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INSTITUTE OF BUSINESS ADMINISTRATION
Advance Applied Business Research Report
Capital Structure and Profitability: A comparative study between of Pharmaceutical and Food Sector of
Pakistan.
Adnan, Tayyab, Khateeb , Asad & Yasir.
April’ 2015
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Submitted to:
Mr. Muhammad Abdul Salam
Course Instructor
Advance Applied Business Research
Submitted By:
Adnan Hussain (ERP # 08702)
Tayyab Nihal (ERP # 08695)
Khateeb Hussian (ERP # 08692)
S.M. Asad (ERP # 08691)
Yasir Shahbaz (ERP # 08714)
Students of
EMBA-3 Summer Trimester
Submission Date:
23rd April 2015
2 | P a g e
Contents
TOPIC ............................................................................................................................. 3
EXECUTIVE SUMMARY ................................................................................................. 3
INTRODUCTION ............................................................................................................. 4
RESEARCH OBJECTIVE................................................................................................ 5
LITERATURE REVIEW ................................................................................................... 5
HYPOTHESIS ................................................................................................................. 7
RESEARCH METHODOLOGY ....................................................................................... 7
DATA ANALYSIS AND INTERPRETATION .................................................................. 10
CONCLUSION .............................................................................................................. 14
RECOMMANDATIONS ................................................................................................. 14
REFERENCE/ BIBLIOGRAPHY: ................................................................................... 15
SPSS File of Pharmaceutical Sector ............................................................................. 17
SPSS File of Food Sector ............................................................................................. 28
3 | P a g e
TOPIC
To find the relationship between Capital Structure and Profitability: A comparative study
between of Pharmaceutical and Food Sector of Pakistan.
EXECUTIVE SUMMARY
The basic drive of this research was to investigate the nexus between performances and
capital structure of the businesses in two different industries of Pakistan i.e.
Pharmaceutical and Food sector. The data was collected form financial statements of
the companies registered at Karachi Stock Exchange (KSE 100) from year 2010 to
2014.
Total 13 food companies and 08 pharmaceutical companies are registered in Karachi
Stock Exchange. For analysis purpose we have selected 05 best performing companies
of each section were selected. The objective of this research is to find the relationship
between profitability and capital structure of the overall sector and for that we have
selected Return of Asset (ROA) as dependent variable of profitability and represented
capital structure through Debt to Asset Ratio, Long Term Debt to Asset Ratio, Short
Term Debt to Asset Ratio and Debt to Equity Ratio (independent variables).
Regression model is used for the analysis and analysis has revealed that about 61% of
variability in ROA is explained by above mentioned independent variables in
pharmaceutical sector; while for food sector only 37% ROA variability is explained.
Important conclusion drawn by this research is that profitability in pharmaceutical sector
is positive to structures based on more debt and less equity portions. However, food
sector in Pakistan is not much positive in terms of its profitability to the capital structures
based on more debt.
4 | P a g e
INTRODUCTION
A business organization is called business organization because of its economic
objectives i.e. Profit. Different techniques are followed by business organizations for
profit maximization working under the concept of efficiency. Among them an optimal mix
of financing (Capital Structure) is dominant. Capital Structure means a combination of
various fund sources required for the working capital and long investments. Availability
of mix of debt and equity sources instruments and increased opportunities but had also
increased complexities too. Financial sources are different based on risk related to each
source of fund according to the relation of risk and return. The financial managers
struggle to reach to such type of mix having low cost and increase the wealth of the
shareholders.
The relationship of the capital structure decisions with the firm performance was
highlighted by a number of theories mainly, the agency theory, information asymmetry
theory, signaling theory and the tradeoff theory. The most important among them is the
agency problem that exists because ownership (shareholders) and control
(management) of firms lies with different people for most of the firms. And for that
reason, managers are not motivated to apply maximum efforts and are more interested
in personal gains or policies that suit their own interests and thus results in the loss of
value for the firm and harm shareholders’ interests. Therefore, debt finance act as a
controlling tool to restrict the opportunistic behavior for personal gain by managers. It
reduces the free cash flows with the firm by paying fixed interest payments and forces
managers to avoid negative investments and work in the interest of shareholders.
The asymmetric information theory states that the firm managers (insiders) have
more information about their firm compared to the outside investors. The well informed
managers try to send positive information to the market or ill informed investors to
increase the firm value.
Signaling theory states that managers have incentives to use various tools to send
signals to the market about the difference that exist between them and weaker firms.
One of the key tools to send these signals is the use of debt. Employment of debt in
capital structure shows that managers have better expectations about the future
performance whereas equity sends a bad news about the firm performance in the future.
Various research studies were conducted to check the influence of capital structure
decisions on firm performance. As capital structure is mainly based on two sources of
finances that is debt and equity. The use of each source of financing show mixed and
contradictory results on the firm performance. Hadlock and James, (2002) in his study
on undervalued firms found a positive relationship between the use of debt finance and
firm performance, as debt finance mainly from banks reduces information asymmetry
problems and increases investors’ confidence in the firm. Simerly and Li (2000) found in
their study that environmental dynamism and competitive environment play a key role in
5 | P a g e
making decisions about the optimal capital structure. Firms in the underdeveloped
market are faced with financial distress and volatility in interest rates, inflation and tax
rates play a significant role in taking decisions about the optimal capital structure
decisions (Karadeniz et al. 2009). Pakistan is a developing country and has a very small
and undeveloped debt market so firms rely largely on the bank debt to finance its
operations and capital investment needs. Since a major proportion of the banks in the
country are privatized and they do not issue debt finance on attractive terms. The firms
with more uncertain earnings (volatile) earnings find it much more difficult to get to these
sources of finances. So the firms with more uncertain earnings are restricted to borrow
less in these markets. Similarly, consistently increasing cost of raising finances to run
their business smoothly have restricted the firms in Pakistan to largely rely on the
internal sources of funds because the equity markets are limited and always on lower
levels of trading. The existence of information asymmetry problems in the Pakistani
market is also a relevant concern in the decisions of capital structure (Sheikh and Wang,
2011).
