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Journal of Accounting,Auditing & Finance
1(1) 1–27� The Author(s) 2011
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DOI: 10.1177/0148558X11409156http://jaaf.sagepub.com
Multiobjective CapitalStructure Modeling: AnEmpirical Investigation ofGoal Programming ModelUsing Accounting Proxies
Yamini Agarwal1, K. Chandrashekar Iyer1, and Surendra S. Yadav2
Abstract
Capital structure decisions (CSDs) have become complicated in this exceeding competitivebusiness environment. Theories and models of 1950s are unable to incorporate thedemands faced by the decision maker. New models are needed to incorporate multipleobjectives and constraints. Stakeholders are awfully demanding. Practitioners attempt toinnovatively build the capital structures to meet the needs of all stakeholders. Off and onbalance sheet exposure contributes to financial commitments. In the light of this back-ground, the present study investigates the Indian corporates for their capital structurechoices and builds a goal programming model for CSDs. Capital structure practices in Indiaare studied through a sample of top 500 companies classified in 19 industries over 10 yearperiod (1998-2007). Accounting ratios (67) are used to define the multiple considerationsbefore a decision maker. The study has also explored the relationship of leverage ratio withmarket capitalization and earnings per share (EPS). Using a questionnaire approach, the pre-mise of multiple objectives for CSD is evaluated. Chief financial officers (CFOs) as respon-dents are investigated for their goals, priorities, motivations, constraints, and capitalstructure practices. The study has attempted to develop a goal programming (GP) modelfor providing satisficing solutions to multiple goals simultaneously by minimizing the devia-tion from the objective function after assuming that the decision maker is an optimist anddoes not attempt to satisfy all objectives fully. GP model has been developed and illustratedfor CSDs through agriculture-based firm having multiple objectives that are proxied usingaccounting variables.
Keywords
capital structure decisions, multicriteria decision making, Indian corporates, goalprogramming model
1Indian Institute of Finance, Delhi, India2Indian Institute of Technology, Delhi, India
Corresponding Author:
Yamini Agarwal, Indian Institute of Finance, Ashok Vihar, Phase-II, Delhi, India
Email:yagarwal@iif.edu
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Introduction
Theories and principles on capital structure decisions (CSDs) developed in 1950s have lost
their relevance in today’s globalized interlocked dynamic financial world. In 1950s,
Modigliani and Miller (MM) in their path breaking work did not perceive and inculcate the
complexities, risk, and uncertainties, which are posed by the emergence of a new financial
architecture. The financial architecture has globally integrated electronic finance, privatiza-
tion, and liberalization in different economies. Sixty years after the work of MM, the size,
magnitude, complexity in the number of instruments, and international capital flows
(inflows/outflows) have increased multifold (Merton, 1995). The world has now become a
global village, and firms have access to global and domestic financial markets and instru-
ments. Challenges before a firm motivates or constraints financial and nonfinancial actions
that contribute to costs. Firms are constantly challenged by conflicting goals, agency prob-
lems, financial innovations, globalization, competitive pressures, social responsibility mea-
sures, environmental consciousness, financial costs, value creation, and many other
tangible and intangible issues. Adaptability to change cost structures of a firm form an inte-
gral part of CSD-making process. Management perceptions and the economic environment
further complicate the CSD process. The priorities of a firm change with the changing
times and over its life.
Empirical behavioral studies indicate that firms pursue multiple considerations while
determining their capital structures. However, no attempt has been made so far to provide
for a deeper understanding of such considerations as goals or constraints, their priorities,
and the relevance to the Indian Industry. CEO’s or a firm’s decision is based on an overall
assessment of the situation which at times apparently appears to lack economic rationale.
These considerations and their dimensions are not always quantifiable and readily accessi-
ble. A firm’s ability to choose a specific alternative in its capital structure is a matter of
judgment and may remain a mystery for most researchers (Welch, 2004). Such mysteries
can be resolved if the firm’s goals and constraints can be quantitatively and qualitatively
developed to arrive at optimizing or satisficing solutions, for any given economic rational-
ities and realities.
Multiobjective framework in today’s dynamic corporate environment emerges from the
constraints and goals that pose the need for a sensitive CSD model. There is a need for a
new model framework that accommodates the changing environment and gives results
which satisfy all wants. The role of a decision maker is indispensable for the choice of
goals, their priorities, and in the selection of an optimal solution. Decision maker, however,
is constrained by his own perceived and existing external environment. This restricts the
decision maker to choose a solution that is ‘‘satisficing’’ for multiobjective criteria as
against an optimal solution for a single objective.
The study develops a goal programming (GP) model that provides for satisficing solu-
tions to the multiobjective framework in which a decision maker is forced to exist. This
article illustrates the use of a new capital structure model on an Indian Firm. The model is
developed using a GP approach to decision making with accounting information. The eco-
nomic, industry, and company-specific analysis of the capital structure practices is con-
ducted with an Indian backdrop using a sample of top 500 listed Indian firms classified
into 20 industries (see Appendix A) ranked by a popular financial daily The Economic
Times in the year 2007. Company statistics on leverages over 10 years for the Indian
Industry is assessed through long-term debt-to-equity ratio (LTD) and total debt-to-equity
ratio (TDE). Behavioral dimensions of decision making for capital structures among Indian
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chief financial officers (CFOs) are assessed using a questionnaire approach that contains 19
questions and subquestions (Y. Agarwal, Iyer, & Yadav, 2009). We identified 96 qualitative
and quantitative considerations (Iyer & Agarwal, 2007) which a CEO/CFO evaluates simulta-
neously for CSDs. These considerations based on empirical investigation were narrowed to
67 quantitative variables using accounting information (see Appendix B). Interrelationships
between the leverage variables and 66 other variables of 19 industries form the leverage con-
straint (for TDE see Appendix C and for LTD see Appendix D) using stepwise regression.
Other firm-specific constraints are also developed using a stepwise regression method. Goals
for a firm are identified after discussions with the management and quantitatively developed
using accounting information. A GP model for CSDs under multiple objectives is then devel-
oped using these goal and constraints. The model is illustrated using a real life case study of
an Indian firm, namely, a1 operating in agriculture sector.
Data Compilation
The top 500 companies were divided into 20 industries (see Appendix A). Among the 20
industries, finance industry (consisting of 56 companies) was not considered for evaluation
as it contained banks, nonbanking financing companies (NBFCs), and financial institutions
that are governed by the banking guidelines identified and issued by Reserve Bank of
India. Capital Market Online database for Indian companies was used to compile the data
for 10 years (from 1998 to 2007) for 67 variables. There were 10 companies for which the
data were either incomplete or not available or incompatible for use. After removing the 56
finance companies and 10 not available companies, the sample size contained only 434
firms. A 10-year period from 1998 to 2007 is selected for study of 67 variables including
leverage variables that were used to develop possible relationships that define goals and
constraints for a firm. These relationships also assess the influence over the variables (TDE
and LTD) that proxy capital structure. The study assesses whether leverages differ over
time and across industries. The study also assesses the correlation between the leverage
variables and other variables. Furthermore, whether these leverage ratios follow a normal
probability distribution is assessed on time and industry classifications.
The questionnaire with 19 questions was sent to these 434 companies. The survey results
observed and published (Agarwal et al., 2009) are used to develop an empirical evidence
that multiple considerations exist simultaneously that influence CSD. Among all existing
financial models, GP technique was identified as an application tool that can handle multi-
ple objective and constraints simultaneously. A case study was developed to illustrate the
use of GP model for CSDs under multiple objectives.
Literature Review
In the past six decades, the field of CSDs has enlarged the dimensions of the influencing
factors or acceptable variables, which decide the capital structure choices. In the earliest
works we can find Harris (1954) did not initially restrict the definition of capital structures.
He identified CSDs to support long- and short-term activities of business by making good
any shrinkage in the asset values and decisions that provide necessary support for credit
availability and banking solvency. Later, Dobrovolsky (1955) restricted its impact as deci-
sion that minimized cost besides raising funds. Value of the firm became synonymous to
capital structure choices with the work of Durand (1959). Since then, the works have con-
tributed to how different factors influence the value of a firm when the firm undertakes a
decision for financing its activities, it included the work of Modigliani and Miller (1958,
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1963). Optimal capital structure and value of the firm is a concept for debate for over
decades. The works of Schwartz (1959), Schwartz and Aronson (1967), Rao (1989), Singal
and Mittal (1993), Rajan and Zingales (1995), Bahng (2002), Mohnot (2000), Miao (2005),
Das and Roy (2007), and Iyer and Agarwal (2007) identified optimal capital structures that
were constrained by industry dynamics and were studied with a background of a single
objective—the value of a firm. Given the various levels at which optimality of capital
structures has been studied under the single objective framework of value of a firm, multi-
ple objectives are classified under two heads of cost and benefits derived from a decision
of the financing structure. To investigate the cost and benefits associated with financing
decisions, the investigations have been spread over industries, countries, institutional frame-
works, political divides, different ownership firms, and many others.
