analysis final submission

8/7/2019 Analysis final submission

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A FINANCIAL MANAGEMENT PROJECT

ANALYSIS ON INDUSTRIAL & INFRASTRUCTURE

&TRANSPORT EQUIPMENT INDUSTRY

Submitted to

Mr. SUDHAKAR REDDYSubmitted ByGROUP-13

SL NO. NAME ENROLLMENT

NO.

1. SEKHAR J. SAKIA 10BSPHH010706

2. SURBHIT RAI 10BSPHH010926

3. ASHWIN

BALASUBRAMANIAN

10BSPHH010171

4. MANSI MATELA 10BSPHH010389

5. BACHAN DAS 10BSPHH010895


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Project Objectives:

I. Calculate DOL and DFL for all firms in given industries.II. Use Regression to predict to what extent DOL, DFL are affecting market risk of firm or the

systematic risk (Beta)III. Z-Score Analysis to predict whether firms in bankruptcy region in these industries. If yes

whats the proportion, reasons and how they can avoid this.

When we extracted Sales, EPS and PBIT data for 2009 and 2010 from Prowess, we found that a lot ofcompanies have no data so we removed such companies and hence we got Filtered No. of companiesin the above table.

DOL & DFL, Beta Regression Analysis

Sector 1: Industrial & Infrastructure

We have plotted DOL, DFL and Beta together so that we can have a rough estimate first. Left Y axishas the value of the DOL and DFL curves. Right Y axis has the value of Beta. X axis has thecompanies like company 1, 2, 3 and so on.Just brushing through the graph we can say that DFL and Beta of firms are moving opposite like forcompany 55 and 64. Its difficult to say anything about DOL. But more accurate result we can getthrough the regression analysis later.

On an average, it is found that theOperating Leverage is greater than theFinancial Leverage for Industrial &

Infrastructure. This implies that theoperational risk is greater than the financialrisk. (We have excluded the exceptional cases which are shown in the graph).Exceptions areI T D Cementation India Ltd. DOL 82.33723089Punj Lloyd Ltd. DFL -115.1032439

Sector No. of Companies Filtered No. of Companies

Industrial & Infrastructure 605 69

Transport Equipment 554 116

Sum (Excluding

Exceptions) Average

DOL 194.5422742 2.860915798

DFL 79.12703094 1.163632808

Beta 1.229411765


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As we can see the above data are very far from the industry average for DOL and DFL, so weexcluded them as we cannot trust on the credibility of the data for these two companies. This ispurely to improve the research so that later we can analyse on better results.

Regression Analysis:

Regression Analysis was done to compute the linear model using independent variables DOL andDFL and dependent variable Beta.

Model Summary

ModelR

Square F Sig.

1 0.016676 0.542694 0.583831

Regression Results

Variables Beta t p-valStandardized

Beta

(Constant

) 1.215027 13.47895 2.32E-20DOL 0.001189 0.17165 0.864254 0.021326616

DFL -0.00585 -1.03692 0.303673 -0.128832576

From the above tables, R-square value of model shows that only 1.6676% of variation in Beta is explained by

variations in DOL and DFL. It means rest variations in Beta are explained by some otherfactors which have not been considered in the model.

F value of 0.542694 indicates that the model is not significant. F should be >= 3 Regression model is: Beta = 1.215027 + 0.001189 DOL 0.00585 DFL

It means if we change DOL by 1 unit change in Beta will be 0.001189 units which are very

less. Moreover if we see t & P-Values of both DFL & DOL, we find that they are notsignificant (t >= 2 or


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Foreign Investment : In case of economic recession, foreign investment will reduce and will bearrisk to this sector. So highly dependence on foreign investment makes the industry risky.Events : Events like World Cup, Olympics can increase the growth of this industry significantly.

We found that DFL has more significance from standardized Beta, that can be because infrastructureindustry requires a lot of funds and it has to pay more emphasis on financial leverage. Thus firmsrisk (Beta) will more depend on DFL than DOL.

Sector 2: Transport Equipment

We can see huge spikes in the graph.Atul Auto Ltd. DOL - 161.1205099 The percentage change in EBIT is large compare to otherfirms and % change in sales is less so its DOL is so big. Reason can be the launch of a new rearengine three wheeler AUTO GEM. Similarly other hikes can be explained. Lets take one particularcase.Hindustan Composites Ltd. DOL 828.0816871 It has shown a huge jump in EBIT from 4.28 to574.83; i.e. 133.3061 % change in EBIT. When compared the data with annual report, it seems itsnot right. So we will remove this in furtheranalysis.

On an average, it is found that theOperating Leverage is much greater thanthe Financial Leverage. A very high

Operating Leverage implies that a smalldip in the sales would lead to a hugedecline in their profits. Negative Financial Leverage exists when the assets cannot generate greaterreturn than fixed the rate of return. Common stock holders suffer in this case.

