FINANCIAL PERFORMANCE OF TWO WHEELER INDUSTRY IN INDIA

Transcription

1 I J A B E R, Vol. 14, No. 6, (2016): FINANCIAL PERFORMANCE OF TWO WHEELER INDUSTRY IN INDIA A. Aparna * Abstract: Over the past many years, two wheelers have been garnering an important space in the life of Indian population, especially youngsters and working population in the midst of rapid economic growth of India. Since 2-wheeler industry has been a backbone for the growth of many Indians and hence, India, the various aspects of sustainability of this industry in terms of financial and operating performance under the purview of different factors has been looked into. Three leading players in this segment i.e., Hero MotoCorp, TVS Motors and Bajaj Autohave 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 to layout the analysis. The study suggests that CTR has been more influential in terms of determining ROCE than EBITM for all the three organizations. Also, there has been positive correlation between all the liquidity ratios of TVS Motors and Bajaj Auto. INTRODUCTION India, like many other developing countries, is characterized by its rising population and rapid urbanization. The urban population has sprouted over the past three decades from 160 million in 1981 to 217 million in 1991 to 285 million in This rapid growth has resulted in increased usage of motor vehicles and their ownership. As Indian cities have grown in population rapidly, there has also been a proportionate spread outward. This resulted in the increased number of trips and the further dependence on motorized transport. The government has always encouraged development of newer places in distant suburbs thus encouraging sprawl. According to Ramachandran R. (1989) Indian suburbs are characterized by an uncontrolled mix of industrial development, dumps and obnoxious uses with the extension of urban settlement causing conditions in the overtaken villages to deteriorate, both physically and socially. This induces further decentralization causing major transport problems, leading to an increase in the ownership and usage of motor vehicles. The motor vehicle industry in India underwent a sea change during the period , when economic reforms aimed at encouraging competition were introduced. During this period, the two-wheeler industry saw the largest * Narsee Monjee Institute of Management Studies, Bangalore, aparnam1@yahoo.com

2 3608 A. Aparna proliferation of brands in the consumer durables industry. From then on, the rate of growth of two wheelers increased rapidly over the next two decades. The Indian industrial sector has undergone profound regulatory changes as a consequence of economic reforms put together in between 1988 and As a result, some of the industries that have been influenced are the consumer durables which include two-wheelers, refrigerators, some financial services etc. The two-wheeler industry experienced a huge change during the period resulting in certain degree of imperfection. OVERVIEW OF THE INDUSTRY The two-wheeler industry has been in existence in India since It basically consists of three segments: scooters, motorcycles and mopeds. The industry has witnessed increased sales with every passing decade. In 1971, sales were around 0.1 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 because of industrial practices. Policies like MRTP and FERA were principal vehicles which led 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 exit and in their place new technologies took over. The nineties thereafter, saw proliferation of new brands due to liberalization. The was a period during which the overall growth of two-wheeler industry was high, around 15% annually. The oil price hikes in 1974, led to the higher degree of restrictions and control over industry, which in turn, led to greater popularity of two wheelers because of higher fuel efficiency was a period of the initiation of reforms because of technological backwardness earlier, particularly in the two-wheeler segment. The entity of foreign collaborations were allowed up to a capacity of 100 c.c. thereby the weaker players exited. This was specially observed in the motorcycle segment where new technology led to superior products onwards most significant changes were observed in the two-wheeler segment due to liberalization of the economy. The two-wheeler industry was completely deregulated. In the nineties firms started competing on the basis of product features mainly because of an increase in the number of brands and increase in the number of sales. LITERATURE REVIEW Modern research indicates market structure as an important instrument for studying consumer brand preferences. Chintagunta (1994) used static market

