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Journal of Accounting, Auditing &
http://jaf.sagepub.com/content/27/3/359The online version of this article can be found at:
DOI: 10.1177/0148558X11409156
20112012 27: 359 originally published online 30 JuneJournal of Accounting, Auditing & Finance
Yamini Agarwal, K. Chandrashekar Iyer and Surendra S. YadavProgramming Model Using Accounting Proxies
Multiobjective Capital Structure Modeling : An Empirical Investigation of Goal
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Journal of Accounting,
Auditing & Finance27(3) 359385
The Author(s) 2012
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DOI: 10.1177/0148558X11409156
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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 competitive
business environment. Theories and models of 1950s are unable to incorporate the
demands faced by the decision maker. New models are needed to incorporate multiple
objectives and constraints. Stakeholders are awfully demanding. Practitioners attempt to
innovatively build the capital structures to meet the needs of all stakeholders. Off and on
balance sheet exposure contributes to financial commitments. In the light of this back-
ground, the present study investigates the Indian corporates for their capital structure
choices and builds a goal programming model for CSDs. Capital structure practices in India
are 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 considerations
before a decision maker. The study has also explored the relationship of leverage ratio with
market 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 capital
structure practices. The study has attempted to develop a goal programming (GP) model
for 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 and
does not attempt to satisfy all objectives fully. GP model has been developed and illustrated
for CSDs through agriculture-based firm having multiple objectives that are proxied usingaccounting variables.
Keywords
capital structure decisions, multicriteria decision making, Indian corporates, goal
programming model
1
Indian 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:[email protected]
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Theories and principles on capital structure decisions (CSDs) developed in 1950s have lost
their relevance in todays 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. CEOs or a firms 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 firms 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 firms 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 todays 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 thedecision 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 IndianIndustry 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
chief financial officers (CFOs) are assessed using a questionnaire approach that contains
19 questions and subquestions (Y. Agarwal, Iyer, & Yadav, 2009). We identified 96
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qualitative and quantitative considerations (Iyer & Agarwal, 2007) which a CEO/CFO
evaluates simultaneously 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 indus-
tries form the leverage constraint (for TDE see Appendix C and for LTD see AppendixD) 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 developed 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 20industries, finance industry (consisting of 56 companies) was not considered for evalua-
tion as it contained banks, nonbanking financing companies (NBFCs), and financial insti-
tutions 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 con-
tained 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 influenceover 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 cor-
relation 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,
Agarwal et al. 361
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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 singleobjectivethe 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
firms strategy to reduce cost that altered capital structures and increased the value of a
firm.
Similar to market timing, firms 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 thatcredit 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 areoften restricted to choose equity because of low security of creditors 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 IndiaIndia 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
Agarwal et al. 363
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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 assetbased industries like chemical and petrochemical firms
and low tangible assetbased firms like information technology.
On the assessment of firms in different industries, some industries were found to havenormal 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
distributionfor 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, mTDEwere used in the study to estimate the relationship between leverage and market capitaliza-
tion. Correlations between mEPS of 19 industries (for 10 years) andmLTD, 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 bepositively 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 MakingThe 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 receivesufficient 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 Weingartners linear programming solution. However, Hawkins and Adams havenot 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 ofcurrent 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
Agarwal et al. 365
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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 pj11greater 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 andd such that
c5d1pj1; d2pj2; . . . d2mpj2m 6:1
where pji (i = 1, 2, . . . 2m; j = 1, 2, . . . k) are preemptive priority factors, and highest pre-
emptive factorp1 anddis (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 d1
;d2
; . . . dm; d1
1;d1
2; . . . d1m
6:2
Then a GP problem isMinimize cd
Subject to Ax1Rd5b
x;d! 0
6:3
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 studiedfor their relationship with other variables through correlation and stepwise regression that
develop the constraints that the industry possess 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 by8.5%. 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 debtors velocity of 48 days, and the payout maintained by the a1company 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 firms 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 fora company is as follows:
Objective : Minimize z5d1
1d21
1d3
1d31
Subject to:
Goal Constraint 1 : ROE 2 d11
1 d12 = 23.731
Goal Constraint 2 : ROGNS 2 d21 1 d22 = 8.51
Goal Constraint 3 : ROGCE 2 d31
1 d32 = 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 = 0.399ROGCE2
0.0105ROGPATFirm 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 = 42.38Firm Constraint 12 : MC . 401.87
Firm Constraint 13 : CEFX = 0
Firm Constraint 14 : PBDT . 166.24
368 Journal of Accounting, Auditing & Finance
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Table1.
