FINANCIAL PERFORMANCE OF TWO WHEELER …serialsjournals.com/serialjournalmanager/pdf/1469788321.pdftwo-wheelers, refrigerators, some financial services etc. ... Financial Performance

* Narsee Monjee Institute of Management Studies, Bangalore, E-mail: [email protected]

FINANCIAL PERFORMANCE OF TWO WHEELERINDUSTRY IN INDIA

A. Aparna*

Abstract: Over the past many years, two wheelers have been garnering an important space inthe life of Indian population, especially youngsters and working population in the midst ofrapid economic growth of India. Since 2-wheeler industry has been a backbone for the growthof many Indians and hence, India, the various aspects of sustainability of this industry interms of financial and operating performance under the purview of different factors has beenlooked into. Three leading players in this segment i.e., Hero MotoCorp, TVS Motors and BajajAutohave been studied. Financial data for the past 10 years has been gathered for these companies.Multiple regression analysis and correlation analysis across this data has been employed tolayout the analysis. The study suggests that CTR has been more influential in terms ofdetermining ROCE than EBITM for all the three organizations. Also, there has been positivecorrelation between all the liquidity ratios of TVS Motors and Bajaj Auto.

INTRODUCTION

India, like many other developing countries, is characterized by its rising populationand rapid urbanization. The urban population has sprouted over the past threedecades from 160 million in 1981 to 217 million in 1991 to 285 million in 2001. Thisrapid growth has resulted in increased usage of motor vehicles and their ownership.As Indian cities have grown in population rapidly, there has also been aproportionate spread outward. This resulted in the increased number of trips andthe further dependence on motorized transport. The government has alwaysencouraged development of newer places in distant suburbs thus encouragingsprawl. According to Ramachandran R. (1989) Indian suburbs are characterizedby an “uncontrolled mix of industrial development, dumps and obnoxious uses”with the “extension of urban settlement causing conditions in the overtaken villagesto deteriorate, both physically and socially.” This induces further decentralizationcausing major transport problems, leading to an increase in the ownership andusage of motor vehicles.

The motor vehicle industry in India underwent a sea change during the period1985-1991, when economic reforms aimed at encouraging competition wereintroduced. During this period, the two-wheeler industry saw the largest

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proliferation of brands in the consumer durables industry. From then on, the rateof growth of two wheelers increased rapidly over the next two decades. The Indianindustrial sector has undergone profound regulatory changes as a consequence ofeconomic reforms put together in between 1988 and 1991. As a result, some of theindustries that have been influenced are the consumer durables which includetwo-wheelers, refrigerators, some financial services etc.

The two-wheeler industry experienced a huge change during the 1985-1991period resulting in certain degree of imperfection.

OVERVIEW OF THE INDUSTRY

The two-wheeler industry has been in existence in India since 1955. It basicallyconsists of three segments: scooters, motorcycles and mopeds. The industry haswitnessed increased sales with every passing decade. In 1971, sales were around0.1 million units per annum. The production capacities also increased rapidly,resulting in an approximately 4 million units of sales volume in late nineties.

The two-wheeler industry in India to a large extent has been shaped so becauseof industrial practices. Policies like MRTP and FERA were principal vehicles whichled to the growth of some segments in the two-wheeler industry. In the eighties,many new firms entered the market which forced the outdated products to exitand in their place new technologies took over. The nineties thereafter, sawproliferation of new brands due to liberalization.

The 1970-1980 was a period during which the overall growth of two-wheelerindustry was high, around 15% annually. The oil price hikes in 1974, led to thehigher degree of restrictions and control over industry, which in turn, led to greaterpopularity of two wheelers because of higher fuel efficiency.

1981-1990 was a period of the initiation of reforms because of technologicalbackwardness earlier, particularly in the two-wheeler segment. The entity of foreigncollaborations were allowed up to a capacity of 100 c.c. thereby the weaker playersexited. This was specially observed in the motorcycle segment where newtechnology led to superior products.

1991 onwards most significant changes were observed in the two-wheelersegment due to liberalization of the economy. The two-wheeler industry wascompletely deregulated. In the nineties firms started competing on the basis ofproduct features mainly because of an increase in the number of brands and increasein the number of sales.