RESEARCH OBJECTIVE
1. To explore and describe the share of capital structure in profitability.
2. To find out the impact of capital structure on:
a. Debit
b. Capital Investment
3. To determine basis of the profitability function of food and pharma sector
4. To identify combination of Capital Structure that would be best for the company.
LITERATURE REVIEW
The significance of capital structure theories to firm performance and its value was
highlighted by various researchers in their research work over the decades across the
developed world. The importance of capital structure theory to firm performance was first
highlighted by Modigliani and Miller (1958) stating that the decision about company’s
capital structure is immaterial to the value of the firm in the absence of taxes,
asymmetric information, bankruptcy costs, transactions cost and in an efficient markets
with homogeneous expectations. Under these strict assumptions, the type of financing
used does not affect the firm value. As the real world markets do not operate on these
assumptions and new research work was conducted to test the relationship between
capital structure theories with firm performance. Jensen and Meckling (1976)
demonstrates that in the decisions about a firm capital structure, the agency conflicts
between shareholders and managers is affected by the level of leverage, as it
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encourage or constrain managers to take decisions in the interest of shareholders and
their operating decisions and behaviors affects the firm performance. In similar way,
importance of capital structure decisions in firm performance were explored both
empirically and theoretically. Myers and Majluf (1984) in their study on firms capital
structure said that firms are faced with information asymmetries and transaction costs,
so they rely initially on internally generated finances, then move toward debt financing, a
relatively expensive form of financing and then move to equity financing as the last
option. Jensen (1986) in his free cash flow theory said that excess cash flows are used
on less return projects or organization inefficiencies that create agency conflicts among
shareholders and managers of the firm and debt is a useful tool to solve the free cash
flow problem.
Similarly, trade off theory holds that the decision of a firm about the use of debt finance
or equity finance is based on the costs and benefits associated with each source of
funds. Like the use of debt can have tax saving benefits but can also have bankruptcy
costs, so the company must balance the costs and benefits with each source in deciding
about the optimal capital structure. Then an improved version of this theory was capital
signaling theory mentioning that all investors are not rational and neither every investor
have all amount of information or equal level of information compared to the owners and
managers also called insiders of the company. When expected future performance of
the company based on the expected future cash flows and earnings will look good,
insiders will opt for debt financing with low level of interest and default risk thus reducing
the flow of large gains to more shareholders. Whereas in opposite case when expected
future performance outlook seems bad, insiders opt for equity financing thus shifting the
flow of losses to shareholders, which in case of debt financing would have led to
bankruptcy. Then the agency theory, it explains the relationship of principal
(shareholders of the firm) with agent (managers or management of the firm) in the
decision making process about the firm capital structure combination. The complexity of
the agency problem between principal and agent play a key role in deciding about the
optimal capital structure in a firm (Jensen and Meckling, 1976). Then market timing
theory which states that firms issue equity finance to generate funds when the market
prices (current) or values of the company stocks are high compared to its book value or
past market values and buys back these stocks when market values are down for the
company (Baker and Wurgler, 2002). These were the main theories that dominated the
literature in relevance to the relationship of capital structure decisions with the firm’s
performance over the several decades. Whereas Graham and Harvey, (2001) finds little
support for these theories in the actual corporate structures. They find that these
theories and their assumptions do not significantly correlate to the determination of
capital structure decisions in the corporations. Similarly, Brav et al., (2005) also find that
the significance of these theories and its assumptions to the actual capital structure
decisions in corporations have decreased overtime compared to the past
Shahzad and Usman (2014) had conducted a comparative study between Cement and
Auto sector of Pakistan and had analyzed the relationship between Capital Structure and
Profitability of 28 companies, combined, from the period of 2005 to 2011. The results
7 | P a g e
concluded that companies must have short term debts and internal debts as compared
to long term debts.
Khan (2012) also tested the relationship of capital performance in Pakistani environment
by testing sample of 36 engineering firms during 2003 to 2009. His results determined
that the association between financial leverage and company performance is negative
but insignificant with respect to Return on Equity.
HYPOTHESIS
Ho = Long Term Debt to Total Asset has no effect on Profitability.
H1 = Long Term Debt to Total Asset has a significant effect on Profitability.
Ho = Short Term Debt to Total Asset has no effect on Profitability.
H1 = Short Term Debt to Total Asset has a significant effect on Profitability.
Ho = Total Debt divided by the Total Assets has no effect on Profitability.
H1 = Total Debt divided by the Total Assets has a significant effect on Profitability.
RESEARCH METHODOLOGY
The study will be carried out on all Karachi Stock Exchange KSE100 listed
pharmaceutical and food companies. The data for the study will be obtained from Annual
reports, financial statement analysis of firm listed at KSE and balance sheet data. Panel
data from the period of 2010 to 2014 will be used for analysis.