3 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 response of the Indian government. Due to the economic reforms introduced in 1991, it was expected that profit-cost margins would decline as a result of greater competition. Goldar and Aggarwal (2005) studied this aspect whether this was actually observed or not. They used panel data for 137 industries for the periods and An econometric model was estimated using tariff and nontariff barriers as explanatory variables. The results showed that lowering of tariff rates did have a significant effect on competitiveness, thus trending towards reduced price-cost margins. However, the same was not observed in the manufacturing period in the post-reform period. Rather, there was an increase in the profit-cost margins. As a result of this many industries, especially consumer durables have exhibited propensity to evolve into oligopolies post reforms. Sunila George, Raghabendra Jha and Hari. K. Nagarajan (2002) also observed that the two-wheeler industry is characterized by oligopoly post reforms. They studied the evolution of the competitive structure of the two-wheeler industry of India using Kendall s index of 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-conduct performance (SCP) model as the principal factor for determining profitability at an 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 the industry in detail, serves to get better insights. These factors, in turn, help various firms within the same industry to gain competitive advantage which eventually lead to profitability (Amato and Wilder 1990). Further, it has been suggested that increased size has a higher degree of bureaucratization (Besanko et al 2004). According to Leibstein (1976) there could be a negative relationship between firm size and profitability due to x-inefficiencies. Hall and Weiss (1967) state a positive relationship between firm size and profitability. Further, Marcus (1969) stated that firm size and profitability are associated positively to each other, but not in all industries. Many others like Berk (1997), Michaeles et. al (1999), Cassar and Holmes (2003) showed the existence of a positive relationship between firm size and profitability. 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 understanding the relationship between profitability and firm size across industries. However, very little has been researched to study the financial performance of auto-industry and in particular, the two-wheeler industry. Thus the purpose of the study is to examine and analyze the performance of the two-wheeler industry in the Indian context by incorporating various ratios.

4 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 annual reports, Review of the Indian Economy by CMIE (Centre for Monitoring Indian Economy) and SIAM (Society of Indian Automobile Manufacturers) for selected 2-wheeler companies which are the three leading players in the market. In order to study the operating profitability and liquidity trends, various financial ratios have been studied. Further, multiple regression analysis has been used to examine the impact of EBITM and CTR on ROCE. Operating profitability shows how efficiently the firm is applying its resources to generate returns. Operatingprofitability has been chosen over net profitability as a metric for the study as operating profits represent the earnings of the firm from its core operations. Net profit takes into account earnings from core operations as well as other financial income. It also factors in interest expenses, tax and any exceptional item which may be present latently. It might not always give the real picture on how the company is performing on an operating level. So, to reflect the company s core performance in a better way, it was prudent to choose operating profitability. Multiple Regression Analysis is a multi-variate technique that is used to study the cause and effect by regressing the independent variables on the dependent variable. The egression equation can be written as: Where, Y = ROCE X1 = CTR X2 = EBITM Y = 0 + 1x1+ 2x2+ Multiple Regression Analysis is based on the principles of Ordinary Least Squares Estimation (OLSE). Any violation of the assumption of OLSE leads to the subsequent failure of the regression model. R 2 determines the extent to which the independent variables are defining the dependent variable. R 2 is defined as Coefficient 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 variables inflate the value of R 2. Thus, it is imperative to adjust the value of R 2 against these extraneous variables. This R 2 is known as adjusted R 2. All these aspects have been analysed in the forthcoming sections. The study covers three leading players in the 2-wheeler market. The total market share of these three companies combined is about 68 percent (Hero -42%, TVS-

5 Financial Performance of Two Wheeler Industry in India %, 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 to In order to analyse operating profitability and liquidity of the selected 2-wheeler companies various financial ratios and statistical tools like multiple regression analysis and correlation analysis have been used. ANALYSIS OF THE STUDY The main goal of any commercial organization is to earn profits and create value for each of its stakeholders. Every stakeholder looks forward to getting something in return. So, in this section the trends of operating profitability and liquidity of Hero MotoCorp, Bajaj Auto and TVS Motors using financial and statistical tools have been looked into. OPERATING PROFITABILITY ANALYSIS Operating profitability shows how efficiently the firm is applying its resources to generate returns on its business. It gives an idea of how strong the company is on the operational front. The following ratios have been taken for analysis:- (a) (b) (c) Return on capital employed (ROCE) = EBIT/ Capital Employed Capital Turnover ratio (CTR) = Sales/ Capital Employed 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 and TVS with some aberrations in between. For TVS Motors, it is quite clear from the data that it was hit hard by the financial crunch as its EBITM went down dramatically to sub 2% levels. Similarly, ROCE also took a knock during this period bringing it down to mid-single digit levels of around 6%. Its CTR ranged from 2.14 on the lower side to 3.99 on the higher side. Bajaj Auto on the other hand mainly focuses on its margins being high which is reflected in the given data. EBITM is much higher compared to both its rivals. It has significantly improved its CTR over the year which is a good sign. Over the last few years ROCE is seen to be settling on the higher 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