Regression Analysis:

Model Summary

ModelR

Square F Sig.

1 0.006347 0.357702 0.700078

Regression Results

Variables Beta t p-valStandardized

Beta

(Constant) 0.87511 17.63686 4.16E-34

DOL 0.001526 0.803223 0.423547 0.075792371

Sum (Excluding

Exceptions) Average

DOL 353.5243765 3.047624

DFL -77.54101494 -0.66846

Beta 0.882328


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Z > 2.99 represents Safe Zone 1.8


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DETERMINANTS OF CAPITAL STRUCTURE

Analysis of firms in the same sector has been done on the basis of eight parameters provided to us by

way of a research article. The eight key parameters and their formulae are as follows:

1. Asset Structure

2. Profitability

3. Growth Opportunities

4. Size

5. Uniqueness

6. Business Risk

7. Non Debt Tax Shields (NDTS)

8. Liquidity

Analysis of the Industrial & Infrastructure industry

Pearsons Correlation

Leverage Profitabilit

y

Asset

Structure

Size Growt

h

NDTS Dividend Uniqueness Current

Ratio

Pearson

Correlation

Leverage 1.000 -.362 .222 -.031 .006 .093 -.061 -.029 -.123

Profitability -.362 1.000 -.130 .112 .011 -.063 .138 -.076 -.186

Asset

Structure

.222 -.130 1.000 -.003 .055 .461 -.096 .078 .045

Size -.031 .112 -.003 1.00

0

.562 -.116 .433 -.043 -.200

Growth .006 .011 .055 .562 1.000 -.090 .547 .240 -.074

NDTS .093 -.063 .461 -.116 -.090 1.000 -.030 -.071 -.060

Dividend -.061 .138 -.096 .433 .547 -.030 1.000 .009 -.086

Uniqueness -.029 -.076 .078 -.043 .240 -.071 .009 1.000 -.049

Current

Ratio

-.123 -.186 .045 -.200 -.074 -.060 -.086 -.049 1.000

Sig. (1-

tailed)

Leverage . .000 .000 .291 .456 .048 .141 .303 .014

Profitability .000 . .010 .023 .420 .131 .007 .089 .000

Asset

Structure

.000 .010 . .481 .166 .000 .044 .083 .212

Size .291 .023 .481 . .000 .020 .000 .223 .000


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Growth .456 .420 .166 .000 . .054 .000 .000 .095

NDTS .048 .131 .000 .020 .054 . .299 .103 .141

Dividend .141 .007 .044 .000 .000 .299 . .435 .062

Uniqueness .303 .089 .083 .223 .000 .103 .435 . .194

Current

Ratio

.014 .000 .212 .000 .095 .141 .062 .194 .

Pair Wise Correlation

The Pearson's correlation, also called the Pair Wise Correlation is used to find a correlationbetween two continuous variables. The value for a Pearson's can fall between 0.00 (no correlation)

and 1.00 (perfect correlation). In this table we have taken the dependent and all the independentfactor in to consideration. This table shows that Growth is moderately correlated with Size andDividend with a significant level of 0.5 in comparison to other factors. The low correlation valuesindicate that our Model does not have problem ofMulticollinearity

Model Summary (b)

Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson

1 .460(a) .212 .192 .23171 .537

a . Predictors: (Constant): Profitability, Asset Structure, Size, Growth, NDTS, Dividend, Uniquenessand Current Ratio

b . Dependent Variable: Leverage

This table displays R, R squared, adjusted R squared, and the standard error. R is the correlation

between the observed and predicted values of the dependent variable. The absolute value of R

indicates the strength, with larger absolute values indicating stronger relationships. The R value in

our model is .460 which is less than half and not considered appropriate in this kind of model.

ANOVA

ANOVA(b)

Model Sum of Squares df Mean Square F

1Regression 4.461386788 8 0.557673349 10.38658

Residual 16.59074405 309 0.053691728


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F value indicates how significant model is as a whole. If F is significant then it means at least one of the

independent variable is significant and explains the variations in dependent variable. Here we see F is

significant, it means at least one variable is significant which we will see in regression table.

Coefficients Table (Regression Results)

Unstandardiz

Coefficients

Standardiz

Coefficient t Sig.