Data and Sample:
In total there are 8 pharmaceutical and 13 food companies are listed in Karachi Stock
Exchange and prominent ones are listed below;
Pharmaceutical Sector
GlaxoSmithKline (Pakistan) Limited
Sanofi-Aventis Pakistan Limited
Abbot Laboratories (Pakistan) Limited
Ferozsons Laboratories Limited
The Searle Company Limited
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Wyeth Pakistan Limited
IBL HealthCare Limited
Highnoon Laboratories Limited
Otsuka Pakistan Limited
Food Sector
Engro Foods Limited
Nestle Pakistan Limited
National Foods
Rafhan Maize Products Limited
Quice Food Limited
Unilever Pakistan Limited
Mitchells Fruit Farms Limited
Morafco Industries Limited
Ismail Industries Limited
Clover Pakistan Limited
Sample Size:
In order to keep the size of research manageable, our research is confined to 5 year
financial data of top 5 companies of both pharmaceutical and food sector.
Pharmaceutical Sector
GlaxoSmithKline (Pakistan) Limited
Sanofi-Aventis Pakistan Limited
Abbot Laboratories (Pakistan) Limited
The Searle Company Limited
Otsuka Pakistan Limited
Food Sector
Engro Foods Limited
Nestle Pakistan Limited
Rafhan Maize Products Limited
Quice Food Limited
Mitchells Fruit Farms Limited
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Dependent Variables:
Return on Asset (ROA) = Net Profit/ Total Asset
This ratio shows that how efficient the firm has utilized their assets. The higher the value
of ROA the better it is. ROA was considered as dependent variable by Majumdar and
Chhibber (1999), Abor (2005) and Shahzad and Usman (2014).
ROA is one of the key profitability ratio used in Generally Accepted Accounting
Principles (GAAP).
In-Dependent Variables:
1. Debt to total Assets (Debt Asset Ratio) = Total Debt / Total Assets
This ratio shows that how much assets have been financed with debt. This ratio has also
been used as independent variable by Abdul Ghafoor Khan (2012).
2. Short term debt ratio (STDR) = Short Term Debt/Total Asset
This ratio shows that how much assets have been financed through short term loan.
Chin Ai Fu (1997), Sohail Amjad (2007) and Rametulla Ferati (2010) considered STDR
as independent variable in their respective researches.
3. Long term debt ratio (LTDR) =Long Term Debt/ Total Asset
This ratio shows that how much assets have been financed through long term loan. The
higher the value may show the dependency of the form on debt, which may be a
negative sign for the investors because they might be thinking that the form may go
bankrupt. Rametulla Ferati (2010)
4. Total Debt to Equity Ratio (Debt Equity Ratio)
This ratio shows the comparison of Debt to equity. Higher this value means more debt is
present as compared to equity and company has financial leveraged.
Conceptual Framework:
Return on Asset (ROA)
Short Term Debt to Asset Ratio
(STDTA)
Debt to Asset Ratio (TDTA)
Debt to Equity Ratio
Long Term Debt to Asset Ratio (LTDTA)
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DATA ANALYSIS AND INTERPRETATION
Analysis of Pharmaceutical Sector:
Regression Analysis:
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .782a .612 .554 .052564
a. Predictors: (Constant), Debt to equity, Long term debt ratio, Short
term debt ratio
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression .087 3 .029 10.525 .000a
Residual .055 20 .003
Total .142 23
a. Predictors: (Constant), Debt to equity, Long term debt ratio, Short term debt ratio
b. Dependent Variable: Return on asset
Coefficientsa
Model
Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig.
95.0% Confidence Interval
for B
B Std. Error Beta
Lower
Bound
Upper
Bound
1 (Constant) .148 .026 5.754 .000 .094 .202
Long term debt
ratio
-.111 .271 -.070 -.411 .686 -.677 .454
Short term debt
ratio
.110 .095 .308 1.163 .259 -.088 .308
Debt to equity -.080 .024 -.971 -3.368 .003 -.130 -.031
a. Dependent Variable: Return on asset
Regression Model:
ROA = ß1 (LTDR) + ß2 (STDR) + ß3 (Debt Asset Ratio) + ß4 (Debt Equity Ratio) + C
ROA = -0.111(LTDR) + 0.110(STDR) - 0.80 (Debt Equity Ratio) + 0.148
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Excluded Variablesb
Model Beta In t Sig.
Partial
Correlation
Collinearity
Statistics
Tolerance
1 Debt to asset 53.396a .673 .509 .153 3.164E-6
a. Predictors in the Model: (Constant), Debt to equity, Long term debt ratio, Short term debt ratio
b. Dependent Variable: Return on asset
Correlations
Return on
asset
Long term
debt ratio
Short term
debt ratio
Debt to
asset
Debt to
equity
Pearson
Correlation
Return on asset 1.000 -.437 -.478 -.554 -.751
Long term debt
ratio
-.437 1.000 .070 .284 .399
Short term debt
ratio
-.478 .070 1.000 .976 .805
Debt to asset -.554 .284 .976 1.000 .861
Debt to equity -.751 .399 .805 .861 1.000
Sig. (1-tailed) Return on asset . .016 .009 .002 .000
Long term debt
ratio
.016 . .373 .089 .027
Short term debt
ratio
.009 .373 . .000 .000
Debt to asset .002 .089 .000 . .000
Debt to equity .000 .027 .000 .000 .