GoalProgrammingSolutionfora1
CompanyUsingAccountingProxiesforGoalsandConstraints
ObjectiveFunction:Minm
izez5d11d121d31d13
ObjectiveFunc
tion
z=0;
DECISIONVA
RIABLES:
LTD=0;ROGLT=0;
NonBasicVariables
d1151;d150;d12
50;d251;d1351;d5
1
Variables(27)
ROGNS,ROGRE;ROGCE;ROE;ROGPB;TDE;
PBDT;PBIT;ROGGB;MC;PO;
ROGPAT;CFFI;REFX;
NWC;PBDTM;CEFX;FAR;DV;LTD;ROGLT:d
1;
d1;
d2
;
d2;
d3
;
d3
S.No.
Constraints
Targetvalue
Solution
Deviations
Sen
sitivityanalysis
RHSrange
Goals1.
ROE2
d1
1
1
d12
=23.730a
ROE=23.730
d1
1
=1
7.9251-39.8025
d12
=0
2.
ROGNS2
d2
1
1
d22
=8.500a
ROGNS=8.500
d2
1
=0
0.0000-1}
d22
=1
3.
ROGCE2
d3
1
1
d32
=23.250a
ROGCE=23.250
d3
1
=1
0.0000-63.4660
d32
=1
Industry
4.
TDEb2
0.979LTD1
0.0007PBIT2
0.003REFX
2
0.002PBDTM2
0.002RO
GGB
2
0.040CEFX2
0.001ROGCE1
0.001FAR
=1.071
TDE=0.119
0.3400-1}
LTD=0.000
PBIT=Rs.257.310cr
REFX=Rs.163.920cr
PBDTM=Rs.166.240cr
ROGGB=Rs.3.870%
CEFX=0.000cr
ROGCE=23.250%
FAR=6.550%
5.
1.085TDE1
LTDc1
0.001NWC2
0.016DV1
!
0.081
TDE=0.119
20.0476-0.8741
(continued)
369
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Table1.(continued)
S.No.
Constraints
Targetvalue
Solution
Deviations
Sen
sitivityanalysis
RHSrange
0.013PO1
0.001CFFI1
0.010PBTM1
0.008ROE
LTD=0.000
NWC=147.330cr
DV=48.000days
PO=16.790%
CFFI=42.380cr
PBTM=12.260cr
ROE=23.730%
Firm 6.
20.393ROGCE1
ROEd1
0.0105ROGPA
!
0.000
ROGCE=23.250%
S1=15.805
2}
-15.8049
ROE=23.730%
ROGPA=115.440%
7.
ROGCEe2
6.71ROGLT2
74.31ROGRE
!
0.000
ROGCE=23.250%
2}
-23.2500
ROGLT=0.000%
ROGRE=0.313%
8.
ROGPBf2
5.717ROGNS
!
0.000
ROGPB=48.595%
248.5945-1}
ROGNS=8.500%
9.
145.25TDE2
172LTD1
ROGPATg1
0.21ROGPB
!
0.000
TDE=0.119
S2=142.828
2}
-142.8283
LTD=0.000
ROGPA=115.440%
ROGPB=48.595%
10.
PBDTh
!
166.240
PBDT=166.240cr
0.0000-1}
11.
PBITi
!