95% Confiden

Interval for B

Collin

Statist

B Std. Error Beta Lower Bound Upper BoundTolera

(Constant) 0.4677118180.065706024 7.1182497.68E-12 0.3384239880.596999648

Profitability -0.604088040.083604862-0.379285812-7.225513.92E-12 -0.768594892-0.4395811880.925

Asset_Structure 0.4212539020.1164177370.2131488633.6184680.000346 0.192182110.6503256940.735

Size -0.020848410.021386063-0.063680392-0.974860.330392 -0.0629291440.0212323240.597

Growth 2.08015E-063.89713E-060.0383931930.5337640.593889 -5.58812E-069.74842E-060.492

NDTS -0.9122809311.004833788-0.053334173-0.907890.364643 -2.889463121.0649012580.739

Dividend -0.0002123350.011207705-0.001197324-0.018950.984897 -0.02226541 0.021840740.638- - - -

T value indicates the significance of individual independent variables. For significance level 5%, if t

value is >= 2 or


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Standardized coefficients indicate that which of the independent variable is most significant as all

variables has been standardized into same level. From the table we can see, Profitability is the most

significant variable in our model with value -0.3792.

ANALYSIS OF THE TRANSPORT EQUIPMENTINDUSTRY

Pearsons Correlation

LeverageProfitabilityAsset_StructureSize GrowthNDTSDividendUniquene

Pearson

CorrelationLeverage 1.00 -0.15 0.42-0.09 -0.10 0.23 -0.16 -0.0

Profitability -0.15 1.00 0.00 0.07 0.03 0.03 0.08 0.0Asset_Structure 0.42 0.00 1.00 0.00 -0.09 0.47 -0.11 -0.1

Size -0.09 0.07 0.00 1.00 0.60 0.01 0.24 0.0

Growth -0.10 0.03 -0.09 0.60 1.00 -0.09 0.17 0.05

NDTS 0.23 0.03 0.47 0.01 -0.09 1.00 -0.03 -0.1

Dividend -0.16 0.08 -0.11 0.24 0.17 -0.03 1.00 0.02

Uniqueness -0.07 0.01 -0.16 0.01 0.05 - 0.11 0.02 1.0

Current_Ratio -0.08 0.03 -0.13 0.01 -0.04-0.19 -0.05 -0.0

Sig. (1-tailed)Leverage . 0.00 0.00 0.03 0.02 0.00 0.00 0.0

Profitability 0.00. 0.47 0.05 0.24 0.28 0.03 0.42

Asset_Structure 0.00 0.47. 0.47 0.02 0.00 0.01 0.0

Size 0.03 0.05 0.47. 0.00 0.42 0.00 0.37

Growth 0.02 0.24 0.02 0.00. 0.03 0.00 0.13

NDTS 0.00 0.28 0.00 0.42 0.03. 0.22 0.0

Pair Wise Correlation


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The Pearson's correlation, also called the Pair Wise Correlation is used to find a correlationbetween two continuous variables. The value for a Pearson's can fall between 0.00 (no correlation)and 1.00 (perfect correlation). In this table we have taken the dependent and all the independentfactor in to consideration. This table shows that Asset Structure is moderately correlated with NDTSwith a significant level of 0.4 in comparison to other factors. The low correlation values indicate thatour Model does not have problem ofMulticollinearity

Model Summary (b)

Model R R Square Adjusted R Square

Std. Error of

the Estimate Durbi

a . Predictors: (Constant) : Profitability, Asset Structure, Size, Growth, NDTS, Dividend, Uniquenessand Current Ratio

b . Dependent Variable:Leverage

This table displays R, R squared, adjusted R squared, and the standard error. R is the correlation

between the observed and predicted values of the dependent variable. The absolute value of R

indicates the strength, with larger absolute values indicating stronger relationships. The R value in

our model is .462 which is less than half and not considered appropriate in this kind of model.

R squared is the proportion of variation in the dependent variable explained by the regression model.

The values of R squared range from 0 to 1. The data derived from the Regression Analysis has shown

that the Small values of .214 indicate that only 21.4% variation in the dependant variable is explainedby the variation in the independent variables, which is not a significant. Adjusted R squared attempts

to correct R squared to more closely reflect the goodness of fit of the model in the population. In this

table a low R square value shows that this model is not good enough to explain the relationship.

ANOVA

Model Sum of Squares df Mean Square F Sig.

1 Regression 3.576333391 8 0.447041674 16.63300217 7.

Residual 13.16962614 490 0.026876788

F value indicates how significant model is as a whole. If F is significant then it means at least one of the

independent variable is significant and explains the variations in dependent variable. Here we see F is

significant, it means at least one variable is significant which we will see in regression table.


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Coefficients Table (Regression Results)

Model

Unstandardi

Coefficient

Standardiz

Coefficie t Sig.