N Return on asset 24 24 24 24 24
Long term debt
ratio
24 24 24 24 24
Short term debt
ratio
24 24 24 24 24
Debt to asset 24 24 24 24 24
Debt to equity 24 24 24 24 24
From the above analysis we can to know that about 61% variation in profitability (ROA)
can be explained by , long term, total debt and equity ratios. Debts to equity ratios have
significant impact on the profitability of the organization by the statistical model. Long
and short term debts have a strong correlation with the net profit of the firm in terms of
high ROA.
12 | P a g e
Analysis of Food Sector:
Regression Analysis:
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .606a .367 .240 .398901
a. Predictors: (Constant), Debt to equity, Short term debt ratio, Debt to
asset, Long term debt ratio
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 1.843 4 .461 2.896 .048a
Residual 3.182 20 .159
Total 5.026 24
a. Predictors: (Constant), Debt to equity, Short term debt ratio, Debt to asset, Long term debt ratio
b. Dependent Variable: Return on asset
Coefficientsa
Model
Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig.
95.0% Confidence Interval
for B
B Std. Error Beta
Lower
Bound
Upper
Bound
1 (Constant) .484 .326 1.485 .153 -.196 1.163
Long term debt
ratio
-.350 1.130 -.210 -.310 .760 -2.708 2.008
Short term debt
ratio
.805 .924 .251 .872 .394 -1.122 2.732
Debt to asset -.835 1.159 -.478 -.720 .480 -3.253 1.583
Debt to equity -.018 .041 -.117 -.431 .671 -.104 .068
a. Dependent Variable: Return on asset
Regression Model:
ROA = ß1 (LTDR) + ß2 (STDR) + ß3 (Debt Asset Ratio) + ß4 (Debt Equity Ratio) + C
ROA = -0.350(LTDR) + 0.805(STDR) - 0.835(Debt Asset Ratio) -0.18(Debt Equity Ratio) + 0.484
13 | P a g e
Correlations
Return on
asset
Long term
debt ratio
Short term
debt ratio
Debt to
asset
Debt to
equity
Pearson
Correlation
Return on asset 1.000 -.583 .114 -.544 .375
Long term debt
ratio
-.583 1.000 -.087 .908 -.703
Short term debt
ratio
.114 -.087 1.000 .241 .339
Debt to asset -.544 .908 .241 1.000 -.544
Debt to equity .375 -.703 .339 -.544 1.000
Sig. (1-tailed) Return on asset . .001 .294 .002 .032
Long term debt
ratio
.001 . .339 .000 .000
Short term debt
ratio
.294 .339 . .123 .049
Debt to asset .002 .000 .123 . .002
Debt to equity .032 .000 .049 .002 .
N Return on asset 25 25 25 25 25
Long term debt
ratio
25 25 25 25 25
Short term debt
ratio
25 25 25 25 25
Debt to asset 25 25 25 25 25
Debt to equity 25 25 25 25 25
The above analysis for food sectors shows that about 37% variation in profitability
(ROA) can be explained by , long term, debt asset ratio and equity ratios. The impact of
debt to equity ratios has less significant impact on the profitability of the organization by
the statistical model. More equity based structures of the firms become more profitable
and high returns on assets.
However, the long term and short term debt ratios are strongly correlated with each
other but still short term debt has positive impact on the profitability as compared to
pharma industry.
14 | P a g e
CONCLUSION
This research study was conducted to determine the effect of debt and equity structure
on profitability of 5 companies of pharmaceutical and food sector registered in the KSE
from the period of 2010 to 2014. The analysis was performed based on panel data by
use of regression model. The data analysis was done to test the assumption that there is
a positive or negative association between the variables. So for this purpose Return on
Assets (ROA) was considered as controlled variable and Long Term Debt to Total Asset
(LDTA), Short Term Debt to Total Asset (STDA) and Total Debt to Total Asset (TDTA)
were controlling factors.
The result shows that explanatory variables and financial structures of companies have
strong impact on their profitability and growth.
Profitability in pharmaceutical sector is positive to structures based on more debt and
less equity portions.
However, food sector in Pakistan is not much positive in terms of its profitability to the
capital structures based on debt- equity ratios.
Further study should be done in the Pakistan’s market on the other sectors to determine
the effect of different factors on profitability.
RECOMMANDATIONS
As we already know the financial decision is considered to be most difficult decision and
specially capital structure. Present research paper tried to work out evaluating the
relationship of capital structure with profitability.
Theoretical models do not indicate best possible mixture of capital structure that would
be ideal for the companies in pharmaceutical and food sector, so that they can yield
maximum profits out of it. Still on the basis of results it is recommended that companies
in pharma & food sectors should reconsider their best possible capital structures to
maximize their earnings and net profits.
Most of the companies in both the sectors have lower debt as compared to other
developed countries such as Japan, United Kingdom, United States of America and
Germany. This shows that Pakistani companies are not using the high level of debt.
Reason might be high interest rates being offered by the banks, political instability,
terrorism and uncertain atmosphere. So we may say that companies in Pakistan are not
opting for high level of debts. Because of the same reason some of the results are
contradictory to previous studies that are mentioned in conclusion section.
15 | P a g e
REFERENCE/ BIBLIOGRAPHY:
Shahzad and Usman (2014), “Capital structure and profitability: A comparative study of
cement and auto sector of Pakistan”, Pakistan Business Review.
Abdul Ghafoor Khan (2012), “The relationship of capital structure decisions with firm
performance: A study of the engineering sector of Pakistan”, International Journal of
Accounting and Financial Reporting, 2012, Vol. 2, No. 1.
