Split Ratings and Debt-signaling in Bond Markets- A [email protected]

7/25/2019 Split Ratings and Debt-signaling in Bond Markets- A [email protected]

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Split ratings and debt-signaling in bond markets: A note

Ashraf Ismail , Seunghack Oh 1, Nuruzzaman Arsyad 1

Sampoerna School of Business, Building D Mulia Business Park, Jl. Letjen MT. Haryono Kav. 58 60, Jakarta 12780, Indonesia

a b s t r a c ta r t i c l e i n f o

Article history:

Received 17 May 2013

Received in revised form 23 December 2014

Accepted 25 December 2014

Available online 3 January 2015

JEL classication:

C26

C58

G12

G14

F34

F36

Keywords:

Credit ratings

Asymmetric information

Debt signal

Bond markets

Split ratings occur when national and international credit rating agencies assign different ratings to the same

rm. Employing various proxies for asymmetric information and data from advanced and emerging bond mar-

kets, we review the evidence that split ratings are caused by asymmetric information between rms and credit

rating agencies. We then apply the debt-signaling model to the split ratings problem, by testing for a systematic

relationship between the debt-to-equity ratio and the magnitude of split ratings across countries.We nally test

forthe existenceof an optimaldebt-signal, which impliesthat higherdebt-to-equity ratioswill reducethe ratings

split to an optimal minimum, after which accumulating more debt widens the ratings split. Our results suggest

that rms in emerging markets can use thedebt-signal upto a maximal point, after which it becomes inefcient.

2014 Elsevier Inc. All rights reserved.

1. Introduction

Split ratings occur when national and international credit rating

agencies (CRAs) assign different ratings to the same rm (Shin &

Moore, 2003). We focus on the information asymmetry between rms

and CRAs, and examine the mechanism by which CRAs update their

beliefs regarding bond risk in response to information publicly released

by rms. First, we hypothesize that due to asymmetric information,

CRAs possess distinct prior beliefs regarding the quality of rms,

which may be updated in response to new information. To penetrate

market noise, rms attempt to reveal their quality via a debt-signal,

which if successful, will cause the posterior beliefs of CRAs to converge

in such a manner that contracts the ratings split. Second, using several

proxies for asymmetric information, we test for the existence of an op-

timal debt signal, which implies a quadratic relationship between

debt-to-equity ratios and split ratings.

Our primary contribution to the literature is the idea that debt-

signaling can help emerging market rms overcome the more pro-

nounced split ratings margins from which they suffer. We further

contribute to the literature by arguing that the debt-signal has an

upper bound, after which it is detrimental to a rm's credit rating. Our

results are signicant for two reasons. First, if the magnitude of split

ratings is correlated with the degree of asymmetric information, then

higher levels of split ratings will hinder the price discovery process.

This is particularly true in emerging bond markets where information

about rm performance is relatively scarce. Second, if the optimal

debt-signal reduces the cost of price discovery then it could promote

the convergence of bond yields for comparable rms across emerging

and advanced markets. Hence, our research has important implications

for bond market efciency and integration.

In Section 2, we develop our hypothesis regarding the importance of

debt-signaling for less efcient bond markets, and we compare several

proxies for asymmetric information, including the debt-to-equity

ratio, market-based proxies such as the price-to-earnings ratio and the

price-to-book ratio, as well as the standard deviation of forecasted EPS

(which serves as an opinion-based proxy). In Section 3, we measure

the effect of asymmetric information upon nationalinternational split

ratings. We rst analyze our cross-country data set, which consists of

313 rms drawn randomly from 14 countries, to detect evidence of

split ratings. We then employ a step-wise regression method to test

for the relationship between split ratings and debt-signaling. We con-

sider baseline models that control for rm size, industry and country

effects, and proceed sequentially to examine models that contain

Review of Financial Economics 24 (2015) 3641

Corresponding author. Tel.: +62 812 8363 6965.

E-mail addresses:[email protected](A. Ismail),[email protected](S. Oh),

[email protected](N. Arsyad).1 Tel.: +62 812 8363 6965.

http://dx.doi.org/10.1016/j.rfe.2014.12.003

1058-3300/ 2014 Elsevier Inc. All rights reserved.

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information proxies, including the debt-to-equity ratio, market-based

proxies and an opinion-based proxy, and regress the proxy variables

on the split ratings gap.