LITERATURE REVIEW

Modern research indicates market structure as an important instrument forstudying consumer brand preferences. Chintagunta (1994) used static market

Financial Performance of Two Wheeler Industry in India � 3609

models to study this aspect. Erdem (1996) used dynamic market structures model.Joshi and Little (1996) discuss the economic crisis of 1991 and the policy responseof the Indian government. Due to the economic reforms introduced in 1991, itwas expected that profit-cost margins would decline as a result of greatercompetition. Goldar and Aggarwal (2005) studied this aspect whether this wasactually observed or not. They used panel data for 137 industries for the periods1980-81 and 1997-98. An econometric model was estimated using tariff and non-tariff barriers as explanatory variables. The results showed that lowering of tariffrates did have a significant effect on competitiveness, thus trending towardsreduced price-cost margins. However, the same was not observed in themanufacturing period in the post-reform period. Rather, there was an increasein the profit-cost margins.

As a result of this many industries, especially consumer durables have exhibitedpropensity to evolve into oligopolies post reforms. Sunila George, RaghabendraJha and Hari. K. Nagarajan (2002) also observed that the two-wheeler industry ischaracterized by oligopoly post reforms. They studied the evolution of thecompetitive structure of the two-wheeler industry of India using Kendall’s indexof Rank concordance. In determining factors that influence performance diversity,literature suggests that they could be either industry specific or firm specific.(Capon, Farley and Hoenig 1990). Porter (1981) used the structure-conductperformance (SCP) model as the principal factor for determining profitability atan industry level. On the other hand Wernerfelt (1984), Barney (1991), Peteraf (1993)suggest the explanation of the existence of more or less profitable firms within theindustry in detail, serves to get better insights. These factors, in turn, help variousfirms within the same industry to gain competitive advantage which eventuallylead to profitability (Amato and Wilder 1990). Further, it has been suggested thatincreased size has a higher degree of bureaucratization (Besanko et al 2004).According to Leibstein (1976) there could be a negative relationship between firmsize and profitability due to x-inefficiencies. Hall and Weiss (1967) state a positiverelationship between firm size and profitability. Further, Marcus (1969) stated thatfirm size and profitability are associated positively to each other, but not in allindustries. Many others like Berk (1997), Michaeles et. al (1999), Cassar and Holmes(2003) showed the existence of a positive relationship between firm size andprofitability. On the other hand, Rajan and Zingales (2000), Goddard and Wilson(2005), Bala Ramasamy et al. (2005) exhibited a negative relationship.

It can be observed that a lot of research is already existing on understandingthe relationship between profitability and firm size across industries. However,very little has been researched to study the financial performance of auto-industryand in particular, the two-wheeler industry. Thus the purpose of the study is toexamine and analyze the performance of the two-wheeler industry in the Indiancontext by incorporating various ratios.

3610 � A. Aparna

METHODOLOGY

The study has been conducted for a period of 10 years from FY2005 to FY2014.Data has been collected from various sources like Capitoline, Company and annualreports, Review of the Indian Economy by CMIE (Centre for Monitoring IndianEconomy) and SIAM (Society of Indian Automobile Manufacturers) for selected2-wheeler companies which are the three leading players in the market.

In order to study the operating profitability and liquidity trends, variousfinancial ratios have been studied. Further, multiple regression analysis has beenused to examine the impact of EBITM and CTR on ROCE.

Operating profitability shows how efficiently the firm is applying its resourcesto generate returns. Operatingprofitability has been chosen over net profitabilityas a metric for the study as operating profits represent the earnings of the firmfrom its core operations. Net profit takes into account earnings from core operationsas well as other financial income. It also factors in interest expenses, tax and anyexceptional item which may be present latently. It might not always give the realpicture on how the company is performing on an operating level. So, to reflect thecompany’s core performance in a better way, it was prudent to choose operatingprofitability.

