Multiobjective Capital Structure Modeling - CiteSeerX

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:[email protected]

at PENNSYLVANIA STATE UNIV on May 17, 2016jaf.sagepub.comDownloaded from

http://jaf.sagepub.com/

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

2 Journal of Accounting, Auditing & Finance



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,

Agarwal et al. 3



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




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

Agarwal et al. 5



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.




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

Agarwal et al. 7



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




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

Agarwal et al. 9



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




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



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



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



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



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



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




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



Ap

pen

dix

C.

TD

EIn

dust

ryC

onst

rain

tEquat

ions

(19

Indust

ries

)

Expla

nat

ory

pow

erVar

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Var

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With

TD

EW

ith

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1A

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71

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79

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and

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95

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5D

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din

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8H

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ted

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E=

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88

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0.9

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None

None

(con

tinue

d)

18



Ap

pen

dix

C.

(continued

)

Expla

nat

ory

pow

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iable

sposi

tive

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With

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ith

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11

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None

11

Met

alan

dm

etal

pro

duct

TD

E=

0.0

45

11.0

98LT

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12

Mis

cella

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us

indust

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=1.4

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None

None

13

Oil

and

gas

indust

ryT

DE

=1.7

15

20.0

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0.8

68

0.7

54

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LTD

None

14

Pow

erT

DE

=0.5

23

10.9

62LT

D2

0.3

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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

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0.0

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GPA

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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

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CEPS,

BV,

EPS,

and

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17

Touri

smT

DE

=0.0

15

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D0.9

99

0.9

98

0.0

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LTD

None

18

Tran

sport

equip

men

tsT

DE

=0.1

96

10.1

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0.0

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0.0

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0.0

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None

None

19

Tran

sport

serv

ices

TD

E=

20.1

34

11.0

47LT

D1

0.0

05D

V2

0.0

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0.0

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11

0.0

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RO

GM

C,LT

DN

one

19



Ap

pen

dix

D.

LTD

Indust

ryC

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rain

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ions

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Indust

ries

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Expla

nat

ory

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erVar

iable

sposi

tive

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rrel

ated

(r=

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iable

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1A

gric

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None

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1.5

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11.0

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EN

one

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(con

tinue

d)

20



Ap

pen

dix

D.

(continued

)

Expla

nat

ory

pow

erVar

iable

sposi

tive

lyco

rrel

ated

(r=

0.9

0)

Var

iable

sneg

ativ

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corr

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0.9

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one

11

Met

alan

dm

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pro

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LTD

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0.0

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1.7

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0.0

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13

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14

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23

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97

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one

15

Tele

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LTD

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67

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16

Textile

LTD

=0.1

18

10.6

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DE

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0.0

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17

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Tran

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91

0.0

154

None

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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

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RO

GPB

IT,RO

GM

C,an

dT

DE

None

21



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

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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

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71.1

91.0

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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

ange

1.0

92.1

87.0

97.1

23.2

93.3

91.4

25.3

62.3

1.3

9A

vera

ge0.7

10.8

11.1

21.4

91.2

1.1

61.0

41.2

61

0.8

2

22



Ap

pen

dix

F.Su

mm

ary

of10

Year

sT

DE

for

19

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

1.1

1.0

61.4

31.6

71.8

82.0

62.0

62.1

82.1

41.4

11.0

62.1

81.1

21.7

2C

apital

goods

0.9

1.0

21.0

81.1

61.0

91.1

10.9

80.9

41

0.9

60.9

1.1

60.2

61.0

23

Chem

ical

and

pet

roch

emic

als

0.9

61.5

111.1

312.0

42.8

11.9

71.7

61.8

71.4

61.3

40.9

612.0

411.0

73.6

94

Consu

mer

dura

ble

s1.4

91.4

71.6

21.4

81.3

3.0

41.9

62.0

71.9

41.8

71.3

3.0

41.7

41.8

25

Div

ersi

fied

1.2

31.2

21.2

1.1

81.2

41.1

81.2

11.5

12.3

22.2

41.1

82.3

21.1

41.4

56

FMC

G0.7

70.8

0.7

40.9

51.0

10.9

92.0

20.7

40.6

70.6

60.6

62.0

21.3

60.9

37

Hea

lth

care

0.7

20.6

40.5

50.6

0.6

70.6

70.5

70.5

80.7

50.9

10.5

50.9

10.3

60.6

78

Housi

ng

rela

ted

1.2

41.4

1.8

81.5

72.3

21.9

31.5

11.7

12.8

81.3

1.2

42.8

81.6

41.7

79

Info

rmat

ion

tech

nolo

gy0.3

80.3

90.4

20.6

40.4

90.3

60.4

20.5

0.7

40.5

40.3

60.7

40.3

80.4

910

Med

iaan

dpublis

hin

g0.4

40.5

60.6

30.5

50.3

90.4

60.4

30.6

50.7

30.3

90.7

30.3

40.5

411

Met

al,m

etal

pro

duct

s,an

dm

inin

g1.2

91.9

61.1

61.5

3.5

74.1

81.6

1.0

81.2

10.8

30.8

34.1

83.3

51.8

4

12

Mis

cella

neo

us

1.1

91.2

91.4

31.3

71.3

21.3

61.4

11.1

91.1

20.9

30.9

31.4

30.5

11.2

613

Oil

and

gas

0.6

10.6

50.7

10.8

91.1

11.1

80.9

40.8

30.8

90.6

40.6

11.1

80.5

70.8

414

Pow

er1.0

60.7

70.7

81.8

91.3

71.8

82.0

41.8

61.5

91.5

40.7

72.0

41.2

61.4

815

Tele

com

0.8

0.6

40.6

1.4

22.8

1.3

61.6

11.4

11.4

82.0

70.6

2.8

2.2

1.4

216

Textile

s1.5

82.4

81.5

51.1

31.0

51.2

21.4

61.3

81.3

31.1

91.0

52.4

81.4

31.4

417

Touri

sm0.4

40.5

50.8

20.7

40.5

40.4

10.2

90.2

40.1

60.1

40.1

40.8

20.6

90.4

318

Tran

sport

equip

men

ts0.9

30.9

30.8

50.8

80.9

20.8

90.8

80.9

20.9

20.8

0.8

0.9

30.1

30.8

919

Tran

sport

serv

ices

1.3

11.0

51.8

96.8

23.1

71.8

51.6

32.2

50.9

20.6

80.6

86.8

26.1

42.1

6M

in0.3

80.3

90.4

20.5

50.3

90.3

60.2

90.2

40.1

60.1

4M

ax1.5

82.4

811.1

312.0

43.5

74.1

82.0

62.2

52.8

82.2

4R

ange

1.2

2.0

910.7

111.4

83.1

83.8

21.7

72.0

12.7

22.1

Ave

rage

0.9

71.0

71.6

2.0

21.5

31.4

81.3

1.2

61.2

81.1

1

23



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




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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