To test the debt-signaling hypothesis, we employ a dummy variable

to capture the bloc effect,which measures the impact of incremental

changes in the magnitude of information proxies upon split ratings in

emerging bond markets compared to their advanced counterparts. In

this manner we estimate the effectiveness of each information proxy

as an explanation for split ratings across bond markets. More generally,

this method allows us to distinguish between the explanatory power of

debt-signaling compared to other proxies. To test for the existenceof an

optimal debt-signal, we rst specify an ordered probit model and em-

ploy polynomial regression to detect the hypothesized quadratic rela-

tionship between the debtequity ratio and split ratings, and we then

compare the results with the outcome of the original linear regression.

We nally analyze the results from our interaction terms in the context

of a multiple regression test on the debt-to-equity ratio and other prox-

ies, to determine whether their signs or signicance are altered by the

presence of other independent variables.

InSection 4, we review and interpret the results of our empirical

analysis, with regard to their implications for credit ratings and bond

market efciency. We nd evidence that supports the optimal debt-

signal hypothesis in emerging markets, but not in advanced bond mar-

kets.Consequently, our research explores the relationship between cap-

ital structure, asymmetricinformation, and splitratings, and addresses a

major challenge that emerging economies must overcome in order to

create more efcient and integrated bond markets.

2. Hypothesis development

Credit rating agencies exist in order to mitigate information

asymmetries between investors andrm insiders regarding a rm's val-

uation (Langhor & Langhor, 2008), but this role is often complicated by

the fact that CRAs can assign different ratings to the same rm or bond.

The prevalence of split ratings is displayed in Table 1, which isa sample

of 313 randomly selectedrms collected from ve advanced and nine

emerging economies.Table 1shows that rms in emerging markets

have higher degrees of split ratings than rms in advanced markets.

Ofthe 90rms not assigned split ratings, 81 are in advanced economies

and only nine rms are in emerging markets. At the other end of the

spectrum, only 13 rms in advanced markets are assigned split ratings

of two or more rating levels, while 56 rms in emerging markets aresimilarly rated. These stylized facts support our claim that emerging

marketrms tend to have a higher incidence of split ratings.

Explanations for split ratings range from Ederington (1986) who ar-

gues that split ratings are random errors, toCantor and Packer (1997),

who emphasize the use of different rating models by CRAs, toMorgan

(2002)who hypothesizes that split ratings are correlated with the

opacity of a rm's assets. Each of these arguments is problematic. For

instance, the random error argument is contradicted by evidence that

Moodys systematically assigns lower ratings than S & P ( Livingston,

Naranjo, & Zhou, 2007).Dandapani and Lawrence (2007)found that

one third of all split ratings can be explained by different rating

methods, which means that the majority of split ratings variation can-

not be similarly explained, nor does their approach explain why split

ratings are more prevalent in emerging markets than in advanced

markets. Finally,Livingston et al. (2007) nd that six out of seven vari-

ables used to measure asset opacity are signicant for explaining the

ratings split, but asset opacity is specic to the rm and not the CRA,

and perhaps for this reason,Shin and Moores (2003)test of the asset

opacity hypothesis in Japanese markets is inconclusive.

Our argument rests on the claim that asymmetric information is the

most importantnancial market imperfection.2 We postulate that if all

parties were equally well informed there would be less variation among

CRAs, and the distinction between advanced and emerging markets in

terms of split ratings would disappear.3 But due to asymmetricinforma-

tion, each CRA forms distinct prior beliefs, or conditional probability

distributions regarding the quality ofrms, which are expressed as

a variation in credit ratings. By beliefs we mean that CRAs have prior

probability distributions regarding the value of a specic rm, and in

response to the debt-signal CRAs may update their prior beliefs, gener-

ating aposteriorprobability distribution that is distinct from the prior

distribution. Alternatively, new information may have no effect on

CRA beliefs regarding the value/risk of a specic rm, which means

that the CRA's prior and posterior distributions are identical and so the

rm's credit ratings will remain unchanged. In principle however, CRA

beliefs may continue to evolve in response to new information until a

stationary signaling equilibrium is obtained, which implies that the un-

derlying stochastic process is stable over time, so that CRA beliefs and

credit ratings are consistent and mutually reinforcing.

Debt-signaling has two primary outcomes; pooling and separating.