Costs associated with capital structure of a firm are largely influenced by the proceeds
generated from an issue of a financial instrument. Factors that influence the issue of debt,
equity, and other instruments and their influence on the firm and its stake holders have to
be investigated in different regions from different viewpoints. Likewise, the contributions
of Jensen and Meckling (1976); Leland and Pyle (1977); Korajczyk, Lucas, and McDonald
(1991); Matthew (1991); Gertner, Scharfstein, and Stein (1994); Neto and Marques (1997);
Bolton and Von Thadden (1998); Subrahmanyam and Titman (1999); Kumar (2000);
Almeida and Wolfenzon (2006); Verschueren and Deloof (2006); Dittman and Thakor
(2007); and Helwege, Pirinsky, and Stulz (2007) have studied institutional frameworks that
identified agency costs, agents’ self-motivated objectives, ownership objectives, and trans-
parency objectives as factors that decide the debt equity mix.
Furthermore, studies that concentrated on the cost advantage of cheap source of financ-
ing or adjustment cost and increase in profitability included the works of Jalilvand and
Harris (1984); Myers and Majluf (1984); Myers (1984); Titman and Wessels (1988);
Fischer, Heinkel, and Zechner (1989); Chatrath, Kamath, Ramachander, and Chaudhary
(1997); Kakani (1999); Altinkilic and Hansen (2000); Roberts (2001); Pandey (2002);
Fama and French (2002); Welch (2004); and Leary and Roberts (2005). Lately, Strebuleav
(2007) also identified that higher business risk, bankruptcy cost, and a lower tax advantage
all reduce optimal leverage.
Among many considerations, the cost of the structure is largely to be influenced by fac-
tors like (a) risk management, (b) tax structures, (c) agency cost, (d) flotation/issuance cost,
(e) regulatory frameworks, (f) term structures of interest rate, (g) exchange rate float and
regulations, (h) technological advances (in real and money markets), (i) accounting gim-
micks, (j) capital market sentiments/movements, (k) corporate liaison with market opera-
tors, and (l) government bodies that have been worked on by various research scholars
world over. The works of Asquith and Mullins (1986); Baker and Wurgler (2002); Jung,
Kim, and Stulz (1996); and Mickelson and Partch (1989) recognized market timing as a
firm’s strategy to reduce cost that altered capital structures and increased the value of a
firm.
Similar to market timing, firm’s accessibility to cheap funds was developed as one
among several other factors (openness in the economy, developments in the financial mar-
kets, credit rating, accreditation, investment environment, government support to industry
and many others) that influenced cost. Graham and Harvey (2002) acknowledged that
credit ratings were the second highest concern for CFOs when determining their capital
structure. It was found that 57.1% CFOs found credit ratings as an important variable for
the choice of the amount of debt that they would categorize to use. More commonly,
market timing, media interventions, credit analysis, and their effect on CSD is an area of
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study in developed capital markets. Despite the range of the studies conducted on capital
structure, no attempts have been made to integrate the efforts of these studies for universal
applicability. The studies have been region specific, descriptive, and segmented, and they
do not give a holistic view of the CSD process. Psychological aspects have also not been
investigated in the decision making. Corporates in developing economies like India are
often restricted to choose equity because of low security of creditor’s rights, low institu-
tional penetration, and shallow capital markets and inadequate access to international capi-
tal markets. This study evaluated Indian firms for their leverage positions and debt
structures before investigating multiple objectives the firms may pursue for their CSDs.
The next section addresses the concerns for the use of debt by firms in a developing econ-
omy like India over a period when second phase of financial reforms have set pace and
boom in the economy provides adequate access to domestic and international markets.
Capital Structure Practices in India
India and other Asian economies have been dependent on their savings for their financing
needs at individual or corporate level. One wonders, if leverage has been used by Indian
entrepreneurs to meet their needs. Our study finds that the mean (m) LTD was 1.064 and
TDE was 1.16 for a 10-year period from 1998 to 2007. The leverages are well distributed
in old and new economy stocks. Industries in India were found levered in the following
ascending order: information technology, media and publishing, health care, fast moving
consumer goods (FMCG), transport equipments, capital goods, miscellaneous, textiles,
tourism, diversified, telecom, agriculture, consumer durables, oil and gas, power, housing
related, metal, metal products and mining, transport services, and chemical and petrochem-
icals. LTD and TDE is found to be highest in the chemical and petrochemical industry, and
lowest in the information technology industry.
We found that capital structure positions among industries (interindustry) have significant
differences (see Appendix E) statistically evidence using ANOVA and results to the pilot
study have been published in (Iyer & Agarwal, 2007). However, time (intertemporal) had no
influence over the CSDs in the industries (see Appendix F) statistically evidenced using
ANOVA, and its results to the pilot study have been published (Iyer & Agarwal, 2007). The
means (m) of capital structure were not significantly different for the 10-year period.
Absence of intertemporal differences in the sample reflects low or no influence of economic
changes on the leverage positions. Work of Rajan and Zingales (1995) also found that finan-
cial development does not seem to affect everybody equally, contrary to the common belief
that country-specific development influences capital structure practices. The study (Iyer &
Agarwal, 2007) used time differences as proxy for financial development over a 10-year
period during which the financial liberalization in India had stabilized. Results of the study
indicate that time-specific factors have little influence on mean (m) capital structure posi-
tions in the Indian industry. Among the two macroeconomic variables (economy and indus-
try), industry was found to play an influencing role in India. This was in agreement with
previous studies conducted in India for CSDs of Rao (1989), Babu (1998), Mohnot (2000),
and Das and Roy (2007) who had investigated the interindustry differences in the capital
structure of Indian firms and identified the possible sources of variations that existed in dif-
ferent industries.
Our study also found that LTD and TDE for over 4,000 observations collected for 10-
year period did not follow a normal distribution (see Appendices G and H) evidenced using
Jarque Bera Test (observation more than 50) and Anderson Darling Test (observations less
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than 50). The low level of leverages in value-creating firms needed more investigation into
their possible asymmetries that existed in the Industry. Furthermore, the distribution was
positively skewed in heavy asset–based industries like chemical and petrochemical firms
and low tangible asset–based firms like information technology.
On the assessment of firms in different industries, some industries were found to have
normal distribution. For LTD, the two industries where normal distribution is observed are
the capital goods industry and the tourism industry (see Appendix G). Normal distribution
is also observed in the housing-related industry, information technology industry, and tour-
ism industry for the TDE (see Appendix H). In the tourism industry, both LTD and TDE
observed normal distribution. However, TDE, in most industries is not close to normal
distribution—for instance, in case of chemical and petrochemical industry; consumer dur-
ables; diversified FMCG; metal and metal products; and transport services, there is no
proximity to normal distribution that could be observed. Hence, there is need to investigate
more into the possible factors affecting the leverage positions in these industries.
Correlations between mmarket capitalization of 19 industries (for 10 years) and mLTD, mTDE
were used in the study to estimate the relationship between leverage and market capitaliza-
tion. Correlations between mEPS of 19 industries (for 10 years) and mLTD, mTDE were used
in the study to estimate the relationship between leverage and EPS. In India, market capita-
lization (proxy for value of firm) was found to have low correlation with paid-up equity.
Leverage ratios were found to be highly negatively correlated with market capitalization,
all industries except high asset base industries like capital goods, chemical and petrochem-
ical, health care, metal and metal products, oil and gas, tourism, transport equipment, trans-
port services. Earnings per share (EPS) was found to be positively correlated with leverage
for only 38% of the sample that offers contradiction to existing theories that EPS should be
positively correlated with leverage. Quantitative and qualitative dimensions to the CSD
need to be explored. Agarwal, Iyer, and Yadav (2008) identified these dimensions as multi-
ple objectives and constraints influencing the capital structure. Behavioral dimensions to
CSDs in India is in its premature stage. We used a questionnaire approach for identifying
goals, motives, and constraints of a decision maker in a CSD. The questionnaire contained
19 questions and sub-questions based on 96 considerations outlined in our previous work
(Agarwal et al., 2008), and was sent to CFOs of top 434 firms in India.