6 3612 A. Aparna can be taken as independent variables. We have tried to analyse the impact these 2 independent variables have on ROCE with the help of multiple regression model. Where, Y= ROCE X1= CTR X2= EBITM 1, 2 = regression coefficients = error 0 = intercept Y = 0 + 1x1+ 2x2+ Here, values of the different components of the regression analysis have been shown. For Hero MotoCorp, a unit increase in EBITM results in an increase in ROCE by units whereas for a unit increase in CTR results in units increase in ROCE. This clearly shows that the influence of CTR on ROCE is much higher than that of EBITM. Here, we get the value of R square = This is quite high. It means that 99.9 percent of variation in ROCE is explained by EBITM and CTR. About 0.1 percent is accounted by 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 in ROCE by units. A unit increase in CTR results in an increase in ROCE by units which is higher than that of EBITM. So, we can say that the influence of CTR on ROCE is slightly higher compared to EBITM. R square value is This means that 98.8 of variation in ROCE are explained by both the independent variables. About 1.2 percent accounts for error. In case of Bajaj Auto, the influence of CTR is far greater than that of EBITM. Here, R square value is So, it means that 98.6 percent of the variation in ROCE is explained by both the independent variables. About 1.4 percent is accounted for error. CORRELATION ANALYSIS OF PROFITABILITY RATIOS The correlation among various operating profitability ratios show a high positive association for all the three companies taken for study. Highest positive correlation was found between ROCE and EBITM for TVS Motors at

7 LIQUIDITY ANALYSIS Financial Performance of Two Wheeler Industry in India 3613 Liquidity signifies the ability of the company to meet its current obligations. Adequate liquidity reflects that the company is financially sound but inadequate liquidity is not taken too positively. The following ratios have been taken for analysing the liquidity position of Hero MotoCorp, TVS Motors and Bajaj Auto:- (a) (b) (c) Current Ratio (CR) = Current Assets/ Current Liabilities Debt-Equity Ratio (DER)= Debt/ Shareholder s Equity Quick Ratio (QR) = Liquid Assets/ Current Liabilities Ideally 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. TVS Motors as well as Bajaj Auto have CR close to the levels of 1 whereas for Hero MotoCorp it hovers around the 0.5 mark. This isn t something to worry about as they have generated strong operating cash flows over the years. Although Hero MotoCorp has gone in for expansions over the past few years we still see that it has the best Debt-Equity Ratio amongst the 3 companies for the study period. It has almost negligible debt on its books. Hero MotoCorp has been able to meet its investment and expansion plans from its internal accruals. As for Bajaj Auto it can be seen that they have reduced their external debt quite drastically over the years and is now almost a debt free company. TVS Motors on the other hand 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 on the back of a revival in scooter demand. So, it has been able to fund its investments without taking more debt on board. Quick Ratio (QR) signifies the short term liquidity of a firm. It should be ideally greater than 1 which would show that the company is in apposition to pare off its debts with liquid holdings. But, here we see that the QR is less than 1 which tells us that it is quite acceptable for this industry as a whole. Hero MotoCorp as well as Bajaj Auto have had a proven track record of generating strong operating cash flows over the years. So, the QR on the lower side isn t much 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 of TVS Motors and Bajaj Auto. But, there has been a negative correlation between CR and DER for Hero MotoCorp. DER and QR have also shown a negative correlation for Hero MotoCorp. The highest positive correlation has been seen between CR and DER for TVS Motors at The most negative correlation is seen for Hero MotoCorp between CR and DER at (-0.865).

8 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 sales have been sluggish over the past few years? The operating profit levels of the individual companies clearly reflect that. Exports have been the only saviour for companies like Bajaj Auto in this sluggish period. The Indian companies have gained a major foothold in the past few years in the global markets. Bajaj Auto is the market leader in motorcycles in countries like Sri Lanka, Bangladesh, Nigeria, etc.hero MotoCorpon the other hand is strongly focussing its exports in Latin American markets like Brazil and Argentina. It also has presence in Peru, Columbia and a few African markets. As for TVS Motors its focus of exports is largely in South and South-east Asia. Liquidity issues shouldn t be a worry for any of these companies going forward as they are financially quite sound. Same would be the case for Honda Motorcycle and Scooter India (HMSI). Any upturn in the economy would ensure that they would be able to capitalize and grow at a good pace. The only concern regarding exports for the industry remains with the currency fluctuations around the globe and 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 is reflecting on the performance of Hero MotoCorp. TVS Motors would also be affected in case of a rural slowdown. It too has a strong rural presence along with entry-level portfolio. For the likes of Bajaj Auto and HMSI rural isn t such a big play. A large part of their portfolio is urban focussed. Provided urban consumption improves faster than expected they narrow the gap to the leader Hero MotoCorp. Exports too pose a threat in some of the highly unpredictable (politically) countries in Africa and Latin America. On the margins front Bajaj Auto has been an industry leader for quite some time and it expects to hold onto margins going forward with the increase in premium segment portfolio.hero MotoCorpon the other hand has been fighting hard to expand margins and match that of Bajaj s. Data for HMSI isn t available as it is not listed. TVS Motors too only can boast of mid-single digit margins. It would want to expand that going forward to near double-digits. India is projected to be the 3 rd largest auto market in the world by There is significant growth potential in India with the burgeoning middle class and rising rural incomes to go with it. Price discounts, automobile loans and a slew of launches slated in the next few years from all the big companies the 2-wheeler industry would certainly be on a growth trajectory that would be sustainable over the years to come.

