A Data Envelopment Analysis of Shipping Industry Bond Ratings
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Tamkang Journal of Science and Engineering, Vol. 9, No 4, pp. 403-408 (2006) 403
A Data Envelopment Analysis of Shipping Industry
Bond Ratings
Gin-Shuh Liang1, Chin-Feng Liu1, Wen-Cheng Lin1* and Chen-Huei Yeh2
1
Department of Shipping and Transportation Management, National Taiwan Ocean University,
Taoyuan, Taiwan 330, R.O.C
2
Yang Ming Marine Transport CORP.
Taiwan, R.O.C
Abstract
Industrial corporate bonds have been assigned quality ratings since the early 1900s. Moody and
Standard & Poors (S&P), two renowned ratings organizations assign ratings to a portion of new bonds
issued each year. However, many businesses and industry leaders have doubts about the consequences
of bond ratings. This paper attempts to build an objective and user-friendly bond ratings approach for
the shipping industry and investors. Data envelopment analysis (DEA) is employed to evaluate the
corporate bond ratings of Taiwan’s shipping industry from 1997 to 2004, by applying two input
variables (fixed assets and debt ratio) and two output variables (fixed assets turnover and times interest
earned) as rating factors. The results show that 4 different bonds, in particular, have had relatively high
ratings: ETITC’s issue of bonds from 1998 to 2003, EVERGREEN’s bond issue in 1996, and Yang
Ming’s bond issue in 2003, respectively. This paper also illustrates that ETITC and Yang Ming have
paid more attention on reducing default risks and creating revenue competency during the given time
period.
Key Words: Bond Rating, Data Envelopment Analysis (DEA), Default Risks
1. Introduction The main purpose of bond ratings is reach an effective
evaluation as to the ability and legal obligations of an is-
The Bond market is an integral part of global finan- suer to make timely payments of principal and interest on
cial system. Several handbooks (e.g. Fridson [1]; Fabozzi a security over the life of the instrument. A bond rating is
and Cheung [2]; Altman [3]) examine credit analysis; also designed to rank, within a consistent framework, the
however, no explicit link is made with regard to bond rat- relative risk of each debt issue and issuer. Because it per-
ings. Industrial corporate bonds have been assigned qua- tains to the future, a credit rating (like all other long-term
lity ratings since the early 1900s. Several international financial analyses) is necessarily subjective. When an is-
private organizations (such as Moody and S&P) have suer and each debt issue value the pricing of bonds, a
been assigning ratings to a portion of new bonds issued bond rating is also the major determinant for corporate
each year. Besides the limited scope of those bond rat- managers on the pricing spread of bond offerings (Gram-
ings, some industry executives and investors alike have menos and Arkoulis [4]). In arriving at an issue’s rating,
not had complete confidence in the effectiveness of such international rating organizations typically stress the ex-
bond ratings. Thus, many experts have expressed a need amination of the specific circumstances of each issuer
for an impartial and reliable ratings model that might and each debt issue. A long-term view is adopted that ex-
provide useful information for investors and managers. tends beyond a brief earnings period. The foundation of
their rating methodology constructs a basic question:
*Corresponding author. E-mail: d9273001@mail.ntou.edu.tw What is the level of risk associated with receiving timely404 Gin-Shuh Liang et al.
payment of principal and interests of this specific debt ings procedures are somewhat complicated, unclear, and
security, and how does the level of risk compare with that somewhat incredible to many investors and issuers. There-
of all other debt securities? fore, this paper attempts to construct a clear, logical and
The risk to timely payment is measuring the ability comprehensible ratings procedure. Present ratings orga-
of an issuer to generate cash in the future. Of particular nizations usually use related financial indicators to mea-
concern is the ability of management to sustain cash gen- sure bond ratings in the shipping industry, which often
eration in the face of adverse and ever-changing circum- leads to a problem of the weight assignment for each in-
stances in today’s business environment. Generally, the dicator. The financial ratio method can be an appropriate
greater the predictability of an issuer’s cash flow, the method when firms use a single input or generate a single
higher the issuer’s ratings will be designated. Although output. However, as with many firms, it is necessary to
rating organizations have a deliberate rating process, bond employ various inputs to provide a variety services (out-
ratings have not necessarily been based on any one de- puts). Which ratio is selected becomes an issue of evalu-
fined set of numbers or rating index or financial ratio. A ators when a great number of related financial indicators
uniform set of financial ratios that are consistently in- are involved. One type of solution used is to aggregate
sightful across all industries has yet to be established. the average among all indicators in order to integrate a
More specifically, the shipping industry has often found single measurement. In particular, the DEA approach
it necessary to issue debts to cope with operational condi- can be applied to solve the above-mentioned weight as-
tions. Building a clear-cut indicator of bond rating is of signment dilemma. This approach draws on a mathemat-
great urgency as it would not only would help the shipping ical programming method to generate a set of weights for
industry improve financial profitability, but would also each indicator. While it considers how much ratings effi-
help investors make more reliable investment decisions. ciency could be improved, the DEA approach also ranks
This paper is divided into five sections. The next sec- the ratings efficiency scores of individual firms. Section
tion is a brief review of bond ratings and previous re- three will introduce the DEA methodology in more detail.
