ANALYSIS OF THE RETURN ON EQUITY (ROE) FOR
TELECOMMUNICATION INDUSTRY USING DUPONT ANALYSIS
David Winster Praveenraj* 1, S.Gokul Kumar 2 and V. S. Karthic3
1 Faculty – School of Management Studies,Bannari Amman Institute of Technology,
India 2 Faculty – School of Management Studies,Bannari Amman Institute of Technology,
India 3 School of Management Studies, Bannari Amman Institute of Technology
India 1 [email protected], 2 [email protected], [email protected]
Abstract
The net income of the organization is not enough to determine its efficiency and performance
unless profit margin, asset turnover, financial leverage, etc. are taken into consideration.
examines That is why this study has used the quantitative finance tool: Dupont Analysis to
ascertain whether Indian Tele-communication companies are able to generate positive Return on
Equity for its shareholders by analyzing various trends of the factors influencing Return on
Equity. The research employs a Descriptive research design for this report. The study
implements the five factor DuPont Model to disaggregate Return on Equity into five factors viz.
Financial Leverage, Asset Turnover, Operating Profit Margin, Tax Burden and Interest Burden.
Using these results, the trend in the industry over the past twelve years is analyzed and the effect
of recession is studied using time series analysis. Cross sectional analysis is performed to
understand significant focus points of different companies and is benchmarked with the industry
average. Also, regression analysis is performed to comprehend the relative impact of the five
ratios on Return on Equity of the selected companies. The key finding of the research study
indicates that there is an overall slump in Return on Equity of the industry attributed to various
internal and external factors of the business. Based on the findings, research study suggests
healthy ways to improve Return on Equity, through optimal usage of Financial Leverage and by
increasing Asset Turnover and Operating Profit Margin efficiencies.
Keywords: Return on Equity, Asset Turnover, Operating Margin, Tax Margin, Tax Burden,
Time-Series analysis, Regression analysis, DUPONT analysis
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1. Introduction Concurring with the decisions of an Organization’s financial management depends on
the question of whether the decision improves the value of the stakeholder’s equity or not.
Basically, every financial, investment and operational decisions impacting productivity, product
costs, tax limits etc. directly affect the income generated for the investors. Also, any decision
which impacts the type of capital employed (viz. debt or equity or both) affects the capital
structure of the firm.
The financial statements, namely, Balance sheet and Income Statement of an
organization contains significant and extremely crucial information about financial health of the
organization. In order to ascertain the financial health of the organization, the stake holders
employ a variety of techniques and financial ratios viz. profitability ratios, turnover ratios and
performance ratios. One such ratio is the Return On Equity (ROE), which indicates the net
income per equity of shareholder’s funds invested in the organization.\
The DuPont model has become one of the most crucial tools of financial analysis for
assessment of the returns and is widely applied across industries to thoroughly analyze the
important factors influencing Return On Equity (ROE). Thus, in this research study, the
researcher employs the extended DuPont model also known as five factor DuPont model to
disaggregate ROE into a product of various components viz. Financial Leverage (FL), Asset
Turnover (ATO), Operating Profit Margin (OPM), Tax Burden (TB) and Interest Burden (IB).
This division of ROE into various components attaches more meaning to ROE and gives various
insights about different drivers influencing ROE of an organization.
Two major telecom companies are taken into consideration for analysis. A brief about
them are presented. Bharti Airtel is India based Tele-com firm operating globally in 20 countries.
The main services provided include GSM, 3G, 4G LTE mobile services and has also diversified
into a variety of allied activities like Airtel DTH, Airtel Broad Band services, Airtel Payment
Banking services etc. In India it has a total market capitalization of about 1,23,879.4 Cr. Rs.
making it the largest private Tele-com firm in India.
Vodafone India merged with Idea cellular to form Vodafone Idea, which is the second
largest Tele-com firm in India with a total market capitalization of about 27,080 Cr. Rs.
Vodafone Idea also offers similar services to Airtel like GSM, 3G, 4G, payment banking etc.
2. Objectives of the paper To apply quantitative mathematical models to assess the financial performance of the
organizations. Perform Time Series analysis of individual firms by comparing ROE and its
factors within the company.
To conduct scatterplot and correlation analysis between ROE and its factors of the
individual firms to establish relationship between ROE and its factors.
To perform correlation analysis among FL, ATO, OPM, TB and IB for individual firms
to verify the existence of multi-collinearity among these independent variables.
To frame regression equation for ROE, based on factors of ROE to establish strength.
To test whether Indian Tele-com companies are able to generate positive ROE for the
shareholders based on the collected secondary financial data.
To suggest improvements in the ROE of Tele-com companies in India using DuPont
model.
3. Literature review Thalassinos and Curtis, (2005), identified that case of original model of DuPont analysis,
financial performance is represented by Return on Equity (ROE) which is the ratio between Net-
Income to shareholder’s Equity. This can also be expressed as product of Return on assets (ROA)
and Financial Leverage (FL). Thalassinos and Liapis, (2013) , has stated that Return on Asset
(ROA) can be further decomposed into product of Profit Margin (PM) and Asset Turnover
(ATO), where profit margin is the ratio between net-income to sales and asset turnover is ratio
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between sales to total average assets for the period. PM represents firm’s profitability relating to
revenue (which depends on sales quantity and profit margin per unit) and ATO measures how
efficiently and effectively the company’s assets are utilized in generating profits through sales.