152.880
PBIT=257.310cr
S3=104.430
2}
-257.3104
12.
ROGGBj
!
3.870
ROGGB=3.870%
0.0000-1}
13.
MCk
=401.000
MC=401.000cr
0.0000-1}
14.
DVl
=48.000
DV=48.000days
39.9638-97.5718
15.
POm
=16.790
PO=16.790%
0.0000-26.6808
16.
ROGPATn
!
115.440
ROGPA=115.440%
0.0000-1}
17.
CFFIo
=42.380
CFFI=42.380cr
0.0000-170.9600
(continued)
370
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Table1.(continued)
S.No.
Constraints
Targetvalue
Solution
Deviations
Sen
sitivityanalysis
RH
Srange
18.
REFXp
=163.920
REFX=163.920cr
0.0000-1}
19.
211.91TDE1
NWCq
!
0.000
TDE=0.119
S4=145.919
2}
-145.9186
NWC=147.330cr
0.0000-25.1180
20.
PBIDTMr
=12.260
PBTM=12.260cr
21.
CEFXs
=0.000
CEFX=0.000cr
0.0000-1}
22.
FARs
=6.550
FAR=6.550
0.0000-737.5631
23.
NWCt
!
147.330
NWC=147.330cr
2.9958-275.9100
Objectivefunc
tion:Minimizez
=d12
1
d2
11
d32
1
d3
1
Objective
function
z
=0
Decision
variables
LTD=0;ROGLT=0
Nonbasic
variables
d1
1
=1,d12
=0,d2
1
=0,
d22
=1,d3
1
=1,d32
=1
Note:SolutionisobtainedusingPOMSoftware.S1,S2,S3,S4
areslackvariables.
aTargetvaluesfo
rthegoalsarebasedonthefirmsp
referencesanddeterminedwiththe
helpofthemanagementparticipation.
bTotaldebttoequity(TDE)intheagricultureindust
ryisdependentonlong-termdebt(LTD),profitbeforeinterestandtax
(PBIT),revenueearninginforeigne
xchange(REFX),
rateofgrowthinprofitbeforeinterest,depreciationandtax(ROGPBIDT),rateofgrow
thofgrossblock(ROGGB),capitalearninginforeignexchange(CEFX),rateofgrowth
ofcapitalemployed(ROGCE),andfixedassetratio(FAR).Thishasbeenidentifiedthroughthestepwiseregression,pleaser
eferAppendixC.
cLong-termdebttoequity(LTD)intheagricultureindustryisdependentontotaldebttoequity(TDE),networkingcapital
(NWC),debtorsvelocity(DV),payout(PO),market
capitalization(M
C),cashflowfrominvestingactivities(CFFI),profitbeforeinterest,depreciation,taxmargin(PBIDTM),capitalearninginforeignexchange(CEFX).Thishas
beenidentifiedt
hroughastepwiseregression,pleasereferAppendixD.
dRateofreturn
onequity(ROE)isdependentontherateofgrowthofcapitalemploye
d(ROCE)andrateofgrowthofpr
ofit(ROGPAT),whichhasbeendev
elopedusingthe
firms10yearsd
ataandmultipleregressionanalysis.
eRateofgrowth
ofcapitalemployed(ROCE)isde
pendentonrateofgrowthofretainedearnings(ROGRE)andrateofgrowthoflong-termdebt(ROGLT
D).Therateof
growthofpaidu
pequityisnotconsideredastheeq
uityinthepast10yearshasremainedconstantatRs.1.29croresandthefirmdoesnotintendtochangeROGCE.
f Rateofgrowth
ofprofitbeforeinterestandtaxes(ROGPB)isdependentontherateofgrowthofnetsales(ROGNS).
gRateofgrowth
ofprofitaftertax(ROGPAT)isdependentonlong-termdebt(LTD),totaldebttoequity(TDE),rateofgro
wthofprofitbeforeinterestandtax
es(ROGPBIT).
hFirmswantsthatprofitbeforedepreciationandtax
(PBDT)shouldnotfallbelowthepresentlevelofRs.166.24crores.
i Profitbeforeinterestandtaxes(PBIT)hastobehig
herthanthepresentlevelofoperationsintheyear2007atRs.153.88crores.
