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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
Equal variances are notassumed 1.769 9.021 .111
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 assumed 20.365 .000 1.144 457 .253
Equal variances are notassumed .739 134.338 .461
ROA
Equal variances assumed 21.533 .000 5.731 457 .000
Equal variances are notassumed 4.001 144.175 .000
ROCE
Equal variances assumed 25.912 .000 2.897 457 .004
Equal variances are notassumed 1.875 134.452 .063
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.
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