Study by P.Bauman, Mark, (2014) adds to the literature by providing guidance for
improving forecasts of company’s performance. This study employs DuPont analysis, which is a
tool used for decomposing a company’s return on net operating assets (RNOA) into asset
turnover (ATO) and profit margin (PM) which gives a valuable understanding about factors
driving operating profitability.
Work by Gardner, John.C; Mc Gowan, Carl.B; Moeller, Susan.E, (2011) , is a case study
on Coca-Cola Corporation. The main purpose of this literature is to educate students on how to
estimate a firm’s Sustainable Growth by implementation of DuPont system of financial analysis
with Coca-Cola corporation. The term sustainable growth in finance refers to highest sales
growth that a firm can achieve by means of using only its internally generated funds or retained
earnings (RE).
Paper by Loukopoulos, George; Roupas, Theodoros (2014) , aims at performing a
comprehensive financial statement analysis by using DuPont based decomposition scheme of
Nissim and Penman, (2001) . The DuPont decomposition scheme reveals a quick snapshot of
various factors influencing ROE. The descriptive analysis shows how capital structure decisions
(usage of financial leverage) of Hygeia eroded shareholder’s profits.
Key points from Literature Review
The comprehensive study of the Review of Literature provides valuable insights about
the variety of applications of DuPont model viz. establishment of inter relationship between ATO
and PM on RNOA, in ascertaining the sustainable growth, in creating well-organized common
investment indicator, in forecasting future profitability conditions, in development of software
tool to educate investment managers, to understand leverage effect and capital structure decisions
etc.
The literature study showed, a descriptive research methodology being adopted in the
research articles that are reviewed. Literature Review also indicates, the studies on the DuPont
analysis has been extensive and hence there is only a little room for research gap. However, in
context of Indian scenario, research gap exists, since DuPont analysis had not yet been performed
in the Tele-com sector of India.
Thus, from the valuable insights gained from the above Review of Literature, the
researcher now proceeds with Descriptive Research methodology and analysis will be performed
using Five Factor DuPont Model to ascertain the various factors influencing ROE of the selected
Tele-communication firms. A time series analysis for past decade (intra firm analysis) and cross-
sectional analysis (inter firm analysis) of the factors influencing ROE will be performed after
DuPont decomposition of ROE. Additionally, a regression analysis will be carried out between
ROE and its factors to understand the strength with which these factors influence ROE, for each
firm. By understanding their strength, companies can decide upon what factor to improve upon to
contribute for healthy improvement of ROE.
4. Methodology This study uses descriptive research design. The research study here employs the
secondary financial data viz. Income statements and balance sheets of the selected Tele-com
companies between 2007 and 2018. These secondary data are the data originally collected by the
prior researcher which was then utilized by some other researcher for their study. The reliability
of these secondary depends on the purpose and the accuracy with which it was collected by the
prior researcher for the original study. The major sources from which secondary financial data
collected for the current research include various reliable sources of financial statements such
like www.screener.in, www.moneycontrol.com and Company’s official websites etc. Due to the
secondary nature of the data used in this research study, sampling design involving population,
sample size, sampling frame and sampling techniques are irrelevant in this research
methodology.
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The primary tools used in the study include the Five Factor DuPont model of ROE
disaggregation. The secondary tools used include Time Series and Cross Sectional analyses using
line graph, Correlation analysis and Multiple Linear Regression analysis. All these analyses are
carried out using Microsoft Excel tool.
The DuPont Model Formulae [1 Factor]
ROE = (Net Income/Equity)
[3 Factor]
ROE = (NI/Sales) x (Sales/Total Assets) x (Total Assets / Equity)
[Return on Sales] x [ Asset Turnover] x [Financial Leverage]
[5 Factor]
ROE = (NI/EBT) x (EBT/EBIT) x (EBIT/Sales) x (Sales/Total Assets) x (Total Assets/
Equity)
[Tax Burden] x [Interest Burden] x [Operating Margin] x [Asset Turnover] x [Leverage]
Where,
Tax burden is the proportion of profits retained after paying taxes.
Interest burden is the proportion of profits retained after paying interest
obligations.
Operating profit margin is the operating income per dollar of sales.
Asset turnover shows asset utilization efficiency.
Financial leverage indicates proportions of debt and equity employed in capital
structure.
Regression Analysis Equation
Multiple Linear Regression analysis is carried out to understand the strength of impact
contributed by the independent variables (IV) on the dependent variable (DV). The
general form of multiple linear regression is given by the following equation.
Y = β0 + β1X1 + β2X2 +…+ βnXn + ε
Where,
Y = Dependent Variable
X1, X2…Xn = Independent Variables
β1, β 2… β n = Coefficients of Independent Variables
β0 = Y intercept (Constant) and
ε = Standard Error (avg. distance of scatter plots from regression line)
5. Analysis and Interpretation The analysis adopts the following structure. First, the financial data from the balance
sheets and income statements of the respective companies are consolidated into input tables.
Also, in order to make a meaningful comparison between income statement and balance sheet,
average value (current and previous year) is taken for balance sheet values to honor the matching
concept.
Secondly, a DuPont framework is modeled to ascertain ROE, through disaggregated
components of ROE viz. FL, ATO, OPM, TB and IB. Then, using the disaggregated values of
ROEs each firm, a time series analysis is performed to compare ROE and its factors within each
company between 2007 to 2018.
Also, a cross sectional analysis is performed by comparing ROE and its factors, between
two companies with their industrial benchmark values to infer the relative performance of the
companies during this period. These analyses will help to understand the impacts of various
internal and external factors influencing ROE of the companies.