371
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Table1.(continued)
___________
_______________________________________________
_______________________
__________________________________
j Rateofgrowthofgrossblock(ROGGB)is3.88,whichcanbegreaterthantheprevious
yearasthefirmintendstopurchaseequipments.
kMarketcapitaliz
ationisattemptedtobehigherthan
thepresentlevel,managementisnotinterestedinmaintainingitsmarketcapitalizationandonlyinincreasin
git.
l Firmintendsto
maintainitsdebtorsvelocityat48days,itmaychoosetoreduceitinfuturebutnotatpresent.Firmdoes
notintendtoincreaseitaswouldthenincreaseits
requirementfor
thenetworkingcapital.
mThefirmintendstokeepitspayoutratio(PO)at16.42%.
nThefirmintend
stohaveitsrateofgrowthofprofitaftertax(ROGPAT)morethanRs.
115.440crores.
oThefirmstands
investedinamannerthatprovides
forcashfrominvestingactivities(CFFI)whichisRs.42.53croresandthereisnoscopeforimprovement.
pFirmdoesnot
havecapitalearningfromforeignexchange(CEFX)anddoesnotinten
dtohavethesameinfutureandintendstomaintainitsrevenueearnings(REFX)at
163.92crores.
qNetworkingca
pital(NWC)andtotaldebttoequity(TDE)relationshiphasbeendetermined,keepingTDEasindependentandassumingthatcurrentliabilities
financemostof
thecurrentassetsandthetotaldebtisusedtofinan
ceit.
rThefirmwithit
soperationhasprofitbeforeinterest,depreciation,andtaxmargin(PBIDTM)asRs.12.29crores,whichisretainablewithcostefficiencies.
sThefirmissatis
fiedwithitsfixedassetratio(FAR)of6.550.
tNetworkingcapital(NWC)ofthefirmwithpresen
toperationisRs.147.31crores,and
itcannotreduceitwithitspresent
formofoperationandterms.
372
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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 todays complex and dynamic business world with
accounting information.
Agarwal et al. 373
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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) CL
Total 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 PBT
Profit 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 1.20
12 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
374 Journal of Accounting, Auditing & Finance
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Appendix B. (continued)
Variables Abbreviations
Capital earnings in forex CEFX
Capital 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. 375
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AppendixC.
TDEIndustryConstraintEquations(19Industries)
Explanatorypower
Variablespositively
correlated(r=0.90)
Variablesnegatively
co