We use this framework to analyze the adverse selection problem

faced by CRAs in emerging markets, since CRAs cannot always distin-

guish between high quality and low quality rms. We also use thisframework to examine the hypothesis that debt-signaling can help

high quality rms distinguish themselves from low quality rms

(Klein, O'Brien, & Peters, 2002; Leland & Pyle, 1977; Myers & Majluf,

1984; Ross, 1977). A pooling equilibrium illustrates adverse selection,

since pooling implies that CRAs (and investors) assign the same valua-

tion to high quality and low quality rms, thereby under-estimating

the credit worthiness of high qualityrms and over-estimating the

credit worthiness of low quality rms. In a separating equilibrium by

contrast, CRA beliefs regardingrm quality will converge in a manner

that distinguishes between high quality and low quality rms, which

causes a contraction in the ratings split.

We argue that debt-signaling is more likely to yield a separating

equilibrium in emerging markets than in advanced markets. Debt is an

effective signal in noisy markets because it is senior to equity in thecash-ow waterfall, and once equity is exhausted during the bankrupt-

cy work-out process, what remains of the rm's assets reverts to debt-

holders. Debt thereby increases a rm'snancing costs as well as the

likelihood of default if a rm becomes illiquid. The debt-signal is thus

Table 1

Split ratings.

Category Whole samples Advanced economies Emerging economies

Number ofrms with non-split 90 81 9

Number ofrms with split by one level 87 68 19

Number ofrms with split by two levels 67 33 34

Number ofrms with split by more than two levels 69 13 56

Total number ofrms 313 195 118

2 International CRAs provide ratings for a limited number of listed rms in emerging

bond markets and may lack locally specic knowledge, while national CRAs suffer from

ratings criteria that vary widely, so it is not obvious which of these entities is better in-

formed about arm's operations. For this reason, we follow the literature on split ratings

by positing asymmetric information as an explanation for split ratings without specifying

which entity is better informed.3 Shen, Huang, and Hasan (2012)go further to argue that the higher degree of asym-

metric information in emerging markets leads CRAs to adopt different rating methods

across markets.

37A. Ismail et al. / Review of Financial Economics 24 (2015) 3641


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incentive compatible for high quality rms in emerging markets be-

cause it is too costly for low quality rms to mimic.

Our distinction between advanced and emerging markets is consis-

tent withKlapper and Love (2004)who nd that variation in the trans-

parency of corporate governance regimes across emerging markets is

correlated with rm valuations.Morck, Yeung, and Yu (2000) nd

that weak property rights protection in emerging markets makes in-

vestment arbitrage opportunities less attractive; and in the context of

bond ratings, this distinction is also supported by Bhojraj andSengupta (2003), whond that disclosure quality and bond yields are

negatively correlated. In advanced bond markets, corporate governance

regimes reveal higher quality information aboutrms and the legal sys-

tems protect investor rights more effectively, thereby neutralizing the

efcacy the debt signal.

We nally hypothesize the existence of an optimal debt signal. Opti-

mal debt theory posits that raising a rm's debt-to-equity ratio has the

benet of increasing the dollar value of its interest tax shield, but also

increases a rm's bankruptcy costs (Miller, 1977; Myers, 1984).The op-

timal debt level is dened as the point at which the present value of the

benet from increasing the debt-to-equity ratio equals the present

value of its cost. By extension, we posit the existence of an optimal

debt signal, which we dene as the debt level that minimizes the mag-

nitude of the ratings split. Optimality also implies that increasing a

rm's debt level beyond the optimal point should cause the ratings

split to widen, since doing so increases bankruptcy costs beyond any

value added to the rm.

Our expectations are that debt-signaling will help explain the varia-

tion of split ratings across countries, but it will be less effective in

explaining split ratings in the more efcient bond markets of advanced

economies. In what follows, we test our hypothesis that the debt-to-

equity ratio is the most effective proxy for explaining the difference in

the ratings split between advanced and emerging markets, and we also

test the idea that the relationship between split ratings and debt-

signaling is quadratic,as suggested by the optimal debt-signal hypothesis.

3. Data and methodology

In order to estimate the differential impact of asymmetric informa-tion upon the credit rating process, we must rst select an appropriate

proxy measure. In this regard,Clarke and Shastri (2000)review three

general measures of asymmetric information: 1) opinion-based proxies

derived from analyst forecasts, 2) proxies based upon nancial state-

ment ratios, such as price-to-book or debt ratios; 3) microstructure

proxies usually involving a decomposition of the bidask spread; and

beyond Clarke and Shastri, 4) the literature on asymmetric information

also relies upon the volatility of returns or trading volume, or some

permutation thereof, as measures of asymmetric information (Bates,

Conghenour, & Shastri, 1999).