Abor, J. (2005), “The effect of capital structure on profitability: an empirical analysis of
listed firms in Ghana”, Journal of Risk Finance, Vol. 6, pp. 438-47.
Abor, J. (2007), “Debt policy and performance of SMEs: evidence from Ghanaian and
South Africa firms”, Journal of Risk Finance, Vol. 8, pp. 364-79.
Amidu, M. (2007), “Determinants of capital structure of banks in Ghana: an empirical
approach”, Baltic Journal of Management, Vol. 2 No. 1, pp. 67-79.
Berger, A.N. and Bonaccorsi di Patti, E. (2006), “Capital structure and firm performance:
a new approach to testing pecking order theory and an application to banking industry”,
Journal of Banking & Finance, Vol. 30 No. 4, pp. 1065-102.
Bokpin, G.A. (2009), “Macroeconomic development and capital structure decisions of
firms: Evidence from emerging market economies”, Studies in Economics and Finance,
Vol. 26 No. 2, pp. 129-142.
Bokpin, G.A., Aboagye, A. Q. Q. and Osei, K. A. (2010), “Risk exposure and corporate
financial policy on the Ghana Stock Exchange”, The Journal of Risk Finance, Vol. 11 No.
3, pp. 323-332.
Brav, A., Graham, J., Harvey, C. and Michaely, R. (2005), “Payout policy in the 21st
century”,Journal of Financial Economics, Vol. 77, pp. 483-527.
Chen, J., Chen, M., Liao, W. and Chen, T. (2009), “Influence of capital structure and
operational risk on profitability of life insurance industry in Taiwan”, Journal of Modelling
in Management, Vol. 4 No. 1, pp. 7-18.
Deesomsak, R ., Paudyal, K ., & Pescetto, G . (2004), “The determinantso f capital
structure: evidence from the Asia Pacific region”, Journal of Multinational Financial
Management, Vol. 14 No. 4/5, pp. 3 87-405.
Ebaid, I. E. (2009), “The impact of capital-structure choice on firm performance:
empirical evidence from Egypt”, The Journal of Risk Finance, Vol. 10 No. 5, pp. 477-487.
Frank, M. and Goyal, V. (2003), “Testing the pecking order theory of capital structure”,
Journal of Financial Economics, Vol. 67, pp. 217-48.
16 | P a g e
Gleason, K. C., Mathur, L. K., & Mathur, I. (2000), “The Interrelationship between
Culture, Capital Structure, and Performance: Evidence from European Retailers”,
Journal of Business Research, Vol. 50 No. 2, pp. 185- 191.
Graham, J. and Harvey, C. (2001), “The theory and practice of corporate finance:
evidence from the field”, Journal of Financial Economics, Vol. 60, pp. 187-243.
Hadlock, C. and James, C. (2002), “Do banks provide financial slack?”, Journal of
Finance, Vol. 57, pp. 1383- 420.
Harris, M. and Raviv, A. (1990), “Capital structure and the informational role of debt”,
Journal of Finance, Vol. 45, pp. 321-49.
17 | P a g e
SPSS File of Pharmaceutical Sector GET
FILE='C:\Users\DELL\Desktop\SPSS_version19\spss\AABR Report (Pharma).sav'.
DATASET NAME DataSet3 WINDOW=FRONT.
REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI(95) R ANOVA CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT ROA
/METHOD=ENTER LTDR STDR DEBT_EQUITY DEBT_ASSET
/SCATTERPLOT=(ROA ,*ZPRED)
/RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID).
Regression
Descriptive Statistics
Mean Std. Deviation N
Return on asset .10508 .078712 24
Long term debt ratio .05921 .049718 24
Short term debt ratio .30433 .219596 24
Debt to equity .86938 .949320 24
Debt to asset .36363 .228522 24
18 | P a g e
Correlations
Return on asset
Long term debt
ratio
Short term debt
ratio
Pearson Correlation Return on asset 1.000 -.437 -.478
Long term debt ratio -.437 1.000 .070
Short term debt ratio -.478 .070 1.000
Debt to equity -.751 .399 .805
Debt to asset -.554 .284 .976
Sig. (1-tailed) Return on asset . .016 .009
Long term debt ratio .016 . .373
Short term debt ratio .009 .373 .
Debt to equity .000 .027 .000
Debt to asset .002 .089 .000
N Return on asset 24 24 24
Long term debt ratio 24 24 24
Short term debt ratio 24 24 24
Debt to equity 24 24 24
Debt to asset 24 24 24
Correlations
Debt to equity Debt to asset
Pearson Correlation Return on asset -.751 -.554
Long term debt ratio .399 .284
19 | P a g e
Short term debt ratio .805 .976
Debt to equity 1.000 .861
Debt to asset .861 1.000
Sig. (1-tailed) Return on asset .000 .002
Long term debt ratio .027 .089
Short term debt ratio .000 .000
Debt to equity . .000
Debt to asset .000 .