search. In Section three the DEA and data are discussed. A number of previous studies have related bond rat-
In Section four DEA results are presented, and finally, ings to the frequency of default. Much literature all indi-
Section five presents our conclusions. cates that some relationship exists between bond ratings
and historical records of bond default (Harold [5]; Hick-
2. Literature Review man [6]; Atkinson and Simpson [7]; Altman [8]).
Much literature revisits the accuracy and validity of
Bond ratings attempt to provide a simple measure of Moody’s credit ratings by using a multiple regression mo-
the relative investment quality of securities. Moody’s em- del, discrimination analysis, or probit model. Horrigan
ploys nine different ratings (from Aaa to C); S&P em- [9] attempted to predict the top six bond classifications
ploys twelve different ratings (AAA to D). Medium and for Moody’s by employing a multiple regression model.
high grade bonds (Aaa, Aa, A and Baa for Moody’s and He concluded that a model containing six variables (sub-
AAA, AA, A and BBB for S&P) are considered “invest- ordination, total assets, working capital/sales, net worth/
ment” class bonds. There are two noted bond ratings total debt, sales/net worth, and net operating profit/sales)
organizations in Taiwan: the Taiwan Economic Journal could predict approximately 58% of Moody’s bond rat-
(TEJ) and the Taiwan Ratings Company. The ratings cri- ings. West [10] made use of the model developed by
teria are based on qualification factors and quantification Fisher and attempted to predict the first six of Moody’s
factors. Qualification factors include a company’s norm, bond ratings. He employed four variables (the logari-
record of past payment of interest, management compe- thms of earnings variability, period of solvency, market
tency and ethics (morality?), market share, the effect ran- value of stock/debt, and market value of all bonds out-
ge of prosperity, industry vision and payment method. standing) in a multiple regression model. He was able to
Quantification factors are reflected by solvency and op- predict approximately 62% of the actual ratings. Bel-
erational conditions of the issuer, profitability, coverage kaoui [11] used discrimination analysis to build a rating
analysis and asset efficiency. However, these bond rat- model, and concluded that a discrimination model con-A Data Envelopment Analysis of Shipping Industry Bond Ratings 405
taining eight variables could predict approximately 66% ficient if it cannot increase any output or reduce any in-
of S&P bond ratings. Although discrimination analysis put without reducing other output or increasing any other
has higher accuracy than the regression model, it must input. An efficient bond can enjoy higher rating scores of
hypothesize that each variable must be a multiple normal unity, while an inefficienct bond would receive DEA
distribution assumption, and possibly caused an increase scores of less than unity.
in erroneous classification. In order to solve this prob- Here, we denote the maximum efficiency as Ek, Ykj
lem, much literature has suggested using the Probit mo- as the jth output of the kth DMU and Xki as the ith input of
del to build a bond rating model. Diertrich [2] used the the kth DMU. If a DMU employs p input to produce q
Probit and regression models to build such a ratings mo- output, the score of kth DMU, Ek, is a solution from the
del, and he concluded that the Probit model containing fractional linear programming problem:
three variables (debt ratio, operation cash flows, and sales
q
growth) has higher accuracy than the regression model.