Each of these proxies has weaknesses, such as the inherent subjec-

tivity of analysts' estimates, and the lack of a consensus regarding how

to disaggregate the relevant components of the bidask spread, as

well as the potential for measurement error in a rm'snancial ratios.However, proxies based upon nancial ratios also have a numberof dis-

tinct benets. First, nancial ratios are widely available and are open to

critical evaluation. Second, international accounting standards providea

unied framework in which to interpret the precise meaning and impli-

cations ofnancial ratios. Third, when we control for rm size, we can

easily test whether nancial ratios are correlated with the magnitude

of split ratings. For all these reasons, nancial ratios provide effective

proxy measures of asymmetric information that can be applied across

industries and countries.

3.1. Data sources

Table 2 summarizes our sampledistribution by country andindustry

(nancial sector vs. non-nancial sector). Our data set consists of

corporate ratings for 313 rms drawn fromve advanced markets and

nine emerging bond markets, including Canada, Japan, South Korea,

Taiwan, the United States, India, Pakistan, Indonesia, Kazakhstan,

Malaysia, Thailand, the Philippines, South Africa, and Sri Lanka. Of the

total rms in ourdataset,195 (62%)are drawn from advanced markets,

while 118 are drawn from emerging markets. Further, 133 rms (42%)

are nancial rms and the remainder are non-nancial rms. Each

rm in our sample is rated by both national and international credit

rating agencies.4 International credit ratings were drawn from Fitch,

Moody's, and Standard and Poor's, and were collected from their

website as of March 2012. National credit ratings were drawn from sev-

eral national credit rating agencies websites as of March 2012. Firm-

level data was collected from Datastream, and onlyrms listed in equity

markets were included.

3.2. Variable denitions

A split rating is operationally dened by the difference in the letter-

level between national and international CRAs, where the dummy

variable is assigned 0if there is no split rating, 1if the difference

between the ratings is one letter-level (for example AA and A), 2if

the difference is two letter-levels (for example between AA and BBB),

and3if the difference is more than two letter levels (for example be-

tween AA and BB or B). Because the split ratings variable is ordinal, we

use an ordered probit model to analyze the data.

Table 3displays the nancial ratio proxies and the opinion based

proxy for asymmetric information. Financial ratios include the price-

to-book ratio (PBR), the price-to-equity ratio (PER), and the debt-to-

equity ratio (DER), which are widely used in the literature to measure

the difference between market information and accounting information

associated with a specicrm.Table 3shows that at least for PBR, PER,and DER our sample is generally representative of the population of

rms in each economy.

Table 2

Sample distribution.

Country Financial Non-nancial Total

Canada 19 10 29

Japan 31 49 80

South Korea 12 25 37

Taiwan 0 9 9

United States 5 35 40

Advanced 67 (34%) 128 (66%) 195 (62%)

India 19 18 37

Indonesia 11 16 27

Kazakhstan 4 0 4

Malaysia 9 6 15

Pakistan 6 0 6

Philippines 1 1 2

South Africa 3 3 6

Sri Lanka 6 0 6

Thailand 7 8 15

Emerging 66 (56%) 52 (44%) 118 (38%)

Total 133 (42%) 180 (58%) 313

4 DBRS forCanadian rms (http://www.dbrs.com), Japan CreditRatingAgencyand R&I

rating agency for Japanese rms (http://www.jcr.co.jp/english/andhttp://www.r-i.co.jp/

eng/), KIS and NICE Rating Agency for South Korean rms (http://www.kisrating.com/

eng/ratings/hot_disclosure.asp and http://eng.nicerating.com), Taiwan Rating for

Taiwanese rms (http://www.taiwanratings.com), the EganJones rating for US rms

(www.egan-jones.com), CRISIL (http://crisil.com/ratings/credit-ratings-list.jsp), ICRA

(http://www.icra.in/CurrentRating.aspx), and CARE (http://www.careratings.com) for

Indian rms, Pendo Credit Rating Agency for Indonesian rms (http://new.pendo.

com/index.php),RFCAfor Kazakhstan rm (www.rfcaratings.kz), RAM credit rating for

Malaysian rms (http://www.ram.com.my), JCR-VIS for Pakistani rms (http://jcrvis.

com.pk/), PRSfor Philippine rms (www.philratings.com.ph), GCRfor South African rms

(http://globalratings.net/), RAM Ratings (Lanka) for Sri Lankanrms (www.ram.com.lk)

TRIS rating agency for Thairms (http://www.trisrating.com).

