Data and Methodology - SSRN

Corporate Valuation in the Indian ITeS Sector

Ms. Srishty Sarawgi Jain Equity Analyst Mosaic Capital India Pvt. Ltd. Bangalore

Prof. J. V. Ramana Raju Faculty of Finance School of Business Alliance University Bangalore

Prof. Anirban Dutta Faculty of Finance School of Business Alliance University Bangalore

Prof. Mihir Dash Faculty of Management Science School of Business Alliance University Bangalore

Electronic copy available at: http://ssrn.com/abstract=1824577

Corporate Valuation in the Indian ITeS Sector Abstract The post-global financial crisis outlook for the Indian information technology-enabled services (ITeS) sector is fraught with uncertainty. Several major information technology (IT) companies are in the process of corporate restructuring wherein their ITeS offshoots are being merged back with the parents. The motivation for this trend may be more strategic than financial in nature. Traditionally, enterprise valuation models are either asset-based, earnings-based, or a mixture of these. Assets-based valuation models focus on fixed assets or tangible assets as a source of value creation, while earnings-based valuation models focus on earnings/profitability and growth. For ITeS companies, however, human resources (intangible assets) would be expected to play a more important role in driving value than tangible assets. The present study proposes a model for enterprise value in the ITeS sector involving tangible assets, intangible assets (intellectual capital), and earnings/growth. In particular, the model would explain the relative impact of each of these factors on enterprise value. Keywords: ITeS, corporate restructuring, valuation models, enterprise value, intellectual capital.

Introduction The ITeS industry provides a wide range of services that are delivered over telecom or data network to a range of external business areas. Examples of such services include customer service, web-content development, back office management and network consultancy, etc. The ITeS industry has boomed in India over the past decade. Some of the major factors through which India has established dominance in the global ITeS segment include: its low-cost but qualified workforce, English language fluency, governmental support, and a stable legislative and economic framework. The governmental drive to offer subsidies and tax relief benefits to the ITeS industry has been a particularly important factor in the enhanced competitiveness of India. According to NASSCOM’s Strategic Review 2011 the IT-ITeS sector is estimated to aggregate revenues of USD 88.1 billion in FY2011, with the IT software and services sector (excluding hardware) accounting for USD 76.1 billion of revenues. During this period, direct employment is expected to reach nearly 2.5 million, an addition of 240,000 employees, while indirect job creation is estimated at 8.3 million. As a proportion of national GDP, the sector revenues have grown from 1.2 per cent in FY1998 to an estimated 6.4 per cent in FY 2011. Its share of total Indian exports (merchandise plus services) increased from less than 4 per cent in FY1998 to 26 per cent in FY2011.

Electronic copy available at: http://ssrn.com/abstract=1824577

The Indian ITeS sector has not only added scale in the last decade, but has also matured significantly in terms of scope of service offerings, buyer segments served and service delivery models. Apart from achieving maturity in the horizontal segment, providers are increasingly developing vertical/domain specialization to capture greater value. Also, its reliance on the US economy has come down to some extent over the years as Indian ITeS companies have successfully started diversifying their client portfolio to Europe, West Asia, South-East Asia, and so on. The client base is still primarily in the United States (about 60%) followed by the United Kingdom (about 30%), with majority of the clients typically engaged in either the IT, financial services or telecom industries. There is however, a wave of competition threatening the dominant position of Indian ITeS, with countries like China, Mexico, Philippines, and Vietnam expanding outsourcing operations and often providing cheaper services. The industry has recently begun to focus on more lucrative knowledge intensive outsourcing services (knowledge process outsourcing).to differentiate themselves and stay ahead of the competition. The services, ranging from research and development, business and technical analysis, animation and design and learning solution to market research, data analytics, intellectual property rights and legal research, require high-end skills and intellectual attributes. India has a first mover advantage in this area and as a result, has captured a major share of this segment. The industry was very badly affected by the global recession. Most client companies, especially in the US, chose to cut budgets across the board and generate governmental support by not promoting overseas enterprise at the cost of already suffering local jobs. As a result, most Indian ITeS companies faced a tough 18-24 months. There are mixed signals even now regarding the industry’s recovery from the global financial crisis, but the prospects of the knowledge process outsourcing (KPO) segment are currently very bright, with most major players expanding their KPO capabilities in the last six months/one year.

Literature Valuation modeling is a very important application area of financial analysis. There is a very extensive literature on valuation modeling. Damodaran (2002) identified three basic approaches in valuation modeling: discounted cash flow (DCF) valuation models, relative valuation models, and contingent valuation models. DCF valuation models equate the value of a firm to the present value of expected future cash flows to the firm. While this approach is theoretically appealing, there have been several variations in the literature concerning the appropriate cash flows to be used, the treatment of tax shields, and the appropriate discount rate.

Modigliani and Miller (1958) studied the impact of leverage on firm value. According to their analysis, capital structure had no impact on the value of the firm in the absence of taxes; in the presence of a tax structure, however, the value of the firm is enhanced by the present value of tax shields. Myers (1974) proposed the adjusted present value (APV) model. He suggested that the present value of tax shields be computed by discounting the tax savings at the cost of debt (kd). Miles and Ezzel (1980) suggested a valuation model for firms assuming a constant debt-equity ratio. They suggested that the correct rate for discounting the tax saving due to debt is kd for the first year, and ku (the required return to equity in the unlevered firm) for tax savings in subsequent years. On the other hand, Harris and Pringle (1985) proposed that the value of tax shields should be calculated by discounting the tax savings at ku, arguing that the interest tax shields have the same systematic risk as the firm’s underlying cash