rrelated(r=20.90)
S.No.Industry
TDEconstrainte
quation
R
R2
SE
WithTDE
W
ithTDE
1
Agricultu
re
TDE=1.0711
0.979LTD2
0.0007PBIT1
0.003REFX1
0.002ROGPBIDT1
0.002ROGGB1
0.040CEFX1
0.001ROGCE
1
0.001FAR
1
1
79
PBDTM,CPM,PAT,PBT,PBDT,MC,
CP,and
LTD
N
one
2
Capitalg
oods
TDE=0.7541
0.001ROGMC2
0.002CFFF1
0.014PBIDTM
0.9760.9530.02048CFFO,EP,ROGCOP,ICR,OI,IR,
CEXFX,DR,PBIDTM,CV,PBDTM,
ROCE,PBITM,CPM,LTD,CEFX,
andPO
VOTA
3
Chemica
land
petrochemicals
TDE=22.2951
1.502LTD2
0.000ROGCP1
0.003CE
1
0.9990.1216LTD
N
one
4
Consumerdurables
TDE=0.3971
1.21LTD1
0.028OI2
0.013CEPS20
.002PBT
1
0.9990.01933LTD
N
one
5
Diversifiedindustry
TDE=0.5301
1.087LTD2
0.073CR1
0.008EPS
0.9990.9980.02427LTD
N
one
6
FMCG
TDE=1.0381
0.092VOGB2
0.008APATM
1
0.9990.08282REXFX,R
OGNS,ROGTA,IR,
ROGGS
,REFX,CEXFX,ICR,
REXFX,
MC,PAT,PBT,CP,
NWCPBDT,DIV,PBIT,PBIDT,NET,
CFFO,C
L,CA,CE,OI,COP,VOGB,
NS,VO,GS,CEPS,GBTAL,SC,
PBITM,EPS,ROCE,PO,PBIDTM,
DV,BV,CR,DR,FAR,EP,CV,VOTA,
andLTD
CFFF,CFFI
7
Healthcare
TDE=0.1421
1.155LTD
0.9930.9870.0122LTD
N
one
8
Housing
related
TDE=0.1881
1.022LTD1
0.000ROGMC
0.9990.9990.031071LTD
PBIDTM
9
Informat
ion
technology
TDE=20.0701
2.069LTD2
0.001DIV
0.9660.9340.0335None
N
one
(continued)
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AppendixC.
(continued)
Explanatorypower
Variablespositively
correlated(r=0.90)
Variablesnegatively
co
rrelated(r=20.90)
S.No.Industry
TDEconstrainte
quation
R
R2
SE
WithTDE
W
ithTDE
10
Mediaan
dpublishing
TDE=0.4341
0.866LTD1
0.139CR1
0.005CV1
0.000PBT2
0.001DV1
0.000EP
1
1
0.0007LTD
N
one
11
Metalandmetal
produc
t
TDE=0.0451
1.098LTD1
0.001ROGGS1
0.000ROGPBID
T1
0.002ICR1
0.000ROGGB
2
0.002CV
1
1
0.0049LTD
N
one
12
Miscellan
eousindustryTDE=1.4702
0.062APATM1
0.001ROGMC
0.9490.9010.0497None
N
one
13
Oilandgasindustry
TDE=1.7152
0.004ROGPBDT
0.8680.7540.05673LTD
N
one
14
Power
TDE=0.5231
0.962LTD2
0.3.1CPM1
0.021
PBITM
0.9990.9990.0183LTD
N
one
15
Telecom
TDE=21.1681
1.525LTD2
0.361VOGB1
0.000PAT10.0
01ROGPAT
1
1
0.0163LTD
N
one
16
Textile
TDE=20.1761
1.493LTD1
0.001ROGMC1
0.031FAR
1
0.9990.01369CEPS,BV,EPS,andLTD
17
Tourism
TDE=0.0151
1.049LTD
0.9990.9980.01134LTD
N
one
18
TransportequipmentsTDE=0.1961
0.1073LTD1
0.004CV2
0.014CPM
0.9680.9370.01185None
N
one
19
Transportservices
TDE=20.1341
1.047LTD1
0.005DV2
0.001ROGMC1
0.005ROCE
1
1
0.005032ROGMC,
LTD
N
one
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AppendixD.
LTDIndustryConstraintEquations(19Industries)
Explanatorypower
Variablespositively
correlated(r=0.90)
Variablesnegatively
correlated(r=20.90)
S.No.