The analysis finally ends with the Regression analysis of ROE, by taking ROE as a
dependent variable and its factors as independent variables. This analysis is performed to
understand the strength of influence of these individual factors on ROE of each company
between 2007-2018.
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Table 5.1 Consolidated Financial Data – Private Telecom Firms in India - 2007-2018
AIRTEL 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 11456 16589 25423 34501 44321 49688 50467 55039 49766 53299 67158 68546
AVERAGE TOTAL ASSETS (TA) 28754 38309 55814 67418 108857 151972 158279 171201 183738 207070 235024 249841
SALES (S) 18420 27012 37352 41829 59602 71506 76947 85864 96101 96532 95468 83688
OPERATING PROFIT (EBIT) 7264 11031 14973 16469 20164 23705 23258 26936 32749 32205 34150 29626
EARNINGS BEFORE TAX (EBT) 4678 7312 8591 10895 7678 6518 4785 7864 10540 12846 7723 3267
NET INCOME (NI) 4062 6395 7859 9163 5752 4257 2258 2773 4621 6077 3800 1099
VODAFONE IDEA 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 2593 2860 8394 12288 11814 12675 13678 15416 19777 23289 24142 25997
AVERAGE TOTAL ASSETS (TA) 8673 10801 19821 25353 27416 32217 35384 41988 53780 71215 91141 102921
SALES (S) 3927 6720 10131 12398 15438 19489 22407 26519 31571 35949 35576 28279
OPERATING PROFIT (EBIT) 1476 2262 2826 3251 3734 5047 5969 8302 10827 11675 10245 6082
EARNINGS BEFORE TAX (EBT) 509 1115 939 1075 997 1055 1577 3044 4933 4250 -863 -6499
NET INCOME (NI) 502 1042 882 954 899 723 1011 1968 3193 2728 -400 -4168
RELIANCE COMM. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 22930 25978 35653 42821 41930 38398 35073 33323 35366 34955 30272 15676
AVERAGE TOTAL ASSETS (TA) 56543 67066 89904 97772 94364 94157 91966 91202 91338 98708 104260 91571
SALES (S) 17190 18827 22251 21496 22431 19677 20561 21238 24787 25594 6554 4593
OPERATING PROFIT (EBIT) 6334 7827 8722 7147 8411 5785 4439 5674 6225 6265 -383 -1825
EARNINGS BEFORE TAX (EBT) 3600 7076 6197 5223 1517 882 815 116 946 232 -1373 -23891
NET INCOME (NI) 3531 5401 6045 4655 1345 928 672 1047 714 639 -1403 -23839
TATA COMM. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 5252 5202 5129 4821 4064 2937 1854 1113 561 303 615 1046
AVERAGE TOTAL ASSETS (TA) 12402 13458 17310 19921 19906 21362 23564 25076 25246 25076 23219 20353
SALES (S) 8611 8297 9963 11026 11932 14196 17213 17714 19913 18149 17620 16651
OPERATING PROFIT (EBIT) 1009 868 1254 1012 1216 1791 1974 2282 2804 2434 2393 2291
EARNINGS BEFORE TAX (EBT) 281 149 423 -681 -707 -718 -431 444 373 243 1467 46
NET INCOME (NI) 15 10 316 -598 -777 -795 -623 101 1 9 1233 -329
NETTLINX 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 17.5 18.3 19.0 18.8 18.0 17.3 17.6 17.7 18.0 18.0 18.3 18.9
AVERAGE TOTAL ASSETS (TA) 22.2 29.4 35.0 32.0 29.7 30.0 31.2 29.8 28.4 30.3 36.1 47.7
SALES (S) 10.4 11.4 15.82 12.95 10.52 8.71 11.46 8.63 10.89 21.39 19 15.72
OPERATING PROFIT (EBIT) 2.05 2.57 1.54 1.11 -1.72 -0.96 1.03 -2.09 -0.44 1.99 3.72 2.53
EARNINGS BEFORE TAX (EBT) 6.37 0.66 0.23 -0.72 -2.37 -0.21 0.67 -0.21 -0.17 1.66 3.36 1.91
NET INCOME (NI) 5.4 0.33 0.11 -0.63 -2.33 -0.28 0.58 -0.19 -0.16 1.66 2.34 -6.45
QUADRANT TELE. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 591 591 591 634 757 837 837 837 561.5 286 173.5 61
AVERAGE TOTAL ASSETS (TA) 826 884.5 855.5 773.5 809.5 803.5 731.5 799 627.5 615 557.5 404
SALES (S) 276 249 224 196 236 281 330 406 521 558 339 344
OPERATING PROFIT (EBIT) 37 8 -104 -16 -75 -32 12 -110 -78 27 -26 -28
EARNINGS BEFORE TAX (EBT) -116 -142 -214 -21 -224 -179 -136 -261 -240 -135 385 -301
NET INCOME (NI) -116 -143 -215 -21 -224 -179 -136 -261 -240 -135 385 -301
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AIRTEL 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 11456 16589 25423 34501 44321 49688 50467 55039 49766 53299 67158 68546
AVERAGE TOTAL ASSETS (TA) 28754 38309 55814 67418 108857 151972 158279 171201 183738 207070 235024 249841
SALES (S) 18420 27012 37352 41829 59602 71506 76947 85864 96101 96532 95468 83688
OPERATING PROFIT (EBIT) 7264 11031 14973 16469 20164 23705 23258 26936 32749 32205 34150 29626
EARNINGS BEFORE TAX (EBT) 4678 7312 8591 10895 7678 6518 4785 7864 10540 12846 7723 3267
NET INCOME (NI) 4062 6395 7859 9163 5752 4257 2258 2773 4621 6077 3800 1099