Multiple Objectives and Constraints for CSD Making
The questionnaire survey received a 15.6% response. Responses indicated that in India,
firms follow simultaneous considerations. The study grouped these considerations as finan-
cial and nonfinancial objectives. Among the 68 respondents, there was consensus on the
existence of multiple objectives. Firm-level differences on objectives and priorities existed.
Moreover, priorities and goals have been found to be firm and time specific.
The decision makers preferred equity over debt; target capital structure is not explicitly
placed as a priority. Even then, they maintained a range for their capital structure. The
maintenance of ownership stake and high interest burden motivated the firms to raise
equity. The diluted EPS has acted as a main constraint for raising equity in India. The mon-
itoring role of financial institutions has played a critical role for raising debt. Damp equity
markets constrained premiums on equity issues. Bonus issues were perceived to have short-
term influence on stock prices. Stock splits and buybacks were not much used by the firms.
Discounted cash flow techniques were largely used to evaluate CSD options.
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The decision makers wanted prime lending rates to come down and regulatory bodies to
be more transparent, which have restricted their action for using debt. Exposures in interna-
tional markets were hedged and were primarily used for business purpose as against specu-
lation. Off balance sheet exposure were either not recognized as part of the CSDs or were
not used. Other strategies such as bonus shares, stock splits, and buybacks did not receive
sufficient response. It is also observed that the use of equity was more predominant than
debt in the survey, which complements the finding of the previous investigation in
‘‘Capital Structure Practices in India’’ section. Firms believe that there is range of debt to
equity mix that should be followed. However, there may not be a particular target value.
The survey clearly gave the base for multiobjective frameworks for CSDs. The risk
aversion was present among the decision makers. The study further emphasizes the need to
develop models that resolve the present difficulty of providing satisficing solutions to mul-
tiple conflicting objectives for CSDs. The next section attempts to seek satisficing solutions
to multiple goals and objectives for given priorities in CSDs. GP technique has been identi-
fied and applied to firms. Here, we illustrate it using a real life case study of an Indian firm
operating in the agriculture sector.
GP Model for CSDs Using Accounting Proxies
Mathematical programming techniques such as linear programming, integer programming,
and GP give a model framework that satisfies multiple objectives simultaneously. GP
model was first of all developed by Charnes and Cooper (1961) as an extension and modifi-
cation of linear programming model since the concept of GP problems. Later, Ijiri (1965)
studied the detailed techniques of GP as developed by Charnes and Cooper. Ijiri reinforced
and refined the concept of GP and developed it as a distinct mathematical programming
technique. His study was primarily concerned with the development of the technique and
its possible applications to accounting and management control. In addition, GP has also
been applied by Charnes and Cooper (1968) and Lee (1973) to advertising media planning,
man power planning and production, and so on. They further suggested that GP may be
applied to an almost unlimited number of managerial and administrative decision areas
such as allocation problem, planning and scheduling problems, policy analysis, and so on.
Hawkins and Adams (1974) applied GP model to capital budgeting decision problem
taking up Lorie and Savage case, and made a comparative analysis of optimal solutions as
given by Weingartner’s linear programming solution. However, Hawkins and Adams have
not taken into account the assignment of priorities to different objectives that a firm postu-
lates to achieve in order of their importance. Although a GP model as developed and
applied by Sang M. Lee, Ijiri, and others requires consistent ordering of priorities between
the numbers of multiple sets, it can be applied using its linear approximations.
Agarwal (1978) developed GP and a stochastic GP model to the capital budgeting deci-
sions under risk and uncertainty. In the problem identified by him, projects were selected
based on optimization solution derived after considering the multiple considerations as con-
straints. Agarwal (1978) extended the GP model to working capital management that oper-
ated on the premise that no specific theory undertakes the interrelationship between various
current assets and liabilities, and in the past all studies have referred to the management of
current assets as an isolated problem. In addition, Romero (1991) has presented a compre-
hensive overview of the technique, though not in finance but for engineering problems.
GP technique is capable of handling decision problems that deal with (a) single goals
only, (b) single goals with multiple subgoals, (c) multiple goals, and (d) multiple goals
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with multiple subgoals. In presence of incompatible multiple goals, the decision maker is
to identify the importance of the individual goals. When all constraints and goals are com-
pletely identified in the model, the decision maker analyzes each goal in terms of devia-
tions from the goal that are acceptable and state whether over- or underachievement of
goal is acceptable or not.
If overachievement is undesirable, positive deviation from the goal is eliminated from
the objective function. If underachievement is undesirable, negative deviation from the
goal is eliminated from the objective function. If the exact achievement of the goal is
desired, both negative and positive deviations must be represented in the objective
function.
To give importance to the goals, negative and or positive deviations about the goal must
be ranked according to the ‘‘preemptive’’ priority factors. The model considers high-order
goal prior to the low-order goals. If there are goals in k ranks, the p ‘‘preemptive’’ priority
factor pj (j = 1,2, . . . k) should be assigned to the negative and or positive deviational vari-
ables. The preemptive priority structure would have a relationship such as pj . . pj11,
which implies that the multiplication of n, however large it may be, cannot make pj11
greater than or equal to pj. Weighting can also be used in the deviational variables at the
same priority level. The criterion to be used in determining the differential weights of
deviational variables is the minimization of the opportunity cost or regret. Hence, coeffi-
cient of regret is always positive and should be assigned to individual deviational variable
with the identical pj factor.
The objective functions of the GP problem consist of deviational variables with preemp-
tive priority factors: pjs for ordinal ranking and ds for weighting at the same priority level.
Let c be 2m component row vector whose elements are products pj and d such that
c5ðd1pj1; d2pj2; . . . d2mpj2mÞ ð6:1Þ
where pji (i = 1, 2, . . . 2m; j = 1, 2, . . . k) are preemptive priority factors, and highest pre-
emptive factor p1 and dis (i = 1,2,. . . . 2m) are real numbers. Consider d to be 2m compo-
nent column vector whose elements are d2s and d1s such that
d5 d�1 ;d�2 ; . . . d�m; d1
1 ;d12 ; . . . d1
m
� �ð6:2Þ
Then a GP problem is
Minimize cd
Subject to Ax1Rd5b
x;d � 0
ð6:3Þ
where A and R are m 3 m and m 3 2m matrices, respectively.
The model framework can be used to obtain satisficing solutions to the multiple goals
and constraints faced in the GP model. In capital structure problems, quantitative relation-
ships do not exist, which need to be developed using multiple regression analysis.
The 19 industries with respect to the two leverage variables, LTD and TDE, are studied
for their relationship with other variables through correlation and stepwise regression that
develop the constraints that the industry posses on the CSD process of a firm. The study
has not evaluated the effect of macroeconomic parameters like capital markets, economic
growth rates, financial intermediation, and others as these factors in India were found to
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have insignificant effect on the leverages. Interindustry differences were found to be signif-
icant so the use of industry ratios and industry-leverage positions is used to develop the
relationship between the variables. The relationship between TDE and other 66 variables
for 19 industries is represented in Appendix C that would act as external constraints for
firms’ in respective industries when using the GP model for the Indian industry. The rela-
tionship between LTD and other 66 variables that are accounting proxies for multiple
objectives of 19 industries is represented in Appendix D that would act as external con-
straints for respective industries when using the GP model for the Indian industry.
Management discussions are carried out to determine firm-specific goals and constraint as
specified in the case study. The identified model is applied to firms to test for their validity.
The model can be defined in the following manner for all firms aiming at satisficing solu-
tion for their CSDs. The study illustrates a real-life example of an Indian firm a1, name
changed.
Case 1: a1 Company (Alpha One Company) in Agriculture Industry
The firm is into agriculture products business and has maintained its equity at Rs. 11.9
crores (for conversion into millions please see Appendix I) for the past 10 years. It is par-
ticular on not issuing any equity for growth. In the year 2007, the LTD of the company
was 0.03 and TDE of the a1 company was 0.15. Internal funds have been the prime source
of increasing the capital employed. The a1 company has observed the return on equity of
23.73% in past 1 year, which has been the highest for the past 10 years. The a1 company
wishes to retain its ROE and wants to see an increase in this position for future. The a1
company from its marketing actions intends to seek the rate of growth of net sales by
8.5%. The company is attempting to look for new markets so that it can increase its sale to
generate more profits. The a1 company intends to see that rate of growth of capital
employed remains at 23.25% after adjusting for the profits as it does not intend to raise any
debt but would like to reduce it, if possible. The a1 company believes in employing less
debt and wishes to follow a more conservative approach.