Still much literature (Goh and Ederington [12]; Dichev
åU Y
j =1
j kj
Ek = Max p
i = 1, 2, ..., p j = 1, 2, ..., q
and Piotroski [13]) believes the change of bond ratings
has positive relations with the profitability and perfor- åV X
i =1
i ki
mance of stock. From an examination of related articles
q
on the prediction of bond ratings, the accuracy of current
rating systems seems too low and not valuable as a true
åU Y
j =1
j kj
s.t. p
£ 1 r = 1, 2, ..., k, ..., R
reference. However, we can examine the positive rela-
tionship with bond ratings and stock investment perfor-
åV X
i =1
i ki
mance and profitability from past literature. Bond ratings
Uj, Vi ³ e > 0 " i, j
affect investment decisions for investors and can be an
improvement indicator for operational performance. If Where Uj and Vi are the variable weights in the jth
we build a clear, easy, and credible bond rating system, output and the ith input, respectively, the former model
such a clear procedure of bond ratings would greatly re- can be reformulated to the problem, which provides va-
lieve both investors and bond issuers. Thus, this paper luable information about the cost benefits:
would attempt to build such a clear and plausible
bond-rating model by using data envelopment analysis. p q
Min TE = q - e (å S ki- + å S kj+ )
i =1 j =1
3. Methodology
R
Charnes, Cooper, and Rhodes [14] were the first to
s.t. ål X
r =1
r ri - q X ki + S ki- = 0
propose the DEA methodology as an evaluation tool for R
decision units. DEA has been applied successfully as a ål Y
r =1
r rj - S kj+ = Ykj
performance evaluation tool in many fields including ma-
nufacturing, academic institutions, banks, pharmaceuti- lr ³ 0, S ki- ³ 0, S kj+ ³ 0, " i, j, k, r
cal firms, small business development centers, and nurs-
ing home chains. Here, we apply this method to bond rat- Where q is the efficiency rating score, lr is the solu-
ings. DEA is a non-parametric approach for evaluating tion weight and e is a non-archimedean quantity which is
the relative efficiency of decision-making units (DMUs) very minute. We can calculate the relative efficiency rat-
using multiple inputs to produce multiple outputs. The ing score from the above model and further estimate the
basic idea of DEA is to identify the most efficient deci- targeted value for each output/input of each bond. That
sion-making unit (DMU) among all DMUs. The most ef- is: X ki = q * X$ ki - S ki-* and Y$kj = Y kj - S kj+* where q* is the
ficient DMU is called a Pareto-optimal unit and is con- solution of q, S kj+* , S ki-* are the solutions of S kj+ and S ki- , re-
sidered the standard for comparison for all other DMUs. spectively. X$ ki and Y$kj represents the targeted value for
That is to say, a single firm is considered DEA Pareto ef- the input/output of kth bond. Xki and Ykj means the corre-406 Gin-Shuh Liang et al.
sponding actual value of bonds. total of 10 bonds were available for analysis. The distri-
According to the bond ratings of the Taiwan Econo- butions of these samples are presented in Table 1.
mic Journal (TEJ), this paper follows their criteria in- We can see the above samples, most shipping com-
cluding qualification factors and quantification factors. panies issue five-year period bonds. These bonds include
Because qualification factors are hard to get and mea- convertible bonds, which are more attractive for inves-
sure, we have chosen quantification factors to serve as tors to buy and they are easier to issue. Because the ship-
measurement indicators. Quantification factors include ping industry is capital-intensive, high in debt, with high
solvency and operational conditions of the issuer, profit- financial risk, unsteady in income, and highly affected by
ability, coverage analysis and asset efficiency. According oil prices and exchange rates, the rank results of evalua-
to financial statement analysis, long-term liabilities make tion bond risks are undeniably important criteria for in-
use of long-term assets. Thus, we could examine the turn- vestors and managers. Table 2 shows the input/output
over of long-term assets. Generally, fixed assets turnover variables of 10 issuer bonds.
is used to measure how well a corporation is creating We find ETITC has higher debt ratio than other com-
sales and profits. With a sound capital structure, a corpo- panies, its debt ratio had exceeded fifty percent in 1997,
ration will have a lower debt ratio, which stands for low- 1998 and 2003. It shows ETITC has had bad capital st-
er default risk. Without enough earnings, though, a cor- ructure. Because of the shipping industry is so capi-
poration would not produce enough revenue to pay back tal-intensive and with high debt characteristics, issuers
long-term liabilities. Moreover, long-term solvency has a must pay much more attention specifically to their debt
positive relationship with earnings. In general, the ratio conditions. Table 3 shows in the first analysis, four vari-
of times interest earned is used to measure long-term sol- ables (2 outputs and 2 inputs) were used in DEA. The es-
vency. So this paper selected two input variables: fixed timated efficiencies for the 10 issued bonds in Taiwan,
assets and debt ratio. Considering the direct relationship along with their rank orders, are shown in Table 3. As ex-
between input variables and output variables, we chose plained before, these efficiencies were computed for each
fixed assets turnover and the ratio of times interest earn- bond after taking into consideration the inputs and out-
ed as output items. puts of all 10 issued bonds in the set. Hence these effi-
Fixed assets are tangible long-term assets used in the ciencies are relative ratings. Moreover, the high rating
continuing operation of the business. They represent a bonds (whose efficiency = 1) were used as the bench-
place to operate (land and buildings) and the equipment mark. Therefore, these rating results represent relative-
to produce, sell, deliver, and service the company’s goods. to-best efficiencies.