VODAFONE IDEA 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 2593 2860 8394 12288 11814 12675 13678 15416 19777 23289 24142 25997
AVERAGE TOTAL ASSETS (TA) 8673 10801 19821 25353 27416 32217 35384 41988 53780 71215 91141 102921
SALES (S) 3927 6720 10131 12398 15438 19489 22407 26519 31571 35949 35576 28279
OPERATING PROFIT (EBIT) 1476 2262 2826 3251 3734 5047 5969 8302 10827 11675 10245 6082
EARNINGS BEFORE TAX (EBT) 509 1115 939 1075 997 1055 1577 3044 4933 4250 -863 -6499
NET INCOME (NI) 502 1042 882 954 899 723 1011 1968 3193 2728 -400 -4168
RELIANCE COMM. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 22930 25978 35653 42821 41930 38398 35073 33323 35366 34955 30272 15676
AVERAGE TOTAL ASSETS (TA) 56543 67066 89904 97772 94364 94157 91966 91202 91338 98708 104260 91571
SALES (S) 17190 18827 22251 21496 22431 19677 20561 21238 24787 25594 6554 4593
OPERATING PROFIT (EBIT) 6334 7827 8722 7147 8411 5785 4439 5674 6225 6265 -383 -1825
EARNINGS BEFORE TAX (EBT) 3600 7076 6197 5223 1517 882 815 116 946 232 -1373 -23891
NET INCOME (NI) 3531 5401 6045 4655 1345 928 672 1047 714 639 -1403 -23839
TATA COMM. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 5252 5202 5129 4821 4064 2937 1854 1113 561 303 615 1046
AVERAGE TOTAL ASSETS (TA) 12402 13458 17310 19921 19906 21362 23564 25076 25246 25076 23219 20353
SALES (S) 8611 8297 9963 11026 11932 14196 17213 17714 19913 18149 17620 16651
OPERATING PROFIT (EBIT) 1009 868 1254 1012 1216 1791 1974 2282 2804 2434 2393 2291
EARNINGS BEFORE TAX (EBT) 281 149 423 -681 -707 -718 -431 444 373 243 1467 46
NET INCOME (NI) 15 10 316 -598 -777 -795 -623 101 1 9 1233 -329
NETTLINX 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 17.5 18.3 19.0 18.8 18.0 17.3 17.6 17.7 18.0 18.0 18.3 18.9
AVERAGE TOTAL ASSETS (TA) 22.2 29.4 35.0 32.0 29.7 30.0 31.2 29.8 28.4 30.3 36.1 47.7
SALES (S) 10.4 11.4 15.82 12.95 10.52 8.71 11.46 8.63 10.89 21.39 19 15.72
OPERATING PROFIT (EBIT) 2.05 2.57 1.54 1.11 -1.72 -0.96 1.03 -2.09 -0.44 1.99 3.72 2.53
EARNINGS BEFORE TAX (EBT) 6.37 0.66 0.23 -0.72 -2.37 -0.21 0.67 -0.21 -0.17 1.66 3.36 1.91
NET INCOME (NI) 5.4 0.33 0.11 -0.63 -2.33 -0.28 0.58 -0.19 -0.16 1.66 2.34 -6.45
QUADRANT TELE. 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
AVERAGE EQUITY (EQ) 591 591 591 634 757 837 837 837 561.5 286 173.5 61
AVERAGE TOTAL ASSETS (TA) 826 884.5 855.5 773.5 809.5 803.5 731.5 799 627.5 615 557.5 404
SALES (S) 276 249 224 196 236 281 330 406 521 558 339 344
OPERATING PROFIT (EBIT) 37 8 -104 -16 -75 -32 12 -110 -78 27 -26 -28
EARNINGS BEFORE TAX (EBT) -116 -142 -214 -21 -224 -179 -136 -261 -240 -135 385 -301
NET INCOME (NI) -116 -143 -215 -21 -224 -179 -136 -261 -240 -135 385 -301
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Table 5.2 Five Factor DuPont Model for Disaggregation of ROE
Table 5.2 shows the framework of five factor DuPont Model which is created to
ascertain ROE. It involves disaggregating ROE into various components, known as factors. The
original DuPont equation has only 3 factors namely Profit Margin (PM), Asset Turnover (ATO)
and Financial Leverage (FL). In this model an extended DuPont Model is used which has 5
factors in total, obtained by further drilling down PM into 3 factors namely Operating Profit
Margin (OPM), Tax Burden (TB) and Interest Burden (IB). Using this model ROE values are
disaggregated into respective components to attach more meaning to ROE and a time series
analysis and cross sectional analysis will be performed based on the results from the model.
In the time series analysis, the consolidated financial data gathered is fed into the model
to ascertain the factors contributing to ROE. The results for each company is tabulated for period
between 2007 to 2018. Based on the table values, line graphs are plotted in excel to analyze the
trend of individual factors influencing ROE of the firm. Inferences are derived on the basis of
correlating the trend with the internal and external factors of the business scenario.
The following tables show the financial leverage, asset turnover, operating profit margin, tax
burden and interest burden affecting ROE of Bharti Airtel between 2007 to 2018, both per year
value and average value, calculated using excel sheet separately.