The a1 company is not adverse to the use of more capital but wishes to generate the
same through internal funds. The a1 company has profit before interest, depreciation and
tax margin of 12.26 which it feels would not improve in the future as the raw material
costs are rising in India. Presently, a1 company employs a net working capital of Rs.
147.31 crores; it has a debtor’s velocity of 48 days, and the payout maintained by the a1
company is 16.79% and the cash flow from investing activities is Rs. 42.88 crores. The
capital expenses in foreign exchange are zero. It does not intend to observe changes in
these values for next few years. The a1 company presently enjoys a market capitalization
of Rs. 401.87crores, which is the highest market capitalization observed by the a1 company
for the past 10 years and wishes to only raise it and not lose its valuation. The a1 company
also believes that higher leverage results in low market capitalizations. The a1 company
has not attached any priority to the three goals. The firm’s goals have been identified by
the study in the following manner:
Goal A1: To retain and increase rate of return on equity (ROE) at 23.73% can be
stated as
ROE � 23:73
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Goal A2: To observe a rate of growth of net sales (ROGNS) at 8.5, this is presently
7.9% is stated as
ROGNS � 8:5
Goal A3: To observe a rate of growth of capital employed at 23.25% is stated as
ROGCE523:25
The deviations from the goals can be positive (d1) or negative (d2). The positive devia-
tion (d1) in first two goals is desirable; however, the negative deviations (d2) from the
goals are not desirable. The negative deviations violate the goal requirement and hence
should be minimized for the first two goals. In the third goal, both positive (d1) and nega-
tive deviation (d2) are not desirable so both positive and negative deviations have to be
minimized, for the exact attainment of the goal.
In each goal when the deviational variables are introduced, the inequalities converted
into equalities by introducing on left hand side (LHS), di(s) and the minimization function
shall be established using the undesirable deviational variable that have to be minimized.
The GP model for CSD for a company is as follows:
Objective : Minimize z5d1�
1d21
1d3�
1d31
Subject to:
Goal Constraint 1 : ROE 2 d11 1 d1
2 = 23.731
Goal Constraint 2 : ROGNS 2 d21 1 d2
2 = 8.51
Goal Constraint 3 : ROGCE 2 d31 1 d3
2 = 23.251
Industry Constraint 1 : TDE = 1.071 1 0.979 LTD 2 0.0007 PBIT 1
0.003 REFX 1 0.002ROGPBIDT 1
0.002ROGGB 1 0.040 CEFX 1
0.001ROGCE 1 0.001 FAR
Industry Constraint 2 : LTD = 20.812 1 1.085 TDE 1 0.001 NWC 2
0.016DV 1 0.013PO 1 0.000MC 1
0.001CFFI 1 0.010PBIDTM 2 0.008CEFX
Firm Constraint 1 : ROE = 0.399ROGCE 2 0.0105ROGPAT
Firm Constraint 2 : ROGCE = 74.31ROGRE 1 6.71ROGLTD
Firm Constraint 3 : ROGPBIT = 5.717 ROGNS
Firm Constraint 4 : ROGPAT = 172LTD 2 145.25TDE 2 0.21 ROGPBIT
Firm Constraint 5 : NWC = 97.84 TDE
Firm Constraint 6 : PBIT . 153.88
Firm Constraint 7 : ROGGB . 3.8
Firm Constraint 8 : NWC . 147.31
Firm Constraint 9 : DV = 48
Firm Constraint 10 : PBIDTM = 12.26
Firm Constraint 11 : CFFI = 42.38
Firm Constraint 12 : MC . 401.87
Firm Constraint 13 : CEFX = 0
Firm Constraint 14 : PBDT . 166.24
10 Journal of Accounting, Auditing & Finance
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Tab
le1.
Goal
Pro
gram
min
gSo
lution
for
a1
Com
pan
yU
sing
Acc
ounting
Pro
xie
sfo
rG
oal
san
dC
onst
rain
ts
Obje
ctiv
eFu
nct
ion:M
inm
ize
z5d� 1
1d1 2
1d� 3
1d1 3
Obje
ctiv
eFu
nct
ion
z=
0;
DEC
ISIO
NVA
RIA
BLE
S:LT
D=
0;
RO
GLT
=0;
Non
Bas
icVar
iable
sd1 1
51;d� 1
50;d
1 25
0;d� 2
51;d
1 35
1;d�
51
Var
iable
s(2
7)
RO
GN
S,RO
GR
E;RO
GC
E;RO
E;RO
GPB
;T
DE;PB
DT
;PB
IT;RO
GG
B;M
C;PO
;RO
GPA
T;C
FFI;
REFX
;
NW
C;PB
DT
M;C
EFX
;FA
R;D
V;LT
D;RO
GLT
:dþ 1;d� 1;dþ 2;d� 2;dþ 3;d� 3
S.N
o.
Const
rain
tsTa
rget
valu
eSo
lution
Dev
iations
Sensi
tivi
tyan
alys
isR
HS
range
Goal
s1.
RO
E2
d 11
1d 1
2=
23.7
30
aRO
E=
23.7
30
d 11
=1
7.9
251-3
9.8
025
d 12
=0
2.
RO
GN
S2
d 21
1d 2
2=
8.5
00
aRO
GN
S=
8.5
00
d 21
=0
0.0
000-1
}
d 22
=1
3.
RO
GC
E2
d 31
1d 3
2=
23.2
50
aRO
GC
E=
23.2
50
d 31
=1
0.0
000-6
3.4
660
d 32
=1
Indust
ry4.
TD
Eb
20.9
79LT
D1
0.0
007PB
IT2
0.0
03R
EFX
2
0.0
02PB
DT
M2
0.0
02RO
GG
B2
0.0
40C
EFX
2
0.0
01RO
GC
E1
0.0
01FA
R
=1.0
71
TD
E=
0.1
19
—0.3
400-1
}
LTD
=0.0
00
PB
IT=
Rs.
257.3
10
crR
EFX
=R
s.163.9
20
crPB
DT
M=
Rs.
166.2
40
crRO
GG
B=
Rs.
3.8
70%
CEFX
=0.0
00cr
RO
GC
E=
23.2
50%
FAR
=6.5
50%
5.
1.0
85T
DE
1LT
Dc
1
0.0
01N
WC
20.0
16D
V1
�0.0
81
TD
E=
0.1
19
—2
0.0
476-0
.8741
(con
tinue
d)
11
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Tab
le1.
(continued
)
S.N
o.
Const
rain
tsTa
rget
valu
eSo
lution
Dev
iations
Sensi
tivi
tyan
alys
isR
HS
range
0.0
13PO
10.0
01C
FFI1
0.0
10PB
TM
10.0
08RO
ELT
D=
0.0
00
NW
C=
147.3
30
crD
V=
48.0
00day
sPO
=16.7
90%
CFF
I=
42.3
80cr
PB
TM
=12.2
60cr
RO
E=
23.7
30%
Firm 6
.2
0.3
93RO
GC
E1
RO
Ed
1
0.0
105RO
GPA
�0.0
00
RO
GC
E=
23.2
50%
S 1=
15.8
05
2}
-15.8
049
RO
E=
23.7
30%
RO
GPA
=115.4
40%
7.
RO
GC
Ee
26.7
1RO
GLT
2
74.3
1RO
GR
E�
0.0
00
RO
GC
E=
23.2
50%
—2
}-2
3.2
500
RO
GLT
=0.0
00%
RO
GR
E=
0.3
13%
8.
RO
GPB
f2
5.7
17RO
GN
S�
0.0
00
RO
GPB
=48.5
95%
—2
48.5
945-1
}
RO
GN
S=
8.5
00%
9.
145.2
5T
DE
2172LT
D1
RO
GPA
Tg
10.2
1RO
GPB
�0.0
00
TD
E=
0.1
19
S 2=
142.8
28
2}
-142.8
283
LTD
=0.0
00
RO
GPA
=115.4
40%
RO
GPB
=48.5
95%
10.
PB
DT
h�
166.2
40
PB
DT
=166.2
40
cr—
0.0
000-1
}
11.
PB
ITi
�152.8
80
PB
IT=
257.3
10
crS 3
=104.4
30
2}
-257.3
104
12.
RO
GG
Bj
�3.8
70
RO
GG
B=
3.8
70%
—0.0
000-1
}
13.
MC
k=
401.0
00
MC
=401.0
00
cr—
0.0
000-1
}
14.