Industry
LTD
constraintequation
R
R2
SE
withLTD
withLTD
1
Agricult
ure
LTD=20.8121
1.085TDE1
0.001NWC2
0.016DV10.013PO1
0.000MC1
0.001CFF
I
1
0.010PBIDT
M1
0.008CEFX
1
1
0.0012
TDE
APATM
2
Capitalgoods
LTD=0.3041
0.000PO2
0.571TDE2
0.157CR
1
1
0.01146CFFO,EP,CFFF,ROGCOP,ICR,OI,
IR,CEXFX,DR,PBIDTM,CV,
PBDTM
,ROCE,PBITM,CPM,CEFX,
PO,andTDE
VOTA
3
Chemicaland
petrochemicals
LTD=1.5741
0.664TDE2
0.000ROGCP2
0.002CE
1
0.9990.08091EP,ICR,
GB,CPM,APATM,REFX,
ROGN
S,ROGGS,PBDTM,CFFO,
ROGM
C,andTDE
None
4
Consum
erdurables
LTD=1.5091
1.001TDE1
0.035VOGB1
0.000REXFX
1
0.9960.0417
TDE
None
5
Diversifiedindustry
LTD=20.1331
0.0839TDE2
0.002CFFO
10.001ROGC
P1
0.001RONW
1
0.001ROGGS
1
1
0.00731TDE
None
6
FMCG
LTD=20.0131
0.582TDE
0.990.9820.1774
ROGGS,ROGPAT,IR,REFX,CEXFX,
ICR,REXFX,PAT,MC,PBT,CP,
PBDT,
PBIDT,CFFO,NWCCL,NET,
CA,OI,SC,CEPS,COP,VOGB,NS,
VO,CE,DIV,PBIT,GS,EPS,GB,
ROCE,TAL,PBITM,BV,PBIDTM,
DV,PO
,DR,FAR,EP,CV,VOTA,CR,
andTD
E
CFFF,CFFI
7
Healthcare
LTD=20.0461
0.857TDE2
0.004ROCE
1
1
0.00055DR,CFF
I,CEPS,VOTA,PO,CR,
BVVOGB,FAR,andTDE
None
8
Housing
related
LTD=20.1791
0.976TDE1
0.000ROGMC
1
0.9990.03036PO,ROG
PAT,BV,ROGCE,CFFI,
VOTA,ROGNW,andTDE
PBIDTM
9
Information
technology
LTD=20.0251
0.476TDE1
0.001DIV1
0.000CFFF1
0.006FAR
0.970.9340.0335
None
None
(continued)
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AppendixD.
(continued)
Explanatorypower
Variablespositively
correlated(r=0.90)
Variablesnegatively
correlated(r=20.90)
S.No.
Industry
LTD
constraintequation
R
R2
SE
withLTD
withLTD
10
Mediaa
ndpublishing
LTD=0.1301
0.707TDE2
0.005RONW
0.970.930.02422TDE
None
11
Metalandmetal
product
LTD=20.0411
0.911TDE1
0.001ROGGS1
0.002ICR10.000ROGGB2
0.002CV1
0.000ROGPBIDT
1
1
0.0044
TDE
None
12
Miscella
neousindustryLTD=1.7152
0.072ROCE1
0.005ROGTA
0.970.9390.0394
TDE
ROCE
13
Oiland
gasindustry
LTD=0.3111
0.150CEPS2
0.205EPS
1
0.990.19315TDE
None
14
Power
LTD=20.0231
0.883TDE2
0.000CL1
0.000CFFF
1
0.9970.0309
TDE
None
15
Telecom
LTD=0.7671
0.655TDE2
0.237VOGB1
0.000PAT10.000ROGPAT
1
1
0.0107
TDE
None
16
Textile
LTD=0.1181
0.669TDE2
0.001ROGMC
1
0.9990.00916TDE
None
17
Tourism
LTD=0.0131
0.951TDE
1
0.9980.0108
TDE
None
18
Transpo
rtequipmentsLTD=0.1261
0.554TDE
0.830.6910.0154
None
None
19
Transpo
rtservices
LTD=0.1281
0.955TDE2
0.005DV1
0.001ROGMC
2
0.004ROGCE
1
1
0.0048
ROGPBIT,ROGMC,andTDE
None
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AppendixE.
Summaryof10YearsLTDfor
19Industries
S.No.