95% Confide

Interval for

Coll

St

B Std. Error Beta Lower BoundUpper BoundTol

1.0000 (Constant) 0.1679 0.0386 4.35420.0000 0.0922 0.2437Profitability -0.0414 0.0120 -0.1394 -3.45720.0006 -0.0649 -0.0179 0

Asset_Structure 0.4396 0.0530 0.3821 8.29060.0000 0.3354 0.5437 0

Size -0.0127 0.0138 -0.0476 -0.92410.3559 -0.0398 0.0143 0

Growth 0.0000 0.0000 -0.0092 -0.17990.8573 0.0000 0.0000 0

NDTS 0.3897 0.4058 0.0442 0.96040.3373 -0.4076 1.1871 0

Dividend -0.0059 0.0025 -0.0979 -2.34620.0194 -0.0108 -0.0010 0

-

T value indicates the significance of individual independent variables. For significance level 5%, if t value is>= 2 or


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DIVIDEND ANALYSIS

OBJECTIVE

To check the difference in the profitability of dividend paying and non-dividend paying firms in thecontext of Industrial Infrastructure and Transport Equipment industries.

1. The data is for the last 4 years.2. An independent sample t test is conducted on the three profitability measures i.e. ROE, ROI

and ROCE of dividend paying and non-dividend paying firms.

Null Hypothesis: H0: 1= 2Alternate hypothesis: H1: 1 2

Where, 1= mean of ROE/ROI/ROCE if dividend paying firm 2= mean of ROE/ROI/ROCE of non-dividend paying firm

To measure profitability the three ratios have been taken as:

ROE=PAT/Shareholders capital + Reserves

ROCE=EBIT/Total Assets Current Liability

ROI=EBIT/Total Assets

In order to test the difference in the profitability of dividend paying and non-dividend paying firm wehave conducted independent sample t test using a dummy variable. Grouping variable 1 is used fornon-dividend paying firm and 2 is used for dividend paying firm.


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OBSERVATION TABLES

Industrial Infrastructure

Levene's test for equality ofvariances t test for equality of means

F Sig t df Sig. (2tailed)

ROE

Equal variances assumed134.52

4 .000 6.415 306 .000

Equal variances are notassumed 1.174 9.002 .271

ROA

Equal variances assumed 49.423 .000 6.922 306 .000


ROCE

Equal variances assumed156.67

5 .000 6.205 306 .000

Equal variances are notassumed 1.090 9 .304

Transport Equipment Industry

Levene's test for equality ofvariances t test for equality of means

F Sig t df Sig. (2


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tailed)

ROE


Equal variances are notassumed .739 134.338 .461

ROA



ROCE



ANALYSIS OF THE INFRASTRUTURE INDUSTRY

The columns labeled Levenes Test for Equality of Variances shows whether an assumptionof the t-test has been met. The significance (p value) of Levenes test for ROE (.000), ROA(.000) and ROCE (.000) is less than 0.05 so we will assume that the variances are not equaland we will use the second row of the output.

Hence, we will look at the 2nd row of the table for each of the profitability ratios and there thesignificance level of ROE (.271), ROA (.111) and ROCE (.304 ) are more than .05, whichindicates that the null hypothesis is not rejected.

Through this statistical analysis we are in a situation to interpret that on the basis of this datain Industrial Infrastructure, there is no difference between profitability for a dividend and non-dividend paying firms.

ANALYSIS OF THE TRANSPORT EUIPMENT INDUSTRY The columns labeled Levenes Test for Equality of Variances shows whether an assumption

of the t-test has been met. The significance (p value) of Levenes test for ROE (.000), ROA(.000) and ROCE (.000) is less than 0.05 so we will assume that the variances are not equaland we will use the second row of the output.

Hence, we will look at the 2nd row of the table for each of the profitability ratios and there thesignificance level of ROE (.461), is more than .05, which indicates that the null hypothesis isnot rejected, but for ROA (.004) the significance level is less than .05, therefore the nullhypothesis is not accepted and for ROCE (.063) the significance level is close to .05, hence itis exposed to subjectivity of not accepting or not rejecting.

Through this statistical analysis we are in a situation to interpret that on the basis of this data

in Transport Equipment Industry that dividend or non-dividend paying has an impact on theprofitability, is dependent on the ratio which we take.

The differing ratios are due to the capital structure and we see profitability in terms of theequity capital, therefore we can say that there is no significant difference in the profitability ofdividend paying and non-dividend paying companies. But if we look at the p[profitability interms of capital employed and assets, there is significant difference in the profitability ofdividend paying and non-dividend paying companies.


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INTERPRETATION

We can infer that in the Infrastructure industry, if dividends are paid or not, there is no much effecton profitability of the firms. Hence this supports the MM model of dividend policy, i.e. the value ofthe firms are not affected by the dividend policy. It is affected by the investment and returns it gets.

In the transport equipment industry, when ROE is taken as a measure, the MM dividend policy comesinto play. Whereas, when it comes to the other two measures of profitability, the value of the firm isaffected when dividends are paid or not paid.

analysis final submission

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