DV
l=
48.0
00
DV
=48.0
00
day
s—
39.9
638-9
7.5
718
15.
PO
m=
16.7
90
PO
=16.7
90%
—0.0
000-2
6.6
808
16.
RO
GPA
Tn
�115.4
40
RO
GPA
=115.4
40%
—0.0
000-1
}
17.
CFF
Io=
42.3
80
CFF
I=
42.3
80cr
—0.0
000-1
70.9
600
(con
tinue
d)
12
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Tab
le1.
(continued
)
S.N
o.
Const
rain
tsTa
rget
valu
eSo
lution
Dev
iations
Sensi
tivi
tyan
alys
isR
HS
range
18.
REFX
p=
163.9
20
REFX
=163.9
20cr
—0.0
000-1
}
19.
211.9
1T
DE
1N
WC
q�
0.0
00
TD
E=
0.1
19
S 4=
145.9
19
2}
-145.9
186
NW
C=
147.3
30
cr—
0.0
000-2
5.1
180
20.
PB
IDT
Mr
=12.2
60
PB
TM
=12.2
60
cr21.
CEFX
s=
0.0
00
CEFX
=0.0
00
cr—
0.0
000-1
}
22.
FAR
s=
6.5
50
FAR
=6.5
50
—0.0
000-7
37.5
631
23.
NW
Ct
�147.3
30
NW
C=
147.3
30
cr—
2.9
958-2
75.9
100
Obje
ctiv
efu
nct
ion:M
inim
ize
z=
d 12
1d 2
11
d 32
1d 3
1
Obje
ctiv
efu
nct
ion
z=
0
Dec
isio
nva
riab
les
LTD
=0;RO
GLT
=0
Nonbas
icva
riab
les
d 11
=1,d 1
2=
0,d 2
1=
0,d 2
2=
1,d 3
1=
1,d 3
2=
1
Note
:So
lution
isobta
ined
usi
ng
PO
MSo
ftw
are.
S 1,S 2
,S 3
,S 4
are
slac
kva
riab
les.
a Targ
etva
lues
for
the
goal
sar
ebas
edon
the
firm
’spre
fere
nce
san
ddet
erm
ined
with
the
hel
pofth
em
anag
emen
tpar
tici
pat
ion.
bTo
taldeb
tto
equity
(TD
E)in
the
agri
culture
indust
ryis
dep
enden
ton
long-
term
deb
t(L
TD
),pro
fitbef
ore
inte
rest
and
tax
(PB
IT),
reve
nue
earn
ing
info
reig
nex
chan
ge(R
EFX
),
rate
of
grow
thin
pro
fitbef
ore
inte
rest
,dep
reci
atio
nan
dta
x(R
OG
PB
IDT
),ra
teof
grow
thof
gross
blo
ck(R
OG
GB
),ca
pital
earn
ing
info
reig
nex
chan
ge(C
EFX
),ra
teof
grow
th
ofca
pital
empl
oye
d(R
OG
CE),
and
fixed
asse
tra
tio
(FA
R).
This
has
bee
nid
entifie
dth
rough
the
step
wis
ere
gres
sion,ple
ase
refe
rA
ppen
dix
C.
c Long-
term
deb
tto
equity
(LT
D)
inth
eag
ricu
lture
indus
try
isdep
enden
ton
tota
ldeb
tto
equity
(TD
E),net
work
ing
capital
(NW
C),
deb
tors
velo
city
(DV
),pay
out
(PO
),m
arke
t
capital
izat
ion
(MC
),ca
shflo
wfr
om
inve
stin
gac
tivi
ties
(CFF
I),
pro
fitbef
ore
inte
rest
,dep
reci
atio
n,
tax
mar
gin
(PB
IDT
M),
capital
earn
ing
info
reig
nex
chan
ge(C
EFX
).T
his
has
bee
nid
entifie
dth
rough
ast
epw
ise
regr
essi
on,ple
ase
refe
rA
ppen
dix
D.
dR
ate
of
retu
rnon
equity
(RO
E)
isdep
enden
ton
the
rate
of
grow
thof
capital
emplo
yed
(RO
CE)
and
rate
of
grow
thof
pro
fit(R
OG
PAT
),w
hic
hhas
bee
ndev
eloped
usi
ng
the
firm
’s10
year
sdat
aan
dm
ultip
lere
gres
sion
anal
ysis
.eR
ate
of
grow
thof
capital
emplo
yed
(RO
CE)
isdep
enden
ton
rate
of
grow
thof
reta
ined
earn
ings
(RO
GR
E)
and
rate
of
grow
thof
long-
term
deb
t(R
OG
LTD
).T
he
rate
of
grow
thofpai
dup
equity
isnot
consi
der
edas
the
equity
inth
epas
t10
year
shas
rem
ained
const
ant
atR
s.1.2
9cr
ore
san
dth
efir
mdoes
not
inte
nd
toch
ange
RO
GC
E.
f Rat
eofgr
ow
thofpro
fitbef
ore
inte
rest
and
taxe
s(R
OG
PB
)is
dep
enden
ton
the
rate
ofgr
ow
thofnet
sale
s(R
OG
NS)
.g R
ate
ofgr
ow
thofpro
fitaf
ter
tax
(RO
GPA
T)
isdep
enden
ton
long-
term
deb
t(L
TD
),to
taldeb
tto
equity
(TD
E),
rate
ofgr
ow
thofpro
fitbef
ore
inte
rest
and
taxe
s(R
OG
PBIT
).hFi
rms
wan
tsth
atpro
fitbef
ore
dep
reci
atio
nan
dta
x(P
BD
T)
should
not
fall
bel
ow
the
pre
sent
leve
lofR
s.166.2
4cr
ore
s.i P
rofit
bef
ore
inte
rest
and
taxe
s(P
BIT
)has
tobe
hig
her
than
the
pre
sent
leve
lofoper
atio
ns
inth
eye
ar2007
atR
s.153.8
8cr
ore
s.
13
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Tab
le1.
(continued
)
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
__
_j R
ate
ofgr
ow
thofgr
oss
blo
ck(R
OG
GB
)is
3.8
8,w
hic
hca
nbe
grea
ter
than
the
pre
vious
year
asth
efir
min
tends
topurc
has
eeq
uip
men
ts.
kM
arke
tca
pital
izat
ion
isat
tem
pte
dto
be
hig
her
than
the
pre
sent
leve
l,m
anag
emen
tis
not
inte
rest
edin
mai
nta
inin
gits
mar
ket
capital
izat
ion
and
only
inin
crea
sing
it.
l Firm
inte
nds
tom
ainta
inits
deb
tors
velo
city
at48
day
s,it
may
choose
tore
duce
itin
futu
rebut
not
atpre
sent.
Firm
does
not
inte
nd
toin
crea
seit
asw
ould
then
incr
ease
its
requir
emen
tfo
rth
enet
work
ing
capital
.m
The
firm
inte
nds
toke
epits
pay
out
ratio
(PO
)at
16.4
2%
.nT
he
firm
inte
nds
tohav
eits
rate
ofgr
ow
thofpro
fitaf
ter
tax
(RO
GPA
T)
more
than
Rs.
115.4
40cr
ore
s.oT
he
firm
stan
ds
inve
sted
ina
man
ner
that
pro
vides
for
cash
from
inve
stin
gac
tivi
ties
(CFF
I)w
hic
his
Rs.
42.5
3cr
ore
san
dth
ere
isno
scope
for
impro
vem
ent.
pFi
rmdoes
not
hav
eca
pital
earn
ing
from
fore
ign
exch
ange
(CEFX
)an
ddoes
not
inte
nd
tohav
eth
esa
me
infu
ture
and
inte
nds
tom
ainta
inits
reve
nue
earn
ings
(REFX
)at
163.9
2cr
ore
s.qN
etw
ork
ing
capital
(NW
C)
and
tota
ldeb
tto
equity
(TD
E)re
lationsh
iphas
bee
ndet
erm
ined
,ke
epin
gT
DE
asin
dep
enden
tan
das
sum
ing
that
curr
ent
liabili
ties
finan
cem
ost
of
the
curr
ent
asse
tsan
dth
eto
taldeb
tis
use
dto
finan
ceit.
r The
firm
with
its
oper
atio
nhas
pro
fitbef
ore
inte
rest
,dep
reci
atio
n,an
dta
xm
argi
n(P
BID
TM
)as
Rs.