Multiple Regression Analysis is a multi-variate technique that is used to studythe cause and effect by regressing the independent variables on the dependentvariable. The egression equation can be written as:

Y = �0 + �1x1+ �2x2+ €

Where,

Y = ROCE

X1 = CTR

X2 = EBITM

Multiple Regression Analysis is based on the principles of Ordinary LeastSquares Estimation (OLSE). Any violation of the assumption of OLSE leads to thesubsequent failure of the regression model. R2 determines the extent to which theindependent variables are defining the dependent variable. R2 is defined asCoefficient of Determination. It is the ratio of unexplained variance to total variance.In a regression model, it is observed many a times that some extraneous variablesinflate the value of R2. Thus, it is imperative to adjust the value of R2 against theseextraneous variables. This R2 is known as adjusted R2.

All these aspects have been analysed in the forthcoming sections.

The study covers three leading players in the 2-wheeler market. The total marketshare of these three companies combined is about 68 percent (Hero -42%, TVS-


13%, Bajaj -13%) as of FY14. The only major player missing from our study is HMSI(Honda motorcycle and scooter India) which has a chunk of the market share (24%).

This study has been done for a period of 10 years from 2004-05 to 2013-14. Inorder to analyse operating profitability and liquidity of the selected 2-wheelercompanies various financial ratios and statistical tools like multiple regressionanalysis and correlation analysis have been used.

ANALYSIS OF THE STUDY

The main goal of any commercial organization is to earn profits and create valuefor each of its stakeholders. Every stakeholder looks forward to getting somethingin return. So, in this section the trends of operating profitability and liquidity ofHero MotoCorp, Bajaj Auto and TVS Motors using financial and statistical toolshave been looked into.

OPERATING PROFITABILITY ANALYSIS

Operating profitability shows how efficiently the firm is applying its resources togenerate returns on its business. It gives an idea of how strong the company is onthe operational front. The following ratios have been taken for analysis:-

(a) Return on capital employed (ROCE) = EBIT/ Capital Employed(b) Capital Turnover ratio (CTR) = Sales/ Capital Employed(c) Ebit Margin (EBITM) = EBIT/ Sales� EBITM OF Hero MotoCorp ranged between 17.98% on the higher side

and 10.69% on the lower side. CTR is a tad higher compared to its peers.ROCE for the above mentioned period is much better than both Bajaj andTVS with some aberrations in between.

� For TVS Motors, it is quite clear from the data that it was hit hard by the2008-2009 financial crunch as its EBITM went down dramatically to sub2% levels. Similarly, ROCE also took a knock during this period bringingit down to mid-single digit levels of around 6%. Its CTR ranged from 2.14on the lower side to 3.99 on the higher side.

� Bajaj Auto on the other hand mainly focuses on its margins being highwhich is reflected in the given data. EBITM is much higher compared toboth its rivals. It has significantly improved its CTR over the year whichis a good sign. Over the last few years ROCE is seen to be settling on thehigher side of its range.

IMPACT OF EBITM AND CTR ON ROCE

ROCE is a key indicator in financial analysis. It is a product of EBITM and CTR.Hence, ROCE can be taken as the dependent variable whereas EBITM and CTR

3612 � A. Aparna

can be taken as independent variables. We have tried to analyse the impactthese 2 independent variables have on ROCE with the help of multiple regressionmodel.

Y = �0 + �1x1+ �2x2+ €

Where,

Y= ROCE

X1= CTR

X2= EBITM

�1, �2 = regression coefficients

€ = error

�0 = intercept

Here, values of the different components of the regression analysis have beenshown.

� For Hero MotoCorp, a unit increase in EBITM results in an increase inROCE by 4.146 units whereas for a unit increase in CTR results in 14.594units increase in ROCE. This clearly shows that the influence of CTR onROCE is much higher than that of EBITM. Here, we get the value of Rsquare = 0.999. This is quite high. It means that 99.9 percent of variationin ROCE is explained by EBITM and CTR. About 0.1 percent is accountedby error. This R square value suggests that the model is robust.

� In TVS Motors, we see a unit increase in EBITM results in an increase inROCE by 2.990 units. A unit increase in CTR results in an increase inROCE by 3.482 units which is higher than that of EBITM. So, we can saythat the influence of CTR on ROCE is slightly higher compared to EBITM.R square value is 0.988. This means that 98.8 of variation in ROCE areexplained by both the independent variables. About 1.2 percent accountsfor error.