9 Financial Performance of Two Wheeler Industry in India 3615 References Amato, L. and Wilder, R.P. (1985), The effect of firm size on profit rate in U.S. Manufacturing, Southern Economics Journal, Vol. 52, Bala, R., Ong, D. and Matthew, C.H. (2005), Firm size, ownership and performance in the Malaysian palm oil industry, Accounting and Finance, Vol. 1, Barney, J. (1991), Firm resources and sustained competitive advantage, Journal of Management, Vol. 17, No. 1, Berk, J.B. (1997), Does Size Really Matter? Financial Analysts Journal, Vol. 53, pp Besanko, D., and Ulrich, D (2004), Capacity dynamics and endogenous asymmetries in firm size, RAND Journal of Economics, Vol. 35, No.1, Spring, Capon, N., Farley, J. and Hoenig, H. (1990), Determinants of financial performance: A meta- Analysis, Management Science, Vol. 36, No. 10, Cassar, G. and Holmes (2003), Capital Structure and Financing of SMEs: Australian evidence, Accounting and Finance Journal, Vol. 43, No. 2, Chintagunta, P. (1994), Heterogeneous logit model implications for brand positioning, Journal of Marketing Research, Vol. 31, Erdem, T. (1996), A dynamic analysis of market structure based on panel data, Marketing Science, Vol. 15, No. 4, George, S., Jha, R. and Hari, Nagaraj (2002), The evolution and structure of Two wheeler Industry in India, ASARC Working Paper. Goddard, T. and Wilson, M. and John, O. S. (2005), Determinants of profitability in European manufacturing and Service: evidence from dynamic panel model, Applied Financial Economics, Vol. 15, No.18, Goldar, B. and S.C. Aggarwal (2005), Trade liberalization and price-cost margin in Indian industries, the developing Economies, Vol. 43, No. 3, Hall, M. and Weiss, L. (1967), Firms Size and Profitability, The Review of Economics and Statistics, Vol. 49, pp Joshi, V and I.M.D. Little (1996), India s economic reforms , Clarendon Press, Oxford. Leibenstein, H. (1976), Beyond Economic Man, Cambridge, MA: Harvard University Press. Marcus, M. (1969), Profitability and size of firm: some further evidence, Review of Economics and Statistics, Vol. 51, pp Michaeles, N., Chittenden, F. and Poutziouris, P. (1999), Financial policy and capital structure choice in U.K. SMEs: Empirical evidence from company panel data, Small Business Economics Journal, Vol. 12, No.2, Peteraf, M. A. (1993), The cornerstones of competitive advantage, Strategic Management Journal, Vol 14, No.3, Porter, M.E. (1981), Competitive Strategy: Techniques for analysing industries and competitors, New York: The Free Press. Ramachandran, R. (1989), Urbanisation and Urban systems in India, Oxford University Press. Rajan. R and Zingales, L. (2000), The firm as a dedicated hierarchy, a theory of the origins and the growth of firms, NBER Working Paper, No. W Wernerfelt, B. (1984), A resource based view of the firm, Strategic Management Journal, Vol. 5, No. 2,

10 3616 A. Aparna Regression-OUTPUT-HEROMOTOCORP-PROFITABILITY Descriptive Statistics Mean Std. Deviation N Roce Ctr Ebitm Correlations Roce ctr ebitm Pearson Correlation Roce ctr ebitm Sig. (1-tailed) Roce ctr ebitm N Roce ctr ebitm Variables Entered/Removed b Model Variables Entered Variables Removed Method 1 ebitm, ctr a. Enter a. All requested variables entered. b. Dependent Variable: Roce Model Summary b Model R R Square Adjusted R Std. Error of the Durbin-Watson Square Estimate a a. Predictors: (Constant), ebitm, ctrb. Dependent Variable: Roce ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression a Residual Total a. Predictors: (Constant), ebitm, ctr b. Dependent Variable: Roce