12.2
9cr
ore
s,w
hic
his
reta
inab
lew
ith
cost
effic
ienci
es.
s The
firm
issa
tisf
ied
with
its
fixed
asse
tra
tio
(FA
R)
of6.5
50.
t Net
work
ing
capital
(NW
C)
ofth
efir
mw
ith
pre
sent
oper
atio
nis
Rs.
147.3
1cr
ore
s,an
dit
cannot
reduce
itw
ith
its
pre
sent
form
ofoper
atio
nan
dte
rms.
14
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Description of variables is given in Appendix J. Table 1 gives the GP model solution for
the agriculture firm with the formulation. For explanation on the constraints and goal
please see notes to the Table 1.
There are, in all 3 goals with no priorities, 2 industry constraints and 14 firm constraints
of the a1 company. There are a total of 19 constraint equations. There are 27 variables,
including the deviational variables. POM software has been used to seek the GP solution in
its linear formulations. The results are presented in Table 1. On the 26th iteration, the soft-
ware achieved the solution that would minimize the value of z to zero such that ROE is
23.73%, ROGNS is 8.5%, and ROGCE is 23.25 %, which were the goals. The ROGRE
would be 0.313%, ROGPBIT has reduced to 48.595%, TDE is reduced to 0.119, PBDT is
the constraint met at Rs. 166.240 crores, PBIT has increased at Rs. 257.310 crores,
ROGGB is maintained at the constraint level of 3.870%, MC was found to be Rs. 401.87,
PO was also found to be maintained at 16.790%, ROGPAT was same as the previous year
of Rs. 115.440 crores, CFFI is also maintained at Rs. 42.380 crores, REFX was also main-
tained at Rs. 163.920crore, NWC was also maintained at Rs. 147.330 crores, PBIDTM is
also maintained at 12.260%, and CEFX which was a constraint was also zero. However,
the fixed asset ratio has increased to FAR 6.550. DV was to be at the constraint level of
48.000 days.
The a1 company would have a rate of growth of sales at 8.5% which increases its
ROCE by 23.25%, the total debt to equity would reduce from the present level of 0.15 to
0.11, and it is proposed that the long-term debt that was 0.03 may be paid back to keep a
zero level of long-term debt. The REFX is also maintained as a non basic variable that take
up the value of zero in the solution.
Concluding Remarks
GP model is identified as a multicriteria technique providing satisficing solutions that over-
comes the deficiency of the single objective framework using accounting proxies for multi-
ple objective framework. The steps involved in the development of a firm-specific, CSD
process is (a) management participation; (b) analysis of objectives, goals, and policies
using accounting proxies; (c) formulation of a GP model; (d) testing the model and solu-
tion; and (e) final implementation of the solution. The model allows simultaneous solutions
to a system of complex multiple objectives. It utilizes an ordinal hierarchy among conflict-
ing multiple goals where lower order goals are considered after higher order goals are satis-
fied or have reached the desired limit. There is an inbuilt flexibility in the model.
A GP model for multiobjective CSD using accounting proxies has been tested on an
Indian Agricultural Firm. The model supports the fulfillment of multiple objectives and
constraints simultaneously. The model may prove to be highly beneficial for firms in
achieving an optimum or satisficing practical solution to CSDs incorporating multiple goals
in a systematic and scientific way in today’s complex and dynamic business world with
accounting information.
Agarwal et al. 15
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Appendix B. List of Accounting Proxies
Variables Abbreviations
Equity paid up EPNetworth NETCapital employed CEGross block GBNet working capital (Incl. Def. Tax) NWCCurrent assets (Incl. Def. Tax) CACurrent liabilities and provisions (Incl. Def. Tax) CLTotal assets/liabilities (excl revaluation and written off expenses) TALGross sales GSNet sales NSOther income OIValue of output VOCost of production COPSelling cost SCProfit before interest depreciation and taxes PBIDTProfit before depreciation and taxes PBDTProfit before interest and taxes PBITProfit before taxes PBTProfit after tax PATCash profit CPRevenue earnings in forex REFXRevenue expenses in forex REXFX
(continued)
Appendix A. Industry Composition of ET 500 Companies
S. No. Industry compositionNumber of companies
in each industryPercentage of the industries
in the sample survey
1 Agriculture 26 5.202 Capital goods 46 9.203 Chemical and petrochemical 35 7.004 Consumer durables 18 3.605 Diversified 12 2.406 Finance 56 11.207 FMCG 25 5.008 Health care 27 5.409 Housing related 41 8.2010 Information technology 33 6.6011 Media and publishing 6 1.2012 Metal, metal products, and mining 32 6.4013 Miscellaneous 30 6.0014 Oil and gas 15 3.0015 Power 9 18.0016 Telecom 12 2.4017 Textile 21 4.2018 Tourism 3 0.6019 Transport equipments 40 8.0020 Transport services 13 2.60
Total 500 100.00
16 Journal of Accounting, Auditing & Finance
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Appendix B. (continued)
Variables Abbreviations
Capital earnings in forex CEFXCapital expenses in forex CEXFXBook value (unit currency) BVMarket capitalization MCCash earnings per share (annualized; unit currency) CEPSEarnings per share (annualized; unit currency) EPSDividend (annualized %) DIVPayout (%) POCash flow from operating activities CFFOCash flow from investing activities CFFICash flow from financing activities CFFFROG-net worth (%) ROGNETROG-capital employed (%) ROGCEROG-gross block (%) ROGGBROG-gross sales (%) ROGGSROG-net sales (%) ROGNSROG-cost of production (%) ROGCOPROG-total assets (%) ROGTAROG-profit before interest, depreciation, and taxes (%) ROGPBIDTROG-profit before depreciation and taxes (%) ROGPBDTROG-profit before interest and taxes (%) ROGPBITROG-profit before taxes (%) ROGPBTROG-profit after tax (%) ROGPATROG-cash profit (%) ROGCPROG-revenue earnings in forex (%) ROGREFXROG-revenue expenses in forex (%) ROGREXFXROG-market capitalization (%) ROGMCDebt-equity ratio TDELong-term debt-equity ratio LTDCurrent ratio CRFixed assets ratio FARInventory ratio IRDebtors ratio DRInterest cover ratio ICRProfit before interest, depreciation, and tax margin (%) PBITM (%)Profit before interest tax margin (%) PBITM (%)Profit before depreciation and tax margin (%) PBDTM (%)Cash profit margin (%) CPM (%)Amortized profit after tax margin (%) APATMReturn on capital employed (%) ROCE (%)Return on networth (%) RONW (%)Debtors velocity (days) DVCreditors velocity (days) CVValue of output/total assets VOTAValue of output/gross block VOGB
Agarwal et al. 17
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Ap
pen
dix
C.
TD
EIn
dust
ryC
onst
rain
tEquat
ions
(19
Indust
ries
)
Expla
nat
ory
pow
erVar
iable
sposi
tive
lyco
rrel
ated
(r=
0.9
0)
Var
iable
sneg
ativ
ely
corr
elat
ed(r
=2
0.9
0)
S.N
o.I
ndust
ryT
DE
const
rain
teq
uat
ion
RR
2SE
With
TD
EW
ith
TD
E
1A
gric
ulture
TD
E=
1.0
71
10.9
79
LTD
20.0
007
PB
IT1
0.0
03
REFX
10.0
02RO
GPB
IDT
1
0.0
02RO
GG
B1
0.040
CEFX
10.0
01RO
GC
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0.0
01
FAR
11
79
PB
DT
M,C
PM
,PA
T,PB
T,PB
DT,
MC
,C
P,an
dLT
DN
one
2C
apital
goods
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0.7
54
10.0
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C2
0.0
02C
FFF
1
0.0
14
PB
IDT
M0.9
76
0.9
53
0.0
2048
CFF
O,EP,
RO
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OP,
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I,IR
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V,PB
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M,
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PM
,LT
D,C
EFX
,an
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VO
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3C
hem
ical
and
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als
TD
E=
22.2
95
11.5
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0.0
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GC
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0.0
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0.9
99
0.1
216
LTD
None
4C
onsu
mer
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ble
sT
DE
=0.3
97
11.2
1LT
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0.0
28
OI2
0.0
13C
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20.0
02PB
T1
0.9
99
0.0
1933
LTD
None
5D
iver
sifie
din
dust
ryT
DE
=0.5
30
11.0
87LT
D2
0.0
73C
R1
0.0
08EPS
0.9
99
0.9
98
0.0
2427
LTD
None
6FM
CG
TD
E=
1.0
38
10.0
92V
OG
B2
0.0
08A
PAT
M1
0.9
99
0.0
8282
REX
FX,RO
GN
S,RO
GTA
,IR
,RO
GG
S,R
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,C
EX
FX,IC
R,
REX
FX,M
C,PA
T,PB
T,C
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WC
PBD
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,PBID
T,N
ET,
CFF
O,C
L,C
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E,O
I,C
OP,
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GB
,N
S,V
O,G
S,C
EPS,
GB
TA
L,SC
,PB
ITM
,EPS,
RO
CE,PO
,PB
IDT
M,
DV,
BV,
CR
,D
R,FA
R,EP,
CV,
VO
TA
,an
dLT
D
CFF
F,C
FFI
7H
ealth
care
TD
E=
0.1
42
11.1
55
LTD
0.9
93
0.9
87
0.0
122
LTD
None
8H
ousi
ng
rela
ted
TD
E=
0.1
88
11.0
22LT
D1
0.0
00RO
GM
C0.9
99
0.9
99
0.0
31071
LTD
PB
IDT
M9
Info
rmat
ion
tech
nolo
gyT
DE
=2
0.0
70
12.0
69LT
D2
0.0
01D
IV0.9
66
0.9
34
0.0
335
None
None
(con
tinue
d)
18
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Ap
pen
dix
C.