38 A. Ismail et al. / Review of Financial Economics 24 (2015) 3641
http://www.dbrs.com/http://www.jcr.co.jp/english/http://www.r-i.co.jp/eng/http://www.r-i.co.jp/eng/http://www.kisrating.com/eng/ratings/hot_disclosure.asphttp://www.kisrating.com/eng/ratings/hot_disclosure.asphttp://eng.nicerating.com/http://www.taiwanratings.com/http://www.egan-jones.com/http://crisil.com/ratings/credit-ratings-list.jsphttp://www.icra.in/CurrentRating.aspxhttp://www.careratings.com/http://new.pefindo.com/index.php),RFCAhttp://new.pefindo.com/index.php),RFCAhttp://new.pefindo.com/index.php),RFCAhttp://new.pefindo.com/index.php),RFCAhttp://www.rfcaratings.kz/http://www.ram.com.my/http://jcrvis.com.pk/http://jcrvis.com.pk/http://www.philratings.com.ph/http://globalratings.net/http://www.ram.com.lk/http://www.trisrating.com/http://www.trisrating.com/http://www.ram.com.lk/http://globalratings.net/http://www.philratings.com.ph/http://jcrvis.com.pk/http://jcrvis.com.pk/http://www.ram.com.my/http://www.rfcaratings.kz/http://new.pefindo.com/index.php),RFCAhttp://new.pefindo.com/index.php),RFCAhttp://www.careratings.com/http://www.icra.in/CurrentRating.aspxhttp://crisil.com/ratings/credit-ratings-list.jsphttp://www.egan-jones.com/http://www.taiwanratings.com/http://eng.nicerating.com/http://www.kisrating.com/eng/ratings/hot_disclosure.asphttp://www.kisrating.com/eng/ratings/hot_disclosure.asphttp://www.r-i.co.jp/eng/http://www.r-i.co.jp/eng/http://www.jcr.co.jp/english/http://www.dbrs.com/


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Withregard to opinion-based proxies, we usethe standard deviation

of the forecasted EPS (earnings per share). The standard deviation of

EPS is useful because the higher the degree of asymmetric information,

the more likely analysts and investors will differ in their assessments of

arm's future earnings, which translates into a higher standard devia-

tion of forecasted EPS. We eliminate all rms from our sample that are

monitored by only one equity analyst, because this would result in

zero standard deviation of forecasted EPS. Therefore, in regressions

that include the standard deviation of forecasted EPS as an explanatory

variable, our sample is reduced to 247 rms.

In order to test thedebt-signaling hypothesis, we measure the corre-

lation between DER and the magnitude of a rm's ratings split. Since

capital structure is considered an important measure of a rm's future

protability, DER should serve as an effective signal to the market

up to an upper bound, and after that, increasing a rm's debt level is

interpreted negatively by the market, since it increases bankruptcycosts beyond any added benet. To capture the optimal debt-signaling

idea we also include DER^2 as a variable, which is used to test for the

possibility of a quadratic relationship between DER and split ratings.

We also employ several control variables, such as rm size and

dummy-nance, that control for the industry effect arising from the

more opaque balance sheets ofnancial rms, and dummy-countries,

which are included to reduce any bias due to possible differences in

the ratings methodologies of national CRAs. We control for rm size be-

cause large rms areoften under more scrutiny from themarket,thus in-

creasing the informationow to investors. Finally, we use Japan as the

baseline for the dummy-country variables, and we categorize Canada,

Japan, South Korea, Taiwan and the United States as advanced economies

due to their high levels of per capita income, while the remaining coun-

tries in our sample are categorized as emerging economies.

3.3. Methodology

To test our hypothesis, we compare the results of the ordered

probitmodel with theresults of an Ordinary Least Squares (OLS) regres-

sion. Consequently, we use both OLS and the probit model when we

compare split ratings between advance and emerging economies,

which allows us to test the optimal debt-signal hypothesis more thor-

oughly. Using the emerging markets dummy variable (DEM), we divide

our samples into advanced market and emerging market sub-samples.

We further include interaction terms that consist of the emerging mar-

ket dummy variable multiplied by our independent variables, which we

use to distinguish between the effects of independent variables upon

our two sub-samples. If no signicant difference is discovered between

the two sub-samples, this would imply that asymmetric information

has a similar effect upon split ratings in both advanced and emerging

bond markets. However, if an interaction term is signicant,that implies

that the corresponding information proxy is signicant for one sub-

sample and not the other, and our results can thereby provide an effec-

tive measure of the effect of asymmetric information upon split ratings

across bond markets.