N Return on asset 24 24
Long term debt ratio 24 24
Short term debt ratio 24 24
Debt to equity 24 24
Debt to asset 24 24
Variables Entered/Removedb
Model
Variables
Entered
Variables
Removed Method
1 Debt to asset,
Long term debt
ratio, Debt to
equity
. Enter
a. Tolerance = .000 limits reached.
b. Dependent Variable: Return on asset
20 | P a g e
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .783a .612 .554 .052556
Model Summaryb
Model
Change Statistics
Durbin-Watson
R Square
Change F Change df1 df2 Sig. F Change
1 .612 10.530 3 20 .000 .785
a. Predictors: (Constant), Debt to asset, Long term debt ratio, Debt to equity
b. Dependent Variable: Return on asset
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression .087 3 .029 10.530 .000a
Residual .055 20 .003
Total .142 23
a. Predictors: (Constant), Debt to asset, Long term debt ratio, Debt to equity
b. Dependent Variable: Return on asset
21 | P a g e
Coefficientsa
Model
Unstandardized Coefficients
B Std. Error
1 (Constant) .148 .026
Long term debt ratio -.222 .242
Debt to equity -.081 .024
Debt to asset .111 .095
Coefficientsa
Model
Standardized
Coefficients
t Sig.
95.0% Confidence Interval for B
Beta Lower Bound Upper Bound
1 (Constant) 5.752 .000 .094 .202
Long term debt ratio -.140 -.915 .371 -.727 .284
Debt to equity -.971 -3.371 .003 -.130 -.031
Debt to asset .321 1.165 .258 -.087 .309
Excluded Variablesb
Model Beta In t Sig.
Partial
Correlation
Collinearity
Statistics
Tolerance
1 Short term debt ratio -51.011a -.669 .512 -.152 3.426E-6
22 | P a g e
Excluded Variablesb
Model Beta In t Sig.
Partial
Correlation
Collinearity
Statistics
Tolerance
1 Short term debt ratio -51.011a -.669 .512 -.152 3.426E-6
a. Predictors in the Model: (Constant), Debt to asset, Long term debt ratio, Debt to equity
b. Dependent Variable: Return on asset
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value -.15133 .14626 .10508 .061594 24
Residual -.070376 .080839 .000000 .049009 24
Std. Predicted Value -4.163 .669 .000 1.000 24
Std. Residual -1.339 1.538 .000 .933 24
a. Dependent Variable: Return on asset
Charts
23 | P a g e
24 | P a g e
CORRELATIONS
/VARIABLES=ROA LTDR STDR DEBT_EQUITY DEBT_ASSET
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES XPROD
/MISSING=PAIRWISE.
Correlations
Descriptive Statistics
Mean Std. Deviation N
Return on asset .10508 .078712 24
Long term debt ratio .05921 .049718 24
Short term debt ratio .30433 .219596 24
Debt to equity .86938 .949320 24
Debt to asset .36363 .228522 24
Correlations
Return on asset
Long term debt
ratio
Short term debt
ratio
Return on asset Pearson Correlation 1 -.437* -.478
*
Sig. (2-tailed) .033 .018
Sum of Squares and Cross-
products
.142 -.039 -.190
Covariance .006 -.002 -.008
N 24 24 24
Long term debt ratio Pearson Correlation -.437* 1 .070
Sig. (2-tailed) .033 .746
25 | P a g e
Sum of Squares and Cross-
products
-.039 .057 .018
Covariance -.002 .002 .001
N 24 24 24
Short term debt ratio Pearson Correlation -.478* .070 1
Sig. (2-tailed) .018 .746
Sum of Squares and Cross-
products
-.190 .018 1.109
Covariance -.008 .001 .048
N 24 24 24
Debt to equity Pearson Correlation -.751** .399 .805
**
Sig. (2-tailed) .000 .053 .000
Sum of Squares and Cross-
products
-1.290 .434 3.860
Covariance -.056 .019 .168
N 24 24 24
Debt to asset Pearson Correlation -.554** .284 .976
**
Sig. (2-tailed) .005 .178 .000
Sum of Squares and Cross-
products
-.229 .074 1.127
Covariance -.010 .003 .049
N 24 24 24
26 | P a g e
Correlations
Debt to equity Debt to asset
Return on asset Pearson Correlation -.751** -.554
**
Sig. (2-tailed) .000 .005
Sum of Squares and Cross-
products
-1.290 -.229
Covariance -.056 -.010
N 24 24
Long term debt ratio Pearson Correlation .399 .284
Sig. (2-tailed) .053 .178
Sum of Squares and Cross-
products
.434 .074
Covariance .019 .003
N 24 24
Short term debt ratio Pearson Correlation .805** .976
**
Sig. (2-tailed) .000 .000
Sum of Squares and Cross-
products
3.860 1.127
Covariance .168 .049
N 24 24
Debt to equity Pearson Correlation 1 .861**
Sig. (2-tailed) .000
27 | P a g e
Sum of Squares and Cross-
products
20.728 4.294
Covariance .901 .187
N 24 24
Debt to asset Pearson Correlation .861** 1
Sig. (2-tailed) .000
Sum of Squares and Cross-
products
4.294 1.201
Covariance .187 .052
N 24 24
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
*Analyze Patterns of Missing Values.
MULTIPLE IMPUTATION LTDR STDR DEBT_EQUITY DEBT_ASSET
/IMPUTE METHOD=NONE
/MISSINGSUMMARIES OVERALL VARIABLES (MAXVARS=25
MINPCTMISSING=10) PATTERNS.
28 | P a g e
Missing Values
SPSS File of Food Sector REGRESSION
/DESCRIPTIVES MEAN STDDEV CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI(95) R ANOVA CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT ROA
/METHOD=ENTER LTDR STDR DEBT_EQUITY DEBT_ASSET
/RESIDUALS DURBIN HISTOGRAM(ZRESID) NORMPROB(ZRESID).