They are therefore also called operating assets or, some- Now, with such results, we can examine DEA effi-
times, tangible assets, long-lived assets, or plant assets; ciency. To begin with, ETITC has higher rating results
debt ratio, which shows the proportion of the company from 1997 to 2003, where its rating efficiency value is
financed by creditors in comparison with that financed from 0.400679 in 1997 to 1 in 1998 and 2003. This dem-
by stockholders. Fixed assets turnover is computed by
dividing net sales by average fixed assets; times interest
Table 1. Distribution of samples
earned is computed by dividing earnings before interest
expenses and taxation by interest expenses. Shipping firms Issue date Period (year)
ETITC 1997/01/14 5
4. DEA Analysis ETITC 1998/07/10 0.3.4
ETITC 2003/03/17 00.1.25
EVEGREEN 1996/11/27 5
All shipping industrial corporate bonds listed in the EVEGREEN 1998/10/15 5
new issue section of the Taiwan Economic Journal (TEJ) EVEGREEN 1999/01/25 5
from the year 1997 to 2004, were initially selected for the Evegreen Internation 2004/09/16 5
study. After eliminating duplicate bonds (those issuing YML 1997/08/02 7
YML 2003/08/07 5
more than one bond during the same period) and verify-
WAN HAI 2003/01/27 5
ing that all desired financial information was available, aA Data Envelopment Analysis of Shipping Industry Bond Ratings 407
Table 2. Input/output variables of 10 issuer bonds
Inputs Outputs
Bonds Issuer fixed assets: debt ratio fixed assets times interest
thousands dollars (%) turnover (%) earned
ETITC 1997 2,695,679 56.47 1.40 8.35
ETITC 1998 2,768,951 65.46 1.97 306.47
ETITC 2003 2,631,240 60.79 3.85 3.41
EVERGREEN 1996 22,603,348 35.67 1.33 639.95
EVERGREEN 1998 21,337,448 43.64 1.23 162.50
EVERGREEN 1999 19,784,644 76.67 1.06 171.92
Evergreen International 2004 10,999,218 16.10 0.42 17.45
Yang Ming 1997 26,872,811 45.82 1.23 285
Yang Ming 2003 13,208,046 45.32 4.76 13.45
WAN HAI 2003 12,518,600 39.99 3.24 67
Table 3. Rating results of firms using DEA Evergreen International. This table shows the amount of
Bonds Issuer DEA Efficiency Ratings rank slack in each of the controllable input and output obser-
ETITC 1997 0.400679 7 vations for this firm. This slack is computed by compar-
ETITC 1998 1 1 ing the input and output of Evergreen International with
ETITC 2003 1 1 inputs and outputs of its efficient reference firms. Ever-
EVERGREEN 1996 1 1 green International can become efficient (increase effi-
EVERGREEN 1998 0.400132 8 ciency from 0.284904 to 1.00) by decreasing an input by
EVERGREEN 1999 0.295922 9
corresponding slack. Its reference sets are similar to the
Evergreen International 0.284904 100
2004 financial situation of Yang Ming in 2003 and to the fi-
Yang Ming 1997 0.477778 6 nancial situation of EVERGREEN in 1996. Table 4
Yang Ming 2003 1 1 shows Evergreen International can decrease fixed as-
WAN HAI 2003 0.839316 5 sets $1,484,834 (such as transportation, stevedoring and
ship facilities etc.). This paper suggests Evergreen Inter-
onstrates that ETITC paid more attention on reducing de- national could get higher bond ratings by reducing ineffi-
fault risks and creating revenue competency. The rating ciency fixed assets.
results of Yang Ming are also the same in progress. How-
ever, EVERGREEN, despite its worldwide class ship- 5. Conclusion
ping status had a negative rating result progressively
worse from rating efficiency value 1 in 1996 to rating ef- This paper employs data envelopment analysis to
ficiency value 0.295922 in 1999. Such results illustrate evaluate the bond ratings of the shipping industry in Tai-
that EVERGREEN must start to pay more attention to wan from 1997 to 2004, after eliminating duplicate bonds
improving their debt conditions progressively. (those issuing more than one bond during the same pe-
A closer look at each of the inefficient bonds can be riod) and verifying that all desired financial information
taken by sensitivity analysis at each firm level. For ex- was available, where a total of 10 bonds were available
ample, Table 4 shows the sensitivity analysis results for for analysis. The estimated results show that 4 bonds
Table 4. Sensitivity analysis of evergreen international
Variable Name Estimated Weight Value Measured Value if Efficient Slack
Fixed assets turnover 0.5850 00.42 00.42 0
Times interest earned 0.0022 17.45 17.45 0
Fixed assets 0.0621 10,999,218 3,133,721 1,484,834
Debt ration 2.2369 16.10 4.586962 0408 Gin-Shuh Liang et al.
have relatively high ratings, and that there is a rather high Companies,” Transportation Research part E, Vol.
level of overall efficiency. High efficiency is demonst- 39, pp. 459-471 (2003).
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