We specify our ordinal probit model as follows:

RATi 0 1EXPi 2EXPi DEMi 3CONi

(i) RATi denotes the magnitude of the ratings split, where RAT

equals 0if there are no split ratings, 1if the ratings are split

by one letter-level, 2 if the ratings are split by two letter-

levels, and 3if the ratings are split by more than two letter-

levels.

(ii) EXPidenotes the set of independent variables. Independent var-

iables are price-to-book ratio (PBR), price-to-earnings ratio

(PER), the debt-to-equity ratio (DER), debt-to-equity ratio-

squared (DER^2), and the standard deviation of forecasted EPS

(SDF).

(iii) DEMi is the dummy variable for emerging markets, where

DEM = 1 denotes rms from emerging markets, and DEM = 0

denotes advanced markets.

(iv) EXPi DEMiis the interaction term between explanatory vari-

ables and the emerging market dummy.

(v) CONidenotes the set of control variables. Control variables used

for this model are (i) the log of totalassets to capture thesize ef-

fect, (ii) dummy-nance to capture the difference between -nancial and non-nancial rms, and (iii) country-dummy

variables to reduce the biascaused by different rating methodol-

ogies used by national rating agencies.5

Finally, to ensure the integrity of our results, we calculate the corre-

lation coefcients for our independent variables, which are displayed in

Table 4. We nd no notable correlation coefcient in this matrix; and

hence multicollinearity is not a problem for our methodological ap-

proach. However, to further avoid the multicollinearity problem, we

use a step-wise regression procedure that allows us to evaluate the ex-

planatory power of each independent variable.

Table 3

Population and sample comparison (standard deviations are in parentheses).

Country Average PBR Average PER Average DER

Population Sample Population Sample Population Sample

Canada 1.90 1.47 (1.05) 16.24 13.57 (11.14) 0.85 1.13 (0.94)

Japan 0.90 0.98 (0.45) 21.90 28.20 (52.00) 1.81 1.75 (2.22)

South Korea 1.10 1.18 (0.94) 9.10 17.74 (15.82) 1.20 1.76 (1.77)

Taiwan 1.82 1.31 (1.17) 15.27 27.83 (31.19) 0.36 0.49 (0.39)

United States 2.70 3.62 (5.75) 17.40 35.66 (75.62) 2.12 0.84 (1.03)

Advanced 2.19 1.99 (4.37) 17.59 26.28 (52.24) 1.89 1.52 (1.98)

India 2.00 1.84 (1.63) 11.70 12.07 (7.31) 1.30 1.82 (1.68)

Indonesia 3.10 1.73 (0.87) 13.70 18.40 (21.89) 0.81 0.86 (0.63)

Kazakhstan 0.52 3.03 (2.60) 5.50 5.00 (7.12) 1.49 1.33 (1.21)

Malaysia 1.80 2.13 (0.83) 13.80 17.51 (11.93) 0.95 1.17 (0.84)

Pakistan 2.36 0.92 (0.44) 9.91 4.23 (2.24) 2.71 1.57 (1.07)

Philippines 2.87 2.20 (1.27) 17.37 11.90 (4.81) 0.83 0.59 (0.60)

South Africa 1.26 2.05 (1.12) 17.50 13.43 (2.03) 0.40 0.22 (0.25)

Sri Lanka 1.67 1.33 (0.64) 10.74 14.38 (16.46) 0.83 1.51 (1.34)

Thailand 2.40 2.41 (2.48) 12.40 11.80 (3.55) 1.37 1.69 (1.29)

Emerging 2.17 1.91 (1.49) 12.74 13.72 (13.06) 1.16 1.35 (1.28)

Note: Average populations for groups (advanced and emerging) is calculated as a weighted average, with GNI as the weight. Data for populationswas gathered from various sources in-

cluding IMF Financial Statistics, Bloomberg, the Tokyo Stock Exchange, the Japanese Ministry of Finance, and the Thailand Stock Exchange.

5

Seethe footnotesfor Tables 5 and 6 foran explanationof therole of country dummies.



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4. Results and discussion

Our results are organized in Tables5 and 6. Table 5 shows the results

of the ordered probit test for our information proxies regressed upon

our sample's split ratings, andTable 6displays the results of the probit

and OLS tests for the optimal debt-signal. Overall, our results support

the hypothesis that debt-signaling is effective for emerging market

rms, but less so for rms in advanced bond markets. Our results also

provide evidence for optimal debt-signaling in emerging bond markets.