Industry
2007
2006
2005
2004
2003
2002
2001
20001
999
1998
Min
Max
Range
Average
1
Agriculture
0.73
0.69
0.85
0.97
1.13
1.27
1.35
1.58
1.6
0.81
0.7
1.6
0.91
1.1
2
Chem
icalandpetrochemicals
0.58
0.94
7.4
7.45
2.11
1.49
1.07
1.2
1
0.94
0.6
7.5
6.87
2.42
3
Powe
r
0.59
0.51
0.49
1.66
1.08
1.57
1.7
1.55
1.36
1.36
0.5
1.7
1.2
1.19
4
Transportservices
1.17
0.96
1.78
6.74
3.02
1.74
1.53
2.15
0.81
0.67
0.7
6.7
6.06
2.06
5
Consumerdurables
0.61
0.61
0.75
0.75
0.71
2.4
1.4
1.54
1.31
1.11
0.6
2.4
1.8
1.12
6
Capitalgoods
0.57
0.64
0.67
0.77
0.73
0.72
0.62
0.61
0.64
0.61
0.6
0.8
0.21
0.66
7
Diver
sified
0.86
0.82
0.78
0.8
0.83
0.75
0.9
1.16
1.86
1.72
0.8
1.9
1.1
1.05
8
FMCG
0.51
0.49
0.35
0.34
0.5
0.49
1.62
0.37
0.36
0.33
0.3
1.6
1.29
0.54
9
Healthcare
0.51
0.44
0.35
0.39
0.44
0.44
0.38
0.4
0.53
0.66
0.4
0.7
0.31
0.45
10
Housingrelated
1
1.11
1.56
1.31
2.06
1.7
1.28
1.45
2.66
1.07
1
2.7
1.66
1.52
11
Inform
ationtechnology
0.26
0.29
0.31
0.44
0.3
0.21
0.27
0.27
0.38
0.33
0.2
0.4
0.22
0.31
12
Mediaandpublishing
0.35
0.41
0.51
0.48
0.34
0.41
0.39
0.58
0.56
0.3
0.6
0.25
0.45
13
Metal,metalproducts,andmining
1.36
2.48
0.92
1.24
3.59
3.6
1.61
1.16
1.16
0.74
0.7
3.6
2.86
1.79
14
Misce
llaneous
0.58
0.68
0.79
0.77
0.8
0.91
1.07
0.77
0.68
0.58
0.6
1.1
0.49
0.76
15
Oilandgas
0.48
0.52
0.59
0.65
1.03
0.98
0.7
5.63
0.73
0.5
0.5
5.6
5.16
1.18
16
Telecom
0.66
0.54
0.56
1.36
2.02
0.97
1.19
1.02
1.01
1.22
0.5
2
1.49
1.05
17
Textiles
1.04
1.62
0.97
0.67
0.66
0.77
0.95
0.89
0.87
0.84
0.7
1.6
0.95
0.93
18
Tourism
1.09
1.08
1.03
0.83
0.82
0.96
1.15
1
0.86
0.73
0.7
1.2
0.42
0.96
19
Transportequipments
0.6
0.62
0.58
0.63
0.62
0.57
0.58
0.63
0.61
0.53
0.5
0.6
0.1
0.59
Minim
um
0.26
0.29
0.31
0.34
0.3
0.21
0.27
0.27
0.36
0.33
Maxim
um
1.36
2.48
7.4
7.45
3.59
3.6
1.7
5.63
2.66
1.72
Range
1.09
2.18
7.09
7.12
3.29
3.39
1.42
5.36
2.3
1.39
Avera
ge
0.71
0.81
1.12
1.49
1.2
1.16
1.04
1.26
1
0.82
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AppendixF.
Summaryof10YearsTDEfor
19Industries
S.No.
