to find the relationship between capital structure and profitability. a comparative study between of...

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

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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

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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.

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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

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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

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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


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.

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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


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


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


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

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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.

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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.

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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.

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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.

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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

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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

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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



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.


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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


ANOVAb


1 Regression .087 3 .029 10.530 .000a

Residual .055 20 .003

Total .142 23



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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


Partial

Correlation

Collinearity

Statistics

Tolerance

1 Short term debt ratio -51.011a -.669 .512 -.152 3.426E-6

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Excluded Variablesb


Partial

Correlation

Collinearity

Statistics

Tolerance


a. Predictors in the Model: (Constant), Debt to asset, Long term debt ratio, Debt to equity


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


Charts

23 | P a g e

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CORRELATIONS

/VARIABLES=ROA LTDR STDR DEBT_EQUITY DEBT_ASSET

/PRINT=TWOTAIL NOSIG

/STATISTICS DESCRIPTIVES XPROD

/MISSING=PAIRWISE.

Correlations



Return on asset .10508 .078712 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

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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


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


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


products

-.229 .074 1.127

Covariance -.010 .003 .049

N 24 24 24

26 | P a g e

Correlations


Return on asset Pearson Correlation -.751** -.554

**

Sig. (2-tailed) .000 .005


products

-1.290 -.229

Covariance -.056 -.010

N 24 24

Long term debt ratio Pearson Correlation .399 .284

Sig. (2-tailed) .053 .178


products

.434 .074

Covariance .019 .003

N 24 24

Short term debt ratio Pearson Correlation .805** .976

**

Sig. (2-tailed) .000 .000


products

3.860 1.127


N 24 24

Debt to equity Pearson Correlation 1 .861**

Sig. (2-tailed) .000

27 | P a g e


products

20.728 4.294


N 24 24

Debt to asset Pearson Correlation .861** 1



products

4.294 1.201


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


/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


/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



Return on asset .11033 .065767 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


Pearson Correlation Return on asset -.583 -.498



Debt to equity 1.000 .861

Debt to asset .861 1.000

Sig. (1-tailed) Return on asset .001 .007



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.


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



ANOVAb


1 Regression .037 3 .012 3.928 .024a

Residual .063 20 .003

Total .099 23



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


Excluded Variablesb


Partial

Correlation

Collinearity

Statistics

Tolerance


a. Predictors in the Model: (Constant), Debt to asset, Long term debt ratio, Debt to equity


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


Charts

37 | P a g e

CORRELATIONS

/VARIABLES=ROA LTDR STDR DEBT_EQUITY DEBT_ASSET


/STATISTICS DESCRIPTIVES

/MISSING=PAIRWISE.

Correlations

38 | P a g e

Notes


Comments




Filter <none>

Weight <none>

Split File <none>


File

24


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



/STATISTICS DESCRIPTIVES

/MISSING=PAIRWISE.

39 | P a g e


Elapsed Time 00 00:00:00.059




Return on asset .11033 .065767 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


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**


N 24 24

Debt to asset Pearson Correlation .861** 1


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


Comments




Filter <none>

Weight <none>

Split File <none>


File

24

Date <none>


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


/NOLOG

/NOSTANDARDIZE

/TYPE=Q-Q

/FRACTION=BLOM

/TIES=MEAN

/DIST=NORMAL.


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


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

to find the relationship between capital structure and profitability. a comparative study between of...

Business

food sector of pakistan

pharmaceutical companies

food companies

asset ratio

research objective

spss file of food sector

asad erp

research methodology