Table 5.3 ROE and its Factors for each year - Airtel - 2007-2018
Five Factor DuPont Model for ROE Formulae
ROE
FL =TA/EQ
ATO = S/TA
ROA X
FL OPM = EBIT/S
TB = NI/EBT
IB = EBT/EBIT
ATO X
PM
Legend
ROE - RETURN ON EQUITY
ROA - RETURN ON ASSET
PM - PROFIT MARGIN
ATO - ASSET TURN OVER
OPM X
TB X
IB FL - FINANCIAL LEVERAGE
OPM - OPERATING PROFIT MARGIN
TB - TAX BURDEN
IB - INTEREST BURDEN
Factor 3
Factor 1
Factor 2
Factor 4
Factor 5
AIRTEL 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
ROE 35% 39% 31% 27% 13% 9% 4% 5% 9% 11% 6% 2%
FL 251% 231% 220% 195% 246% 306% 314% 311% 369% 389% 350% 364%
ATO 64% 71% 67% 62% 55% 47% 49% 50% 52% 47% 41% 33%
OPM 39% 41% 40% 39% 34% 33% 30% 31% 34% 33% 36% 35%
TB 87% 87% 91% 84% 75% 65% 47% 35% 44% 47% 49% 34%
IB 64% 66% 57% 66% 38% 27% 21% 29% 32% 40% 23% 11%
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Table 5.4 ROE and its Factors for 12 years - Airtel - 2007-2018
Avg. ROE Airtel (2007-2018) 11%
Avg. FL Airtel (2007-2018) 315%
Avg. ATO Airtel (2007-2018) 48%
Avg. OPM Airtel (2007-2018) 34%
Avg. TB Airtel (2007-2018) 63%
Avg. IB Airtel (2007-2018) 34%
The following tables show the financial leverage, asset turnover, operating profit margin,
tax burden and interest burden affecting ROE of Vodafone Idea between 2007 to 2018, both per
year value and average value, calculated using excel separately.
Table 5.5 ROE and its Factors for each year - Vodafone - 2007-2018
Table 5.6 ROE and its Factors for 12 years - Vodafone - 2007-2018
Avg. ROE Vodafone (2007-2018) 5%
Avg. FL Vodafone (2007-2018) 301%
Avg. ATO Vodafone (2007-2018) 48%
Avg. OPM Vodafone (2007-2018) 29%
Avg. TB Vodafone (2007-2018) 77%
Avg. IB Vodafone (2007-2018) 17%
Using the above data Line graphs are plotted with various combinations of ROE and its
Factors to gain insights from the trend formed by them. The graphs are analyzed and results are
interpreted.
Figure 5.1 Line Graph – Cross Section Analysis – ROE Trend - Airtel and
Vodafone - 2007-2018
VODAFONE 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
ROE 19% 36% 11% 8% 8% 6% 7% 13% 16% 12% -2% -16%
FL 334% 378% 236% 206% 232% 254% 259% 272% 272% 306% 378% 396%
ATO 45% 62% 51% 49% 56% 60% 63% 63% 59% 50% 39% 27%
OPM 38% 34% 28% 26% 24% 26% 27% 31% 34% 32% 29% 22%
TB 99% 93% 94% 89% 90% 69% 64% 65% 65% 64% 46% 64%
IB 34% 49% 33% 33% 27% 21% 26% 37% 46% 36% -8% -107%
19%
25%20%
15%
7% 5% 3%
5%
8% 8%3%
-25%
35%39%
31%
2…
13%
9%
4%
5%
9% 11% 6%2%
19%
36%
11%8%
8%6%
7%
13%16%
12%
-2%
-16%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 1 8
ROE (Industry) ROE (Airtel) ROE (Vodafone)
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Table 5.7 Input Data - Regression Analysis of ROE – Airtel – 2007-2018
BHARTI AIRTEL LTD. ROE FL ATO OPM TB IB
2007 35% 251% 64% 39% 87% 64%
2008 39% 231% 71% 41% 87% 66%
2009 31% 220% 67% 40% 91% 57%
2010 27% 195% 62% 39% 84% 66%
2011 13% 246% 55% 34% 75% 38%
2012 9% 306% 47% 33% 65% 27%
2013 4% 314% 49% 30% 47% 21%
2014 5% 311% 50% 31% 35% 29%
2015 9% 369% 52% 34% 44% 32%
2016 11% 389% 47% 33% 47% 40%
2017 6% 350% 41% 36% 49% 23%
2018 2% 364% 33% 35% 34% 11%
Figure 5.2 Evaluation of Correlation btw. DV & IVs - Scatter Plot – Airtel
From the above figures it is clear there exists a strong positive correlation of ROE with
ATO, OPM, TB and IB while FL is negatively correlated. Thus, there is a non-existence of any
non-contributing independent variable in the ROE regression equation for Bharti Airtel.