(continued
)
Expla
nat
ory
pow
erVar
iable
sposi
tive
lyco
rrel
ated
(r=
0.9
0)
Var
iable
sneg
ativ
ely
corr
elat
ed(r
=2
0.9
0)
S.N
o.I
ndust
ryT
DE
const
rain
teq
uat
ion
RR
2SE
With
TD
EW
ith
TD
E
10
Med
iaan
dpublis
hin
gT
DE
=0.4
34
10.8
66LT
D1
0.1
39C
R1
0.0
05C
V1
0.0
00PB
T2
0.0
01D
V1
0.0
00EP
11
0.0
007
LTD
None
11
Met
alan
dm
etal
pro
duct
TD
E=
0.0
45
11.0
98LT
D1
0.0
01RO
GG
S1
0.0
00RO
GPB
IDT
10.0
02IC
R1
0.0
00RO
GG
B2
0.0
02C
V
11
0.0
049
LTD
None
12
Mis
cella
neo
us
indust
ryT
DE
=1.4
70
20.0
62A
PAT
M1
0.0
01RO
GM
C0.9
49
0.9
01
0.0
497
None
None
13
Oil
and
gas
indust
ryT
DE
=1.7
15
20.0
04RO
GPB
DT
0.8
68
0.7
54
0.0
5673
LTD
None
14
Pow
erT
DE
=0.5
23
10.9
62LT
D2
0.3
.1C
PM
10.0
21
PB
ITM
0.9
99
0.9
99
0.0
183
LTD
None
15
Tele
com
TD
E=
21.1
68
11.5
25LT
D2
0.3
61V
OG
B1
0.0
00PA
T1
0.0
01RO
GPA
T1
10.0
163
LTD
None
16
Textile
TD
E=
20.1
76
11.4
93LT
D1
0.0
01RO
GM
C1
0.0
31FA
R1
0.9
99
0.0
1369
CEPS,
BV,
EPS,
and
LTD
17
Touri
smT
DE
=0.0
15
11.0
49LT
D0.9
99
0.9
98
0.0
1134
LTD
None
18
Tran
sport
equip
men
tsT
DE
=0.1
96
10.1
073LT
D1
0.0
04C
V2
0.0
14C
PM
0.9
68
0.9
37
0.0
1185
None
None
19
Tran
sport
serv
ices
TD
E=
20.1
34
11.0
47LT
D1
0.0
05D
V2
0.0
01RO
GM
C1
0.0
05RO
CE
11
0.0
05032
RO
GM
C,LT
DN
one
19
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Ap
pen
dix
D.
LTD
Indust
ryC
onst
rain
tEquat
ions
(19
Indust
ries
)
Expla
nat
ory
pow
erVar
iable
sposi
tive
lyco
rrel
ated
(r=
0.9
0)
Var
iable
sneg
ativ
ely
corr
elat
ed(r
=2
0.9
0)
S.N
o.
Indust
ryLT
Dco
nst
rain
teq
uat
ion
RR
2SE
LTD
with
LTD
1A
gric
ulture
LTD
=2
0.8
12
11.0
85
TD
E1
0.0
01
NW
C2
0.0
16D
V1
0.0
13PO
10.0
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C1
0.0
01C
FFI
10.0
10PB
IDT
M1
0.0
08C
EFX
11
0.0
012
TD
EA
PAT
M
2C
apital
goods
LTD
=0.3
04
10.0
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20.5
71T
DE
2
0.1
57C
R1
10.0
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CFF
O,EP,
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dT
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VO
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3C
hem
ical
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pet
roch
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als
LTD
=1.5
74
10.6
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DE
20.0
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0.0
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0.9
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0.0
8091
EP,
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B,C
PM
,A
PAT
M,R
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DT
M,C
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C,an
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None
4C
onsu
mer
dura
ble
sLT
D=
1.5
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11.0
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DE
10.0
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OG
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0.0
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0.9
96
0.0
417
TD
EN
one
5D
iver
sifie
din
dust
ryLT
D=
20.1
33
10.0
839T
DE
20.0
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FFO
10.0
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GC
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0.0
01RO
NW
1
0.0
01RO
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S
11
0.0
0731
TD
EN
one
6FM
CG
LTD
=2
0.0
13
10.5
82
TD
E0.9
90.9
82
0.1
774
RO
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T,IR
,R
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VO
TA
,C
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and
TD
E
CFF
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7H
ealth
care
LTD
=2
0.0
46
10.8
57
TD
E2
0.0
04RO
CE
11
0.0
0055
DR
,C
FFI,
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VO
TA
,PO
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R,
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VO
GB
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R,an
dT
DE
None
8H
ousi
ng
rela
ted
LTD
=2
0.1
79
10.9
76T
DE
10.0
00RO
GM
C1
0.9
99
0.0
3036
PO
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GPA
T,B
V,RO
GC
E,C
FFI,
VO
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GN
W,an
dT
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M
9In
form
atio
nte
chnolo
gyLT
D=
20.0
25
10.4
76T
DE
10.0
01D
IV1
0.0
00C
FFF
10.0
06FA
R0.9
70.9
34
0.0
335
None
None
(con
tinue
d)
20
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Ap
pen
dix
D.
(continued
)
Expla
nat
ory
pow
erVar
iable
sposi
tive
lyco
rrel
ated
(r=
0.9
0)
Var
iable
sneg
ativ
ely
corr
elat
ed(r
=2
0.9
0)
S.N
o.
Indust
ryLT
Dco
nst
rain
teq
uat
ion
RR
2SE
LTD
with
LTD
10
Med
iaan
dpublis
hin
gLT
D=
0.1
30
10.7
07T
DE
20.0
05RO
NW
0.9
70.9
30.0
2422
TD
EN
one
11
Met
alan
dm
etal
pro
duct
LTD
=2
0.0
41
10.9
11T
DE
10.0
01RO
GG
S1
0.0
02IC
R1
0.0
00RO
GG
B2
0.0
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0.0
00RO
GPB
IDT
11
0.0
044
TD
EN
one
12
Mis
cella
neo
us
indust
ryLT
D=
1.7
15
20.0
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CE
10.0
05RO
GTA
0.9
70.9
39
0.0
394
TD
ERO
CE
13
Oil
and
gas
indust
ryLT
D=
0.3
11
10.1
50C
EPS
20.2
05EPS
10.9
90.1
9315
TD
EN
one
14
Pow
erLT
D=
20.0
23
10.8
83T
DE
20.0
00C
L1
0.0
00C
FFF
10.9
97
0.0
309
TD
EN
one
15
Tele
com
LTD
=0.7
67
10.6
55T
DE
20.2
37V
OG
B1
0.0
00PA
T1
0.0
00RO
GPA
T1
10.0
107
TD
EN
one
16
Textile
LTD
=0.1
18
10.6
69T
DE
20.0
01RO
GM
C1
0.9
99
0.0
0916
TD
EN
one
17
Touri
smLT
D=
0.0
13
10.9
51T
DE
10.9
98
0.0
108
TD
EN
one
18
Tran
sport
equip
men
tsLT
D=
0.1
26
10.5
54T
DE
0.8
30.6
91
0.0
154
None
None
19
Tran
sport
serv
ices
LTD
=0.1
28
10.9
55T
DE
20.0
05D
V1
0.0
01RO
GM
C2
0.0
04RO
GC
E1
10.0
048
RO
GPB
IT,RO
GM
C,an
dT
DE
None
21
at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from
Ap
pen
dix
E.