In order to more thoroughly test our hypothesis we employ a step-

wise ordered probit regression method. As can be seen in Table 5,the

independent variables (PBR, PER, DER, and the standard deviation of

forecasted EPS) are included in separate regression models. We start

with a baseline model that consists only of control variables (column

1) and successively add independent variables in a step-wise fashion

in order to discriminate between the effects of each independent vari-

able. Our analysis is divided into models that contain no interaction

terms and models that contain interaction terms, which captures the

difference between the independent variables' explanatory power for

advanced and emerging bond markets respectively.

4.1. Split ratings and asymmetric information

Columns 2 and 3 in Table 5show that PBR is only signicant for

emerging markets, since PBR is not signicant by itself, but the

PBR DEM interaction term is signicant at the 10% level. Accordingto columns 4 and 5, PER is not signicant for either the advanced or

emerging economies. By contrast, columns 6 and 7 support the debt-

signaling hypothesis, since DER is not signicant for the entire sample,

while the DER DEM interaction term is signicant at the 1% level,

which implies that DER is signicant only for emerging markets. The

negative sign of the interaction term also meets our expectations,

since a higher DER is associated with a lower ratings split in emerging

markets. Columns 8 and 9 show that the standard deviation of forecast-

ed EPS is not signicant for explaining split ratings, in either the whole

sample or in emerging markets. Most importantly however, the multi-ple regression test displayed in column 10 afrms the negative sign of

DER at the 1% signicance level for emerging markets. Consequently,

only DER is signicant at the 1% level foremerging markets, which sup-

ports the debt-signaling hypothesis.

4.2. The optimal debt-signal

Table 6shows the results of the ordered probit and OLS regression

tests for the optimal debt-signal hypothesis. Column 1 ofTable 6dis-

plays the results of the ordered probit test for the entire sample using

DER and DER^2, which reveals that neither variable is statistically

signicant. The results presented in column 1 may be biased towards

rms in advanced markets, and for this reason the next regression

(presented in column 2) includes the DER DEM and DER^2 DEM

interaction terms. Their inclusion allows us to distinguish between the

effect of debt-signaling upon emerging and advanced bond markets.

Note that DER DEM is signicant at the 1% level and its coefcient

has a negative sign, while DER^2 DEM is signicant at the 5% level

and its coefcient has a positive sign. The signs and signicance of the

interaction terms indicate that the relationship between DER and the

split ratings in our sample is quadratic (U-shaped), which thereby pro-

vides evidence for optimal debt-signaling. Our hypothesis is also sup-

ported by the multivariate ordered probit test in column 3, that

includes all independent variables as well as the interaction terms,

whose signs and signicance are therein conrmed.

The results of the OLS test inTable 6provide further evidence for

the optimal debt-signal. DER and DER^2 are signicant for the entire

sample, with the expected U-shape curvature. Note that the OLS test

also yields a negative sign for DER*DEM at the 5% signicance level,while the coefcient for DER^2 DEM is positive, which again suggests

Table 4

Correlation matrix.

Log

(rm size)

PBR PER DER Std. deviation of

forecasted EPS

(SDF)

Log (rm size) 1 0.151 0.069 0.004 0.153

PBR 1 0.044 0.149 0.040

PER 1 0.039 0.043

DER 1 0.060

Std. deviation offorecasted EPS (SDF)

1

Table 5

Ordered probit regression: information asymmetry and split ratings.

Variables 1 2 3 4 5 6 7 8 9 10

Log (rm size) 0.053 0.055 0.058 0.051 0.049 0.000 0.003 0.104T 0.225** 0.049

(0.039) (0.039) (0.048) (0.039) (0.049) (0.029) (0.031) (0.059) (0.039) (0.048)

D. Finance 0.398*

(0.158)

0.408*

(0.161)

0.391*

(0.151)

0.391*

(0.158)

0.411**

(0.148)

0.292

(0.151)

0.285

(0.148)

0.434*

(0.182)

0.220

(0.148)

0.418**

(0.158)

PBR 0.010

(0.032)

0.041

(0.031)

0.065

(0.247)

PBR DEM 0.175

(0.095)

0.168

(0.099)

PER 0.001

(0.001)

0.001

(0.001)PER DEM 0.007

(0.014)

DER 0.011

(0.038)

0.065

(0.057)

0.085

(0.054)

DER DEM 0.358**

(0.122)

0.361**

(0.120)

Std. dev. of forecast (SDF) 0.000

(0.000)

0.000

(0.000)

SDF DEM 0.014

(0.019)

Pseudo R2 0.29 0.29 0.29 0.29 0.29 0.28 0.30 0.29 0.22 0.31

N 313 313 313 313 313 313 313 247 247 313

for 10%, * for 5%, ** for 1%. Standard errors are in parentheses.