Table 5.8 Evaluation of Correlation among IVs - Correlation Analysis – Airtel
FL ATO
FL OPM
FL TB
FL 100%
FL 100%
FL 100%
ATO -82% 100%
OPM -65% 100%
TB -88% 100%
FL IB
ATO OPM
ATO TB
FL 100%
ATO 100%
ATO 100%
0%
500%
0% 500% 1000% 1500%
FL VS ROE
R = -0.769, P = .003
0%
100%
0% 10% 20% 30% 40% 50%
ROE VS ATO
R = 0.9193, P = .000
0%
50%
0% 10% 20% 30% 40% 50%
ROE VS OPM
R = 0.874 , P = .000
0%
50%
100%
0% 10% 20% 30% 40% 50%
ROE VS TB
R =0.901 , P = 0.000
0%
50%
100%
0% 10% 20% 30% 40% 50%
ROE VS IB
R = 0.957 , P =0.000
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IB -78% 100%
OPM 68% 100%
TB 86% 100%
ATO IB
OPM TB
OPM IB
ATO 100%
OPM 100%
OPM 100%
IB 93% 100%
TB 78% 100%
IB 80% 100%
TB IB
TB 100%
IB 87% 100%
The above table reveals the fact that the IVs influencing ROE of Bharti Airtel are in
inter-related, and thus these factors may be redundant variables in the regression equation for
ROE. There exists a strong negative correlation between FL with ATO, OPM and IB, while ATO
has positive relation with OPM, TB and IB. The OPM bears positive relation with TB and IB,
also TB is also strongly proportional to IB. This complex interaction among these 5 IVs makes a
complex or dynamic impact on the DV (ROE).
Hypothesis Testing
Null Hypothesis H0: All the IVs don’t have significant influence on ROE.
i.e., H0: β1 = β2 = β3 = β4 = β5 = 0
Alternate Hypothesis H1: At least one IV have a significant influence on ROE.
i.e., H1: At least one βi ≠ 0, where i = 1 to 5
The significance level chosen for the analysis is, α = 5%.
Table 5.9 Regression and Collinearity Analyses of ROE and its Factors – Airtel
Regression Statistics
VIF > 5 => High Multi-Collinearity
Multiple R 0.9879
FL 3.25 Accept
R Square 0.9759
ATO 6.24 Reject
Adjusted R
Square 0.9557
OPM 2.25 Accept
Standard
Error 0.0278
TB 5.52 Reject
Observations 12.0000
IB 7.41 Reject
ANOVA
df SS MS F
Significa
nce F
Regression 5.0000 0.1872
0.037
4
48.51
11 0.0001
Residual 6.0000 0.0046
0.000
8
Total 11.0000 0.1919
Coefficie
nts
Standard
Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept -0.7961 0.2200
-
3.618
6
0.011
1 -1.3344 -0.2578 -1.3344 -0.2578
FL 0.0347 0.0290
1.196
3
0.276
7 -0.0362 0.1055 -0.0362 0.1055
ATO 0.4953 0.2373
2.087
5
0.081
9 -0.0853 1.0758 -0.0853 1.0758
OPM 1.2406 0.4423
2.805
1
0.031
0 0.1584 2.3227 0.1584 2.3227
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TB 0.1279 0.1129
1.132
4
0.300
7 -0.1485 0.4042 -0.1485 0.4042
IB 0.1730 0.1476
1.172
5
0.285
4 -0.1881 0.5342 -0.1881 0.5342
From the above table, the value of Correlation Coefficient (R) is 0.9879, which
shows high positive correlation between DV and all the IVs. R2 indicates the value of
Coefficient of Determination. In case of Multiple Linear Regression, we consider only
adjusted R2 value. Here, adjusted R2 value is 0.9557, which means that the IVs (viz. FL,
ATO, OPM, TB and IB) accounts for 95.57% of variability in the DV, i.e., ROE. In the
ANOVA table F-Significance value is 0.0001 which is much less than α = 0.05, hence it
affirms with 99.99% confidence that the overall model is statistically significant. In the
last section of the table, P -Value indicates probability value of an IV, which when less
than α = 0.05 then it confirms the significant effect of that IV on the DV. In other words,
if P-value for at least one of the IV is less than α = 0.05, then the Null Hypothesis, H0,
will be rejected. From the above table, P-value of OPM is 0.0310, which is less than α =
0.05, hence Alternate Hypothesis H1 is accepted, i.e., at least one IV (OPM) have a
significant influence on ROE. Also, the effects of FL, ATO, TB and IB doesn’t appear to
be statistically significant for the chosen level of significance. However, based on the
VIFs ascertained for each of the 5 IVs separately, the results indicate only FL and OPM
have VIF < 5, indicating very low collinearity. Thus, in order to have a best regression
model we re-run the entire regression considering only OPM and FL as IVs, although P-
value of FL is statistically insignificant in the above regression. This will eliminate the
multi-collinearity effects of ATO, TB and IB on OPM and FL. The Regression Model is
re-run and the results are tabulated in the following table.
Table 5.10 Input Data - Regression Analysis of ROE – Vodafone – 2007-2018
VODAFONE IDEA LTD. ROE FL ATO OPM TB IB
2007 19% 334% 45% 38% 99% 34%
2008 36% 378% 62% 34% 93% 49%
2009 11% 236% 51% 28% 94% 33%
2010 8% 206% 49% 26% 89% 33%
2011 8% 232% 56% 24% 90% 27%
2012 6% 254% 60% 26% 69% 21%
2013 7% 259% 63% 27% 64% 26%
2014 13% 272% 63% 31% 65% 37%
2015 16% 272% 59% 34% 65% 46%
2016 12% 306% 50% 32% 64% 36%
2017 -2% 378% 39% 29% 46% -8%
2018 -16% 396% 27% 22% 64% -107%
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Figure 5.3 Evaluation of Correlation between. DV & IVs - Scatter Plot – Vodafone
From the scatter plot, it is understood that ATO, OPM and IB have a positive correlation
with ROE, while TB has a medium correlation but its value is statistically insignificant. Though
FL has a weak negative correlation with ROE, even this value is statistically insignificant. Thus
FL and TB are non-contributing IVs in the regression equation of ROE for Vodafone.
