝑋=𝛼0+𝛽1𝑅𝐴𝑇𝐼𝑁𝐺+𝜖

In order to apply co-integration tests first of all, residuals are collected from the ordinary least squares (OLS) of the following regression equation:



Residual 16.8263** [3]

6.63873* [1]

14.1248**[1] I(0)

∆Residual 52.3061* [2]

44.3983* [1]

25.4193* [1]


Residual 13.2393* [3]

4.52342*[1]

5.79349** [1] I(0)

∆Residual 70.9801* [2]

89.1076* [1]

39.9183* [1]

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CO-INTEGRATION TESTSPAGE

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Residual ----

0.70977**[1]

14.1248**[1] I(0)

∆Residual ----

-5.25203* [1]

25.4193* [1]

Levin, Lin & Chu t

Residual -1.97372**[3]

-0.97246**[1]

5.79349** [1] I(0)

∆Residual -6.81616* [2]

-5.64822* [1]

39.9183* [1]

Because the variables INDEX and RATING have no unit root at 1st difference (I(1)), we can analyse two of them in terms of co-integration. After collection of the residuals, unit root test is conducted on the residuals. The estimation results suggest there exist long-run co-movement between value of stock market index and rating values.

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GRAPHICAL REPRESENTATIONPAGE

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References1/1

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Introduction

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References1/1

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CONCLUSIONPAGE

Introduction

Data

Methdology

Emp. Results

Conclusion

ReferencesAfter the collection of dataset each data is tested in terms of whether they have unit root or not.

Then granger causality test applied between two variables and it is found that ratings granger cause stock market performance within short/mid – term. Because the integration level of both variables is I(1), co-integration test applied and the result indicates that there is also long-term effective relationship between rating and stock market performance.

The aim of this work is finding whether there is an effect of the relationship between credit ratings and stock market performance.

In order to get solid and robust results panel data is used during the study; and the dataset consists of the countries that have high volume stock market and not stable credit ratings.

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CONCLUSIONPAGE

Introduction

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Emp. Results

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Eventually, according to econometrical analysis credit ratings affect stock market performance both within short-term and long-term. For future studies, the impact of credit ratings on stock market performance may be measured and its robustness may be tested.

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REFERENCESPAGE

Introduction

Data

Methdology

Emp. Results

Conclusion

References

1) Brown, S. J., and J. B. Warner, 1985, Using daily returns: the case of event studies, Journal of Financial Economics 14, 3–31.2) Creighton, A., L. Gower, and A. Richards, 2004, The Impact of Rating Changes in Australian Financial Markets, research discussion paper (Reserve Bank of Australia, Sydney, NSW).3) Dichev, I. D. and Piotroski, J. D., 2001. “The Long-Run Stock Returns Following Bond Ratings Changes.” Journal of Finance 56: 173-203.4) Ederington, L. and J. Yawitz, 1991. “The Bond Rating Process.” In E. Altman (ed.), The Financial Handbook, 6th ed.5) Grier, P. and Katz S., 1976. “The Differential Effects of Bond Rating Change among Industrial and Public Utility Bonds by Maturity.” Journal of Business 49: 226-239.

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REFERENCESPAGE

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6) Graham, J. R., and C. Harvey, 2001, The theory and practice of corporate finance: evidence from the field, Journal of Financial Economics 60, 187–243.7) Hand, J. R. M., Holthausen, R. W., and Leftwich, R. W., 1992. “The Effect of Bond Rating Agency Announcements on Bond and Stock Prices.” Journal of Finance 47: 733-752.8) Ingram, R. W., Brooks, L.D. and Copeland, R. M., 1983. “The Information Content of Municipal Bond Rating Change: A Note.” Journal of Finance 38: 997-1003.9) Katz Steven, 1974. “The Price and Adjustment Process of Bonds to Rating Reclassifications: A Test of Bond Market Efficiency.” Journal of Finance 29:551-559.10) Pinches, G. E., and J. C. Singleton, 1978, The adjustment of stock prices to bond rating changes, Journal of Finance 33, 29–44.

Economy & Finance

The relationship between the country ratings and stock market performances