� In case of Bajaj Auto, the influence of CTR is far greater than that of EBITM.Here, R square value is 0.986. So, it means that 98.6 percent of the variationin ROCE is explained by both the independent variables. About 1.4 percentis accounted for error.

CORRELATION ANALYSIS OF PROFITABILITY RATIOS

The correlation among various operating profitability ratios show a high positiveassociation for all the three companies taken for study. Highest positive correlationwas found between ROCE and EBITM for TVS Motors at 0.950.


LIQUIDITY ANALYSIS

Liquidity signifies the ability of the company to meet its current obligations.Adequate liquidity reflects that the company is financially sound but inadequateliquidity is not taken too positively. The following ratios have been taken foranalysing the liquidity position of Hero MotoCorp, TVS Motors and Bajaj Auto:-

(a) Current Ratio (CR) = Current Assets/ Current Liabilities(b) Debt-Equity Ratio (DER)= Debt/ Shareholder’s Equity(c) Quick Ratio (QR) = Liquid Assets/ Current LiabilitiesIdeally we know that the Current Ratio (CR) should be more than 1.5 but in

the automobile industry as of today a CR of less than 1 is quite acceptable. TVSMotors as well as Bajaj Auto have CR close to the levels of 1 whereas for HeroMotoCorp it hovers around the 0.5 mark. This isn’t something to worry about asthey have generated strong operating cash flows over the years.

Although Hero MotoCorp has gone in for expansions over the past few yearswe still see that it has the best Debt-Equity Ratio amongst the 3 companies for thestudy period. It has almost negligible debt on its books. Hero MotoCorp has beenable to meet its investment and expansion plans from its internal accruals. As forBajaj Auto it can be seen that they have reduced their external debt quite drasticallyover the years and is now almost a debt free company. TVS Motors on the otherhand has had a higher DER compared to its peers for the entire period of study.But now since the last 3 years its operating performance has improved majorly onthe back of a revival in scooter demand. So, it has been able to fund its investmentswithout taking more debt on board.

Quick Ratio (QR) signifies the short term liquidity of a firm. It should beideally greater than 1 which would show that the company is in apposition topare off its debts with liquid holdings. But, here we see that the QR is less than 1which tells us that it is quite acceptable for this industry as a whole. HeroMotoCorp as well as Bajaj Auto have had a proven track record of generatingstrong operating cash flows over the years. So, the QR on the lower side isn’tmuch of an issue.

CORRELATION ANALYSIS OF LIQUIDITY RATIOS

Here, we have tried to analyse the different liquidity ratios of Hero MotoCorp,Bajaj Auto and TVS Motors. There is a positive correlation between all the ratios ofTVS Motors and Bajaj Auto. But, there has been a negative correlation between CRand DER for Hero MotoCorp. DER and QR have also shown a negative correlationfor Hero MotoCorp. The highest positive correlation has been seen between CRand DER for TVS Motors at 0.841. The most negative correlation is seen for HeroMotoCorp between CR and DER at (-0.865).

3614 � A. Aparna

CONCLUSION

India has been growing below it potential for the last couple of years or so. But,green shoots are visible in the economy and an upturn maybe just round the corner.So, except for Hero MotoCorpwhich has high rural customer base domestic saleshave been sluggish over the past few years? The operating profit levels of theindividual companies clearly reflect that. Exports have been the only saviour forcompanies like Bajaj Auto in this sluggish period. The Indian companies havegained a major foothold in the past few years in the global markets. Bajaj Auto isthe market leader in motorcycles in countries like Sri Lanka, Bangladesh, Nigeria,etc.Hero MotoCorpon the other hand is strongly focussing its exports in LatinAmerican markets like Brazil and Argentina. It also has presence in Peru, Columbiaand a few African markets. As for TVS Motors its focus of exports is largely inSouth and South-east Asia.

Liquidity issues shouldn’t be a worry for any of these companies going forwardas they are financially quite sound. Same would be the case for Honda Motorcycleand Scooter India (HMSI). Any upturn in the economy would ensure that theywould be able to capitalize and grow at a good pace. The only concern regardingexports for the industry remains with the currency fluctuations around the globeand unforeseen geo-political conditions that keep cropping up.