Industry
2007
2006
2005
2004
200
3
2002
2001
2000
1999
1998
Min
Max
Ra
nge
Average
1
Agric
ulture
1.1
1.06
1.43
1.67
1.8
8
2.06
2.06
2.18
2.14
1.41
1.06
2.18
1
.12
1.7
2
Capitalgoods
0.9
1.02
1.08
1.16
1.0
9
1.11
0.98
0.94
1
0.96
0.9
1.16
0
.26
1.02
3
Chem
icalandpetrochemicals
0.96
1.51
11.13
12.04
2.8
1
1.97
1.76
1.87
1.46
1.34
0.96
12.04
11
.07
3.69
4
Cons
umerdurables
1.49
1.47
1.62
1.48
1.3
3.04
1.96
2.07
1.94
1.87
1.3
3.04
1
.74
1.82
5
Diversified
1.23
1.22
1.2
1.18
1.2
4
1.18
1.21
1.51
2.32
2.24
1.18
2.32
1
.14
1.45
6
FMCG
0.77
0.8
0.74
0.95
1.0
1
0.99
2.02
0.74
0.67
0.66
0.66
2.02
1
.36
0.93
7
Healthcare
0.72
0.64
0.55
0.6
0.6
7
0.67
0.57
0.58
0.75
0.91
0.55
0.91
0
.36
0.67
8
Hous
ingrelated
1.24
1.4
1.88
1.57
2.3
2
1.93
1.51
1.71
2.88
1.3
1.24
2.88
1
.64
1.77
9
Informationtechnology
0.38
0.39
0.42
0.64
0.4
9
0.36
0.42
0.5
0.74
0.54
0.36
0.74
0
.38
0.49
10
Mediaandpublishing
0.44
0.56
0.63
0.55
0.3
9
0.46
0.43
0.65
0.73
0.39
0.73
0
.34
0.54
11
Metal,metalproducts,
and
mining
1.29
1.96
1.16
1.5
3.5
7
4.18
1.6
1.08
1.21
0.83
0.83
4.18
3
.35
1.84
12
Misce
llaneous
1.19
1.29
1.43
1.37
1.3
2
1.36
1.41
1.19
1.12
0.93
0.93
1.43
0
.51
1.26
13
Oilandgas
0.61
0.65
0.71
0.89
1.1
1
1.18
0.94
0.83
0.89
0.64
0.61
1.18
0
.57
0.84
14
Powe
r
1.06
0.77
0.78
1.89
1.3
7
1.88
2.04
1.86
1.59
1.54
0.77
2.04
1
.26
1.48
15
Telecom
0.8
0.64
0.6
1.42
2.8
1.36
1.61
1.41
1.48
2.07
0.6
2.8
2
.2
1.42
16
Textiles
1.58
2.48
1.55
1.13
1.0
5
1.22
1.46
1.38
1.33
1.19
1.05
2.48
1
.43
1.44
17
Tourism
0.44
0.55
0.82
0.74
0.5
4
0.41
0.29
0.24
0.16
0.14
0.14
0.82
0
.69
0.43
18
Trans
portequipments
0.93
0.93
0.85
0.88
0.9
2
0.89
0.88
0.92
0.92
0.8
0.8
0.93
0
.13
0.89
19
Trans
portservices
1.31
1.05
1.89
6.82
3.1
7
1.85
1.63
2.25
0.92
0.68
0.68
6.82
6
.14
2.16
Min
0.38
0.39
0.42
0.55
0.3
9
0.36
0.29
0.24
0.16
0.14
Max
1.58
2.48
11.13
12.04
3.5
7
4.18
2.06
2.25
2.88
2.24
Range
1.2
2.09
10.71
11.48
3.1
8
3.82
1.77
2.01
2.72
2.1
Average
0.97
1.07
1.6
2.02
1.5
3
1.48
1.3
1.26
1.28
1.11
381
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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 0.77
Probability 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
382 Journal of Accounting, Auditing & Finance
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Acknowledgments
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
DelhiProf. 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.
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.
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 1
4. d12
= 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 exchange
15. 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 taxes
26. ROGNS = Rate of growth of net sales27. ROGLTD = Rate of growth of long-term debt28. ROGRE = Rate of growth retained earning
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