29 | P a g e
Regression
Notes
Output Created 23-Apr-2015 20:47:18
Comments
Input Data C:\Users\DELL\Desktop\SPSS_version
19\spss\AABR Report.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File
24
Missing Value Handling Definition of Missing User-defined missing values are
treated as missing.
Cases Used Statistics are based on cases with no
missing values for any variable used.
30 | P a g e
Syntax REGRESSION
/DESCRIPTIVES MEAN STDDEV
CORR SIG N
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI(95) R
ANOVA CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT ROA
/METHOD=ENTER LTDR STDR
DEBT_EQUITY DEBT_ASSET
/RESIDUALS DURBIN
HISTOGRAM(ZRESID)
NORMPROB(ZRESID).
Resources Processor Time 00 00:00:00.671
Elapsed Time 00 00:00:00.777
Memory Required 2444 bytes
Additional Memory Required
for Residual Plots
632 bytes
[DataSet1] C:\Users\DELL\Desktop\SPSS_version19\spss\AABR Report.sav
31 | P a g e
Descriptive Statistics
Mean Std. Deviation N
Return on asset .11033 .065767 24
Long term debt ratio .05921 .049718 24
Short term debt ratio .30 .220 24
Debt to equity .87 .949 24
Debt to asset .36363 .228522 24
Correlations
Return on asset
Long term debt
ratio
Short term debt
ratio
Pearson Correlation Return on asset 1.000 -.392 -.430
Long term debt ratio -.392 1.000 .070
Short term debt ratio -.430 .070 1.000
Debt to equity -.583 .399 .805
Debt to asset -.498 .284 .976
Sig. (1-tailed) Return on asset . .029 .018
Long term debt ratio .029 . .373
Short term debt ratio .018 .373 .
Debt to equity .001 .027 .000
Debt to asset .007 .089 .000
N Return on asset 24 24 24
32 | P a g e
Long term debt ratio 24 24 24
Short term debt ratio 24 24 24
Debt to equity 24 24 24
Debt to asset 24 24 24
Correlations
Debt to equity Debt to asset
Pearson Correlation Return on asset -.583 -.498
Long term debt ratio .399 .284
Short term debt ratio .805 .976
Debt to equity 1.000 .861
Debt to asset .861 1.000
Sig. (1-tailed) Return on asset .001 .007
Long term debt ratio .027 .089
Short term debt ratio .000 .000
Debt to equity . .000
Debt to asset .000 .
N Return on asset 24 24
Long term debt ratio 24 24
Short term debt ratio 24 24
Debt to equity 24 24
Debt to asset 24 24
33 | P a g e
Variables Entered/Removedb
Model
Variables
Entered
Variables
Removed Method
1 Debt to asset,
Long term debt
ratio, Debt to
equity
. Enter
a. Tolerance = .000 limits reached.
b. Dependent Variable: Return on asset
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .609a .371 .276 .055947
Model Summaryb
Model
Change Statistics
Durbin-Watson
R Square
Change F Change df1 df2 Sig. F Change
1 .371 3.928 3 20 .024 .731
34 | P a g e
a. Predictors: (Constant), Debt to asset, Long term debt ratio, Debt to equity
b. Dependent Variable: Return on asset
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression .037 3 .012 3.928 .024a
Residual .063 20 .003
Total .099 23
a. Predictors: (Constant), Debt to asset, Long term debt ratio, Debt to equity
b. Dependent Variable: Return on asset
Coefficientsa
Model
Unstandardized Coefficients
B Std. Error
1 (Constant) .157 .027
Long term debt ratio -.253 .258
Debt to equity -.033 .025
Debt to asset -.009 .101
35 | P a g e
Coefficientsa
Model
Standardized
Coefficients
t Sig.
95.0% Confidence Interval for B
Beta Lower Bound Upper Bound
1 (Constant) 5.748 .000 .100 .215
Long term debt ratio -.192 -.982 .338 -.792 .285
Debt to equity -.481 -1.311 .205 -.086 .020
Debt to asset -.030 -.085 .933 -.219 .202
a. Dependent Variable: Return on asset
Excluded Variablesb
Model Beta In t Sig.
Partial
Correlation
Collinearity
Statistics
Tolerance
1 Short term debt ratio -57.341a -.588 .563 -.134 3.426E-6
a. Predictors in the Model: (Constant), Debt to asset, Long term debt ratio, Debt to equity
b. Dependent Variable: Return on asset
36 | P a g e
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value -.03306 .14437 .11033 .040044 24
Residual -.076760 .087075 .000000 .052170 24
Std. Predicted Value -3.581 .850 .000 1.000 24
Std. Residual -1.372 1.556 .000 .933 24
a. Dependent Variable: Return on asset
Charts
37 | P a g e
CORRELATIONS
/VARIABLES=ROA LTDR STDR DEBT_EQUITY DEBT_ASSET
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES
/MISSING=PAIRWISE.
Correlations
38 | P a g e
Notes
Output Created 23-Apr-2015 20:51:59
Comments
Input Data C:\Users\DELL\Desktop\SPSS_version
19\spss\AABR Report.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File
24
Missing Value Handling Definition of Missing User-defined missing values are
treated as missing.
Cases Used Statistics for each pair of variables are
based on all the cases with valid data
for that pair.
Syntax CORRELATIONS
/VARIABLES=ROA LTDR STDR
DEBT_EQUITY DEBT_ASSET
/PRINT=TWOTAIL NOSIG
/STATISTICS DESCRIPTIVES
/MISSING=PAIRWISE.