Note: We consider only letter rating, not the negative or positive sign (for example: AA+, AA, and AAare considered equal). Split rating by one level represents one letter difference

(for example AA and A), split rating by two levels represents two letter differences (for example AA and BBB). We also include country dummy variables in each model, with Japan as

the baseline, to capture the effect of national rating methodologies. The coefcients for country dummies are not reported for the sake of brevity.

40 A. Ismail et al. / Review of Financial Economics 24 (2015) 3641


6/6

a U-shaped relationship between DER and the split ratings dependent

variable. Thus, while DER and DER^2 are not signicant for Canada,

Japan, Korea, Taiwan and the United States, the optimal debt signal is

signicant for emerging markets. Empirically, these results imply that

increasing the DER reduces split ratings to a minimum, after which ac-

cumulating more debt will exacerbate the ratings split.

Based on our results, we can conclude that (1) the debt-to-equity

ratio is an effective proxy for asymmetric information between rms

and CRAs; (2) in advanced bond markets, asymmetric information is

relatively low, so the debt-signal is not effective in reducing the ratingssplit; (3) in emerging markets, information about rm quality is more

scarce, and so the debt-signal has a measurable effect upon the ratings

split;and nally, (4) the resultsof the probit and OLStests imply theex-

istence of an optimal debt-signal in emerging markets that can mini-

mize the ratings split between national and international CRAs.

5. Conclusion

Because DER is only signicant for emerging markets, our research

suggests that in emerging bond markets, the debt-signal can cause

CRA beliefs to converge so as to diminish the ratings split. However,

this result is not replicated in advanced bond markets. Our research fur-

ther suggests that rms can adopt an optimal capital structure that di-minishes the effect of asymmetric information on the price discovery

process for their respective bonds. Our results also provide avenues

for future research. For instance, if rms in emerging markets can

adopt an optimal debt-signal, this should not only minimize the ratings

split, but should also have some measurable impact upon bond yields. If

the optimal debt-signal is indeed effective in mitigating asymmetric in-

formation in emerging markets, then it should narrow the difference

between bond yields for comparable rms across bond markets. In

this manner, our research can contribute more fundamentally to the

xed-income literature in the future.

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Table 6

Ordered probit and OLS regressions: Debt-signaling and split ratings.

Ordered probit

Y = 0 if there is no split

=1 if split by 1 letter

=2 if split by 2 letters

=3 if split by N 2 letters

OLS

Y = national rating international rating

1 2 3 4 5 6

Log (rm size) 0.053 0.062 0.139* 0.296* 0.297* 0.283*

(0.039) (0.040) (0.068) (0.119) (0.124) (0.125)

D.nance 0.385* 0.381* 0.448** 0.566 0.480 0.432

(0.161) (0.183) (0.171) (0.328) (0.334) (0.348)

DER 0.056 0.152 0.069 0.546* 0.135 0.168

(0.083) (0.099) (0.130) (0.225) (0.215) (0.215)

DER^2 0.006 0.010 0.003 0.067* 0.026 0.030

(0.008) (0.008) (0.017) (0.029) (0.031) (0.031)

DER DEM 1.135** 1.159** 1.460* 1.507**

(0.312) (0.357) (0.570) (0.578)

DER^2 DEM 0.155* 0.163* 0.186* 0.192*

(0.067) (0.069) (0.079) (0.079)

PBR 0.129 0.144

(0.117) (0.093)

PBR DEM 0.205 0.260

(0.150) (0.173)

Pseudo R2/adjusted R2 0.29 0.31 0.34 0.57 0.59 0.60

N 313 313 313 313 313 313

for 10%, * for 5% and ** for 1%. Standard errors are in parentheses.

Note: We consider only letter rating, not the negative or positive sign (for example: AA+, AA, and AAare considered equal). split rating by one level represents one letter difference

(for example AA and a), split rating by two levels represents two letter differences (for example AA and BBB). We also include country dummy variables in each model, with Japan as

the base, to capture the effect of national rating methodologies. The coefcients for country dummies are not reported for the sake of brevity.

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Split Ratings and Debt-signaling in Bond Markets- A [email protected]

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