The risk that the largest player,Hero MotoCorpfaces is from the rural slowdown.As has been seen for the last 3 quarters, rural consumption has reduced and that isreflecting on the performance of Hero MotoCorp. TVS Motors would also beaffected in case of a rural slowdown. It too has a strong rural presence along withentry-level portfolio. For the likes of Bajaj Auto and HMSI rural isn’t such a bigplay. A large part of their portfolio is urban focussed. Provided urban consumptionimproves faster than expected they narrow the gap to the leader Hero MotoCorp.Exports too pose a threat in some of the highly unpredictable (politically) countriesin Africa and Latin America.

On the margins front Bajaj Auto has been an industry leader for quite sometime and it expects to hold onto margins going forward with the increase inpremium segment portfolio.Hero MotoCorpon the other hand has been fightinghard to expand margins and match that of Bajaj’s. Data for HMSI isn’t available asit is not listed. TVS Motors too only can boast of mid-single digit margins. It wouldwant to expand that going forward to near double-digits.

India is projected to be the 3rd largest auto market in the world by 2017. Thereis significant growth potential in India with the burgeoning middle class and risingrural incomes to go with it. Price discounts, automobile loans and a slew of launchesslated in the next few years from all the big companies the 2-wheeler industrywould certainly be on a growth trajectory that would be sustainable over the yearsto come.


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3616 � A. Aparna

Regression-OUTPUT-HEROMOTOCORP-PROFITABILITY

Descriptive Statistics

Mean Std. Deviation N

Roce 54.0640 13.16274 10Ctr 3.8910 .41863 10Ebitm 13.8210 2.40235 10

Correlations

Roce ctr ebitm

Pearson Correlation Roce 1.000 .691 .896ctr .691 1.000 .300ebitm .896 .300 1.000

Sig. (1-tailed) Roce . .013 .000ctr .013 . .200ebitm .000 .200 .

N Roce 10 10 10ctr 10 10 10ebitm 10 10 10

Variables Entered/Removedb

Model Variables Entered Variables Removed Method

1 ebitm, ctra . Enter

a. All requested variables entered.b. Dependent Variable: Roce

Model Summaryb

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

1 .999a .999 .998 .57502 2.106

a. Predictors: (Constant), ebitm, ctrb. Dependent Variable: Roce

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 1557.005 2 778.503 2354.445 .000a

Residual 2.315 7 .331Total 1559.320 9



Coefficientsa

Model Unstandardized Standardized CollinearityCoefficients Coefficients StatisticsB Std. Error Beta t Sig. Tolerance VIF

1 (Constant) -60.025 1.887 -31.802 .000Ctr 14.594 .480 .464 30.410 .000 .910 1.099ebitm 4.146 .084 .757 49.581 .000 .910 1.099

a. Dependent Variable: Roce

Collinearity Diagnosticsa

Variance Proportions

Model Dimension Eigenvalue ConditionIndex (Constant) ctr ebitm

1 1 2.979 1.000 .00 .00 .002 .016 13.585 .09 .10 1.003 .005 24.008 .91 .90 .00


Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 42.2349 79.6121 54.0640 13.15297 10Residual -.81819 .63791 .00000 .50712 10Std. Predicted Value -.899 1.942 .000 1.000 10Std. Residual -1.423 1.109 .000 .882 10


Regression-OUTPUT-BAJAJ AUTO-PROFITABILITY

Descriptive Statistics


Roce 41.3320 21.71005 10CTR 2.2120 .76746 10EBITM 17.8900 5.23605 10

Correlations

Roce CTR EBITMPearson Correlation Roce 1.000 .835 .872

CTR .835 1.000 .480EBITM .872 .480 1.000

Sig. (1-tailed) Roce . .001 .001CTR .001 . .080EBITM .001 .080 .