39 | P a g e
Resources Processor Time 00 00:00:00.063
Elapsed Time 00 00:00:00.059
[DataSet1] C:\Users\DELL\Desktop\SPSS_version19\spss\AABR Report.sav
Descriptive Statistics
Mean Std. Deviation N
Return on asset .11033 .065767 24
Long term debt ratio .05921 .049718 24
Short term debt ratio .30 .220 24
Debt to equity .87 .949 24
Debt to asset .36363 .228522 24
Correlations
Return on asset
Long term debt
ratio
Short term debt
ratio
Return on asset Pearson Correlation 1 -.392 -.430*
Sig. (2-tailed) .058 .036
N 24 24 24
40 | P a g e
Long term debt ratio Pearson Correlation -.392 1 .070
Sig. (2-tailed) .058 .746
N 24 24 24
Short term debt ratio Pearson Correlation -.430* .070 1
Sig. (2-tailed) .036 .746
N 24 24 24
Debt to equity Pearson Correlation -.583** .399 .805
**
Sig. (2-tailed) .003 .053 .000
N 24 24 24
Debt to asset Pearson Correlation -.498* .284 .976
**
Sig. (2-tailed) .013 .178 .000
N 24 24 24
Correlations
Debt to equity Debt to asset
Return on asset Pearson Correlation -.583** -.498
*
Sig. (2-tailed) .003 .013
N 24 24
Long term debt ratio Pearson Correlation .399 .284
Sig. (2-tailed) .053 .178
N 24 24
Short term debt ratio Pearson Correlation .805** .976
**
Sig. (2-tailed) .000 .000
41 | P a g e
N 24 24
Debt to equity Pearson Correlation 1 .861**
Sig. (2-tailed) .000
N 24 24
Debt to asset Pearson Correlation .861** 1
Sig. (2-tailed) .000
N 24 24
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
PPLOT
/VARIABLES=LTDR STDR DEBT_EQUITY DEBT_ASSET
/NOLOG
/NOSTANDARDIZE
/TYPE=Q-Q
/FRACTION=BLOM
/TIES=MEAN
/DIST=NORMAL.
42 | P a g e
PPlot
Notes
Output Created 23-Apr-2015 20:54:01
Comments
Input Data C:\Users\DELL\Desktop\SPSS_version
19\spss\AABR Report.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data
File
24
Date <none>
Missing Value Handling Definition of Missing User-defined missing values are
treated as missing.
Cases Used For a given sequence or time series
variable, cases with missing values are
not used in the analysis. Cases with
negative or zero values are also not
used, if the log transform is requested.
43 | P a g e
Syntax PPLOT
/VARIABLES=LTDR STDR
DEBT_EQUITY DEBT_ASSET
/NOLOG
/NOSTANDARDIZE
/TYPE=Q-Q
/FRACTION=BLOM
/TIES=MEAN
/DIST=NORMAL.
Resources Processor Time 00 00:00:02.589
Elapsed Time 00 00:00:02.746
Use From First observation
To Last observation
Time Series Settings (TSET) Amount of Output PRINT = DEFAULT
Saving New Variables NEWVAR = CURRENT
Maximum Number of Lags in
Autocorrelation or Partial
Autocorrelation Plots
MXAUTO = 16
Maximum Number of Lags
Per Cross-Correlation Plots
MXCROSS = 7
Maximum Number of New
Variables Generated Per
Procedure
MXNEWVAR = 60
Maximum Number of New
Cases Per Procedure
MXPREDICT = 1000
44 | P a g e
Treatment of User-Missing
Values
MISSING = EXCLUDE
Confidence Interval
Percentage Value
CIN = 95
Tolerance for Entering
Variables in Regression
Equations
TOLER = .0001
Maximum Iterative
Parameter Change
CNVERGE = .001
Method of Calculating Std.
Errors for Autocorrelations
ACFSE = IND
Length of Seasonal Period Unspecified
Variable Whose Values
Label Observations in Plots
Unspecified
Equations Include CONSTANT
[DataSet1] C:\Users\DELL\Desktop\SPSS_version19\spss\AABR Report.sav
Model Description
Model Name MOD_2
Series or Sequence 1 Long term debt ratio
2 Short term debt ratio
3 Debt to equity
45 | P a g e
4 Debt to asset
Transformation None
Non-Seasonal Differencing 0
Seasonal Differencing 0
Length of Seasonal Period No periodicity
Standardization Not applied
Distribution Type Normal
Location estimated
Scale estimated
Fractional Rank Estimation Method Blom's
Rank Assigned to Ties Mean rank of tied values
Applying the model specifications from MOD_2
Case Processing Summary
Long term debt
ratio
Short term debt
ratio Debt to equity
Series or Sequence Length 24 24 24
Number of Missing Values in
the Plot
User-Missing 0 0 0
System-Missing 0 0 0
Case Processing Summary
Debt to asset
Series or Sequence Length 24
46 | P a g e
Number of Missing Values in
the Plot
User-Missing 0
System-Missing 0
The cases are unweighted.
Estimated Distribution Parameters
Long term debt
ratio
Short term debt
ratio Debt to equity Debt to asset
Normal Distribution Location .05921 .30433 .86938 .36363
Scale .049718 .219596 .949320 .228522
The cases are unweighted.
47 | P a g e
Long term debt ratio
48 | P a g e
Short term debt ratio
49 | P a g e
Debt to equity
50 | P a g e
Debt to asset