Table 5.11 Evaluation of Correlation among IVs - Correlation Analysis – Vodafone
FL ATO
FL OPM
FL TB
FL 100%
FL 100%
FL 100%
ATO -53% 100%
OPM 20% 100%
TB -28% 100%
FL IB
ATO OPM
ATO TB
FL 100%
ATO 100%
ATO 100%
IB -53% 100%
OPM 30% 100%
TB 16% 100%
ATO IB
OPM TB
OPM IB
ATO 100%
OPM 100%
OPM 100%
IB 80% 100%
TB 20% 100%
IB 62% 100%
TB IB
TB 100%
IB 37% 100%
From the above table it is clear that there exists a mild correlation among the IVs. This
correlation leads to redundancy among the IVs influencing ROE. There exists a weak positive
correlation between FL and OPM, ATO and OPM, ATO and TB, TB and IB & OPM and TB. A
strong positive correlation between ATO and IB. A medium correlation between OPM and IB. A
medium negative correlation between FL and ATO & FL and IB. Weak negative correlation
between FL and TB. This multi-collinearity among IVs cause complex or dynamic influence of
IVs on the DV (ROE) for Vodafone.
Hypothesis Testing
Null Hypothesis H0: All the IVs don’t have significant influence on ROE.
i.e., H0: β1 = β2 = β3 = β4 = β5 = 0
0%
500%
-20% 0% 20% 40%
ROE VS FL
R = -0.073 , P = 0.82
0%
100%
-20% 0% 20% 40%
ROE VS ATO
R = 0.668 , P = 0.0174
0%
50%
-20% 0% 20% 40%
ROE VS OPM
R = 0.753 , P = 0.004
0%
200%
-20% 0% 20% 40%
ROE VS TB
R = 0.531 , P = 0.075
-200%
0%
200%
-20% 0% 20% 40%
ROE VS IB
R = 0.82 , P = 0.001
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Alternate Hypothesis H1: At least one IV have a significant influence on ROE.
i.e., H1: At least one βi ≠ 0, where i = 1 to 5
The significance level chosen for the analysis is, α = 5%.
Table 5.12 Regression and Collinearity Analyses of ROE and its Factors – Vodafone
Regression Statistics
VIF > 5 => High Multi-
Collinearity
Multiple R 0.9879
FL 2.32 Accept
R Square 0.9760
ATO 2.45 Accept
Adjusted R
Square 0.9560
OPM 3.16 Accept
Standard
Error 0.0260
TB 0.82 Accept
Observations 12.0000
IB 7.29 Reject
ANOVA
df SS MS F
Significan
ce F
Regression 5 0.16436
0.032
87
48.759
74 0.00009
Residual 6 0.00405
0.000
67
Total 11 0.16841
Coefficie
nts
Standard
Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept -0.7569 0.1301
-
5.817
7 0.0011 -1.0753 -0.4386 -1.0753 -0.4386
FL 0.1008 0.0234
4.300
2 0.0051 0.0434 0.1582 0.0434 0.1582
ATO 0.4493 0.1402
3.204
4 0.0185 0.1062 0.7924 0.1062 0.7924
OPM 0.2885 0.3705
0.778
7 0.4658 -0.6181 1.1951 -0.6181 1.1951
TB 0.2778 0.0527
5.266
8 0.0019 0.1487 0.4068 0.1487 0.4068
IB 0.1661 0.0628
2.645
9 0.0382 0.0125 0.3197 0.0125 0.3197
From the above table, the value of Correlation Coefficient (R) is 0.9879, which shows
high positive correlation between DV and all the IVs. Adjusted R2 value is 0.9560, which means
that the IVs (viz. FL, ATO, OPM, TB and IB) accounts for 95.6% of variability in the DV, i.e.,
ROE. In the ANOVA table F-Significance value is 0.0009 which is much less than α = 0.05,
hence, it affirms with 99.99% confidence that the overall model is statistically significant. In the
last section of the table, P -value indicates probability value of an IV, which when less than α =
0.05, it confirms the significant effect of that IV on the DV. In other words, if P-value for at least
one of the IV is less than α = 0.05, then the Null Hypothesis, H0, will be rejected. From the
above table, P-values of FL = 0.005, ATO = 0.018, TB = 0.0019 and IB = 0.0125 which are less
than α = 0.05, Hence Alternate Hypothesis H1 is accepted as 4 out of 5 IVs have a significant
effect on ROE. Also, the effect of OPM doesn’t appear to be statistically significant for the
chosen level of significance. However, based on the VIFs ascertained for each of the 5 IVs
separately, indicates only IB have VIF > 5, indicating high collinearity. Thus, in spite IB’s
significant P-value, we should eliminate IB from further regression analyses due to high multi-
collinearity and although OPM has insignificant P-value we include OPM in the next regression
analysis due to its low multi-collinearity.
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6. Discussion of findings Based on the time series, cross sectional and regression analyses conducted on ROE and its
factors (decomposed using DuPont model) for Bharti Airtel and Vodafone, a detailed summary
findings are made as follows.
6.1 Findings of Time Series analysis of ROE - Airtel
A downward trend in the ROE from 35% to 2% between 2007- 2018, which
synchronizes with Economic depression, 2G scam’s effect, Reliance Jio’s market
competition and demonetization etc.
It is observed that major portion of capital structure of the firm is financed through debts
or leveraged, increasing from 251% to 364% between 2007-2018, which is observed to
be the major cause for increasing interest payment obligations of the firm.
There is a sort of dynamic inter-relationship that exist among the factors influencing
ROE, i.e., existence of multi-collinearity.