Sum
mar
yof10
Year
sLT
Dfo
r19
Indust
ries
S.N
o.
Indust
ry2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
Min
Max
Ran
geA
vera
ge
1A
gric
ulture
0.7
30.6
90.8
50.9
71.1
31.2
71.3
51.5
81.6
0.8
10.7
1.6
0.9
11.1
2C
hem
ical
and
pet
roch
emic
als
0.5
80.9
47.4
7.4
52.1
11.4
91.0
71.2
10.9
40.6
7.5
6.8
72.4
23
Pow
er0.5
90.5
10.4
91.6
61.0
81.5
71.7
1.5
51.3
61.3
60.5
1.7
1.2
1.1
94
Tran
sport
serv
ices
1.1
70.9
61.7
86.7
43.0
21.7
41.5
32.1
50.8
10.6
70.7
6.7
6.0
62.0
65
Consu
mer
dura
ble
s0.6
10.6
10.7
50.7
50.7
12.4
1.4
1.5
41.3
11.1
10.6
2.4
1.8
1.1
26
Cap
ital
goods
0.5
70.6
40.6
70.7
70.7
30.7
20.6
20.6
10.6
40.6
10.6
0.8
0.2
10.6
67
Div
ersi
fied
0.8
60.8
20.7
80.8
0.8
30.7
50.9
1.1
61.8
61.7
20.8
1.9
1.1
1.0
58
FMC
G0.5
10.4
90.3
50.3
40.5
0.4
91.6
20.3
70.3
60.3
30.3
1.6
1.2
90.5
49
Hea
lth
care
0.5
10.4
40.3
50.3
90.4
40.4
40.3
80.4
0.5
30.6
60.4
0.7
0.3
10.4
510
Housi
ng
rela
ted
11.1
11.5
61.3
12.0
61.7
1.2
81.4
52.6
61.0
71
2.7
1.6
61.5
211
Info
rmat
ion
tech
nolo
gy0.2
60.2
90.3
10.4
40.3
0.2
10.2
70.2
70.3
80.3
30.2
0.4
0.2
20.3
112
Med
iaan
dpublis
hin
g0.3
50.4
10.5
10.4
80.3
40.4
10.3
90.5
80.5
60.3
0.6
0.2
50.4
513
Met
al,m
etal
pro
duct
s,an
dm
inin
g1.3
62.4
80.9
21.2
43.5
93.6
1.6
11.1
61.1
60.7
40.7
3.6
2.8
61.7
914
Mis
cella
neo
us
0.5
80.6
80.7
90.7
70.8
0.9
11.0
70.7
70.6
80.5
80.6
1.1
0.4
90.7
615
Oil
and
gas
0.4
80.5
20.5
90.6
51.0
30.9
80.7
5.6
30.7
30.5
0.5
5.6
5.1
61.1
816
Tele
com
0.6
60.5
40.5
61.3
62.0
20.9
71.1
91.0
21.0
11.2
20.5
21.4
91.0
517
Textile
s1.0
41.6
20.9
70.6
70.6
60.7
70.9
50.8
90.8
70.8
40.7
1.6
0.9
50.9
318
Touri
sm1.0
91.0
81.0
30.8
30.8
20.9
61.1
51
0.8
60.7
30.7
1.2
0.4
20.9
619
Tran
sport
equip
men
ts0.6
0.6
20.5
80.6
30.6
20.5
70.5
80.6
30.6
10.5
30.5
0.6
0.1
0.5
9M
inim
um
0.2
60.2
90.3
10.3
40.3
0.2
10.2
70.2
70.3
60.3
3M
axim
um
1.3
62.4
87.4
7.4
53.5
93.6
1.7
5.6
32.6
61.7
2R
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23
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Appendix G. Industry Wise Normal Distribution Test Results for LTD
LTD AGRI CG CP CD DIV FMCG HC HR IT MP
Jarque Bera 0.91 0.65 3.13 2.26 2.74 18.95 2.23 2.24 0.87 0.77Probability 0.63 0.72 0.19 0.32 0.25 0 0.33 0.33 0.65 0.68Anderson darling (A2) 0.33 0.32 1.75 0.59 1.35 2.06 0.49 0.48 0.36 0.28Probability 0.44 0.45 0 0.08 0 0 0.167 0.175 0.35 0.53
LTD MMMP MIS OG PO TELE TEX TSM TE TS
Jarque Bera 1.68 0.74 21.17 1.25 1.26 6.93 0.69 1.51 1.06Probability 0.43 0.69 0 0.53 0.53 0.03 0.71 0.47 0.01Anderson darling (A2) 0.87 0.34 2.41 0.66 0.38 0.41 0.25 0.42 1.11Probability 0.01 0.41 0 0.055 0.32 0.26 0.65 0.26 0
Appendix H. Industry Wise Normal Distribution Test Results for TDE
TDE AGRI CG CP CD DIV FMCG HC HR IR MP
Jarque Bera 13.6 2.28 1.9 1.33 0.7 2.8 1.5 0.69 0.69 7.87Probability 0 0.32 0.39 0.52 0.7 0.25 0.47 0.71 0.71 0.02Anderson darling (A2) 43 0.23 1.85 0.66 1.69 1.41 0.42 0.43 0.42 0.28Probability 0.23 0.73 0 0 0 0 0.25 0.24 0.24 0.52
TDE MMMP MIS OG PO TEL TS TEX TSM TE
Jarque Bera 0.63 1.9 13.6 2.28 1.9 1.33 0.67 2.8 1.5Probability 0.73 0.39 0 0.32 0.39 0.52 0.72 0.25 0.47Anderson darling (A2) 1.05 0.37 0.32 0.44 0.41 0.83 0.21 0.55 1.06Probability 0 0.34 0.47 0.22 0.27 0.02 0.79 0.11 0
Appendix I. Units of Currency Measurement
1 Crore (1,00,00,000) ˘ 10 Million1 Lakh (1,00,000) ˘ 0.1 Million1 Million (1,000,000) ˘ 0.1 Crores1 Billion (1,000,000,000) ˘ 100 Crores1 Crore (1,00,00,000) ˘ 100 Lakh
24 Journal of Accounting, Auditing & Finance
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Authors’ Note
The views presented in the article are opinions of the authors, based on their research and experience,
and do not depict views of institution or countries to which the authors belong. All errors and omis-
sions are their own.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the authorship and/or publica-
tion of this article.
Funding
The author(s) received no financial support for the research and/or authorship of this article.
Acknowledgment
The authors gratefully acknowledge the technical support of Indian Institute of Technology, Department
of Management Studies (IIT Delhi), and Indian Institute of Finance. Yamini would like to convey special
thanks to her Chairman Prof. J. D. Agarwal, her professors in IIT Delhi, and her colleagues at IIF
Delhi—Prof. Aman Agarwal, Mr. Deepak Bansal, and Mr. Pankaj Jain for their assistance in preparation
of this article. Yamini would also like to thank the referees and the editorial board members of the
Journal of Accounting, Auditing & Finance (JAAF) for their valuable comments and recommendations.
Appendix J. Abbreviations Explanations to the Table 1
1. Z = Goal function to be minimized2. ROE = Return on equity3. d1
1 = Positive deviation from goal 14. d1
2 = Negative deviation from goal 1 (violating variable)5. ROGNS = Rate of growth of net sales6. d2
1 = Positive deviation from goal 27. d2
2 = Negative deviation from goal 2 (violating variable)8. ROGCE = Rate of growth of capital employed9. d3
1 = Positive deviation from goal 3 (violating variable)10. d3
2 = Negative deviation from goal 3 (violating variable)11. TDE = Total debt to equity ratio12. LTD = Long-term debt to equity13. PBIT = Profit before interest and taxes14. REFX = Revenue earning from foreign exchange15. PBDT = Profit before depreciation and taxes16. ROGGB = Rate of growth of gross block17. CEFX = Capital earning in foreign exchange18. ROGCE = Rate of growth of capital employed19. NWC = Networking capital20. DV = Debtors velocity21. PO = Payout22. MC = Market capitalization23. CFFI = Cash flow from investing activities24. PBIDTM = Profit before interest, depreciation, tax margin25. ROGPBIT = Rate of growth of profit before interest and taxes26. ROGNS = Rate of growth of net sales27. ROGLTD = Rate of growth of long-term debt28. ROGRE = Rate of growth retained earning
Agarwal et al. 25
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