N Roce 10 10 10CTR 10 10 10EBITM 10 10 10

3618 � A. Aparna



1 EBITM, CTRa . Enter


Model Summaryb


1 .993a .986 .982 2.94561 1.447

a. Predictors: (Constant), EBITM, CTRb. Dependent Variable: Roce

ANOVAb


1 Regression 4181.199 2 2090.599 240.946 .000a

Residual 60.736 7 8.677Total 4241.935 9

a. Predictors: (Constant), EBITM, CTRb. Dependent Variable: Roce

Coefficientsa

Model Unstandardized Standardized CollinearityCoefficients Coefficients Statistics

B Std. Error Beta t Sig. Tolerance VIF

1 (Constant) -37.943 3.749 -10.120 .000CTR 15.322 1.458 .542 10.508 .000 .770 1.299EBITM 2.537 .214 .612 11.869 .000 .770 1.299




Model Dimension Eigenvalue ConditionIndex (Constant) CTR EBITM

1 1 2.913 1.000 .01 .01 .012 .052 7.507 .35 .93 .073 .036 9.022 .64 .06 .92





Predicted Value 10.5415 74.5320 41.3320 21.55406 10Residual -4.72410 3.78800 .00000 2.59779 10Std. Predicted Value -1.429 1.540 .000 1.000 10Std. Residual -1.604 1.286 .000 .882 10


Regression-OUTPUT- TVS MOTORS-PROFITABILITYDescriptive Statistics


Roce 13.2100 6.81228 10Ctr 3.0140 .67973 10Ebitm 4.1950 1.74303 10

Correlations

Roce ctr ebitm

Pearson Correlation Roce 1.000 .754 .950ctr .754 1.000 .531ebitm .950 .531 1.000

Sig. (1-tailed) Roce . .006 .000ctr .006 . .057ebitm .000 .057 .

N Roce 10 10 10ctr 10 10 10ebitm 10 10 10



1 ebitm, ctra . Enter


Model Summaryb


1 .994a .988 .985 .83562 2.486


3620 � A. Aparna

ANOVAb


1 Regression 412.777 2 206.388 295.574 .000a

Residual 4.888 7 .698Total 417.665 9


Coefficientsa

Model Unstandardized Standardized CollinearityCoefficients Coefficients Statistics

B Std. Error Beta t Sig. Tolerance VIF

1 (Constant) -9.828 1.263 -7.781 .000Ctr 3.482 .484 .347 7.200 .000 .718 1.392ebitm 2.990 .189 .765 15.859 .000 .718 1.392




Model Dimension Eigenvalue ConditionIndex (Constant) ctr ebitm

1 1 2.906 1.000 .01 .00 .012 .075 6.242 .18 .02 .833 .020 12.205 .82 .97 .16




Predicted Value 2.0281 23.4410 13.2100 6.77230 10Residual -1.01881 1.12189 .00000 .73695 10Std. Predicted Value -1.651 1.511 .000 1.000 10Std. Residual -1.219 1.343 .000 .882 10



Correlations-HEROMOTOCORP-LIQUIDITY

Correlations

CR DER QR

CR Pearson Correlation 1 -.865** .740*

Sig. (2-tailed) .001 .014N 10 10 10

DER Pearson Correlation -.865** 1 -.518Sig. (2-tailed) .001 .125N 10 10 10

QR Pearson Correlation .740* -.518 1Sig. (2-tailed) .014 .125N 10 10 10

**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05level (2-tailed).

Correlations-BAJAJ AUTO-LIQUIDITYCorrelations

CR DER QR

CR Pearson Correlation 1 .302 .660*

Sig. (2-tailed) .396 .038N 10 10 10

DER Pearson Correlation .302 1 .230Sig. (2-tailed) .396 .523N 10 10 10

QR Pearson Correlation .660* .230 1Sig. (2-tailed) .038 .523N 10 10 10

*. Correlation is significant at the 0.05 level (2-tailed).

Correlations-TVS MOTORS-LIQUIDITYCorrelations

CR DER QR

CR Pearson Correlation 1 .841** .735*

Sig. (2-tailed) .002 .015N 10 10 10

DER Pearson Correlation .841** 1 .788**

Sig. (2-tailed) .002 .007N 10 10 10

QR Pearson Correlation .735* .788** 1Sig. (2-tailed) .015 .007N 10 10 10

**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05level (2-tailed).

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FINANCIAL PERFORMANCE OF TWO WHEELER …serialsjournals.com/serialjournalmanager/pdf/1469788321.pdftwo-wheelers, refrigerators, some financial services etc. ... Financial Performance