Falling trend in asset usage efficiency is observed from 64% to 33% between 2007-2018.
The operating profit margin of the firm remained constant with minor fluctuations with
an average value of 34% between 2007-2018 which is well above industrial average for
the period.
The tax payment burden between 2007-2018 increased from 13% to 66%, which might
be attributed due to higher profits from operating and non-operating sources and it is
observed that the firm has no control over tax burden to increase ROE, as tax burden is
statutory in nature.
It is found that interest payment commitment of the firm increased from 36% to 89%
between 2007-2018 which might be due to high leveraging effect.
6.2 Findings of Time Series analysis of ROE for Vodafone Idea
A downward trend in the ROE from 19% to -16% between 2007- 2018, which
synchronizes with Economic depression, 2G scam’s effect, Reliance Jio’s market
competition and demonetization and Vodafone Idea merger etc.
It is observed that major portion of capital structure of the firm is financed through debts
or leveraged, increasing from 334% to 396% between 2007-2018, which is observed to
be the major cause for increasing interest payment obligations of the firm.
There is sort of dynamic inter-relationship that exist among the factors influencing ROE,
i.e., existence of multi-collinearity.
Falling trend in asset usage efficiency is observed from 45% to 27% between 2007-2018,
which might be due to poor utilization of acquired assets from Vodafone-Idea merger.
The operating profit margin of the firm fluctuates sinusoidal between 2007-2018 with an
average value of 29% which is slightly below the industry average.
The tax payment burden between 2007-2018 increased from 1% to 36%, which might be
attributed due to higher profits from operating and non-operating sources and it is
observed that the firm has no control over tax burden to increase ROE, as tax burden is
statutory in nature.
It is found that interest payment commitment of the firm increased from 64% to 207%
between 2007-2018 which might be due to high leveraging effect.
6.3 Findings of Cross sectional analysis of ROE
The average ROE for past 12 years shows that Airtel having highest ROE at 11%
followed by the industry with 6% and Vodafone with 5%. This general slump in ROE
may be attributed due to numerous internal factors (firm related decisions viz. ATO
efficiency, Operating efficiency, Capital structure decisions etc.) and external factors
(macro-economic factors viz. Fiscal and Monetary policies, political influences, market
competition etc.).
The average FL for past 12 years shows that Airtel having highest FL at 315% followed
by the industry with 310% and Vodafone with 301%. This increasing FL by Airtel,
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Vodafone and the industry synchronizes with the 2G scam’s effect that forced the
telecom operators to borrow money.
The average ATO for past 12 years shows that Airtel and Vodafone having similar ATO
efficiency and much higher than average ATO of the industry with 41%.
The average OPM for past 12 years shows that Airtel having highest OPM at 34%
followed by the industry with 30% and Vodafone with 29%.
The average TB for past 12 years shows that Industry having highest burden of tax at
39% followed by the Airtel with 37% and Vodafone with 23%. Tax burden is statutory
in nature and the firms have no control over it to improve their ROE.
Finally, the average IB for past 12 years shows that Vodafone having highest interest
payment obligations at 83% followed by the industry with 75% and Airtel with 66%.
6.4 Findings of Regression analysis of ROE - Airtel
Correlation analysis between ROE and its factors showed FL negatively correlated to
ROE with r = -0.769, while ATO, OPM, TB and IB have positive correlation with ROE
with r being 0.919, 0.874, 0.901 and 0.957 respectively.
Correlation analysis among factors influencing ROE shows
o FL and IB with r = -0.78
o ATO and OPM with r = 0.68
o ATO and TB with r = 0.86
o ATO and IB with r = 0.93
o OPM and TB with r = 0.78
o OPM and IB with r = 0.8 and
o TB and IB with r = 0.87,
which means these IVs are multi-collinear and acts as redundant variable to
one another in ROE regression equation. But multi-collinearity issue is not
addressed (mentioned in the limitations of the study) while framing
regression equation because, the original five factor DuPont model suggests
to factor in all the above variables as they have high degree of correlation
with ROE.
Final regression analysis of ROE for Airtel shows only OPM has a statistically
significant impact on ROE with coefficient value of 3.24, which means for every
percentage of OPM increase, ROE increases 3.24 %.
6.5 Findings of Regression analysis of ROE - Vodafone
Correlation analysis between ROE and its factors showed FL negatively correlated to
ROE with r = -0.073, while ATO, OPM, TB and IB have positive correlation with ROE
with r being 0.017, 0.753, 0.531 and 0.820 respectively.
Correlation analysis among factors influencing ROE shows
o FL and IB with r = -0.53
o ATO and OPM with r = 0.20
o ATO and TB with r = -0.28
o ATO and IB with r = -0.53
o OPM and TB with r = 0.20
o OPM and IB with r = 0.62 and
o TB and IB with r = 0.37,
which means these IVs are multi-collinear and acts as redundant variable to
one another in ROE regression equation. But multi-collinearity issue is not
addressed (mentioned in the limitations of the study) while framing
regression equation because, the original five factor DuPont model suggests
to factor in all the above variables as they have high degree of correlation
with ROE.
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Final regression analysis of ROE for Airtel shows that except for IB, all other factors viz.
FL, ATO, TB and OPM to have a statistically significant impact on ROE with coefficient
values being 0.058, 0.717, 0.315 and 1.109 respectively. It means that for every unit
increase in values of FL, ATO, TB and OPM, ROE increases by 0.058, 0.717, 0.315 and
1.109 times respectively.
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