11 Financial Performance of Two Wheeler Industry in India 3617 Coefficients a Model Unstandardized Standardized Collinearity Coefficients Coefficients Statistics B Std. Error Beta t Sig. Tolerance VIF 1 (Constant) Ctr ebitm Collinearity Diagnostics a Variance Proportions Model Dimension Eigenvalue Condition Index (Constant) ctr ebitm Residuals Statistics a Minimum Maximum Mean Std. Deviation N Predicted Value Residual Std. Predicted Value Std. Residual Regression-OUTPUT-BAJAJ AUTO-PROFITABILITY Descriptive Statistics Mean Std. Deviation N Roce CTR EBITM Correlations Roce CTR EBITM Pearson Correlation Roce CTR EBITM Sig. (1-tailed) Roce CTR EBITM N Roce CTR EBITM

12 3618 A. Aparna Variables Entered/Removed b Model Variables Entered Variables Removed Method 1 EBITM, CTR a. Enter a. All requested variables entered. b. Dependent Variable: Roce Model Summary b Model R R Square Adjusted R Std. Error of the Durbin-Watson Square Estimate a a. Predictors: (Constant), EBITM, CTR b. Dependent Variable: Roce ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression a Residual Total a. Predictors: (Constant), EBITM, CTR b. Dependent Variable: Roce Coefficients a Model Unstandardized Standardized Collinearity Coefficients Coefficients Statistics B Std. Error Beta t Sig. Tolerance VIF 1 (Constant) CTR EBITM Collinearity Diagnostics a Variance Proportions Model Dimension Eigenvalue Condition Index (Constant) CTR EBITM

13 Financial Performance of Two Wheeler Industry in India 3619 Residuals Statistics a Minimum Maximum Mean Std. Deviation N Predicted Value Residual Std. Predicted Value Std. Residual Regression-OUTPUT- TVS MOTORS-PROFITABILITY Descriptive Statistics Mean Std. Deviation N Roce Ctr Ebitm Correlations Roce ctr ebitm Pearson Correlation Roce ctr ebitm Sig. (1-tailed) Roce ctr ebitm N Roce ctr ebitm Variables Entered/Removed b Model Variables Entered Variables Removed Method 1 ebitm, ctr a. Enter a. All requested variables entered. b. Dependent Variable: Roce Model Summary b Model R R Square Adjusted R Std. Error of the Durbin-Watson Square Estimate a a. Predictors: (Constant), ebitm, ctr b. Dependent Variable: Roce

14 3620 A. Aparna ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression a Residual Total a. Predictors: (Constant), ebitm, ctr b. Dependent Variable: Roce Coefficients a Model Unstandardized Standardized Collinearity Coefficients Coefficients Statistics B Std. Error Beta t Sig. Tolerance VIF 1 (Constant) Ctr ebitm Collinearity Diagnostics a Variance Proportions Model Dimension Eigenvalue Condition Index (Constant) ctr ebitm Residuals Statistics a Minimum Maximum Mean Std. Deviation N Predicted Value Residual Std. Predicted Value Std. Residual

15 Financial Performance of Two Wheeler Industry in India 3621 Correlations-HEROMOTOCORP-LIQUIDITY Correlations CR DER QR CR Pearson Correlation **.740 * Sig. (2-tailed) DER Pearson Correlation ** Sig. (2-tailed) QR Pearson Correlation.740 * Sig. (2-tailed) **. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed). Correlations-BAJAJ AUTO-LIQUIDITY Correlations CR DER QR CR Pearson Correlation * Sig. (2-tailed) DER Pearson Correlation Sig. (2-tailed) QR Pearson Correlation.660 * Sig. (2-tailed) *. Correlation is significant at the 0.05 level (2-tailed). Correlations-TVS MOTORS-LIQUIDITY Correlations CR DER QR CR Pearson Correlation **.735 * Sig. (2-tailed) DER Pearson Correlation.841 ** ** Sig. (2-tailed) QR Pearson Correlation.735 *.788 ** 1 Sig. (2-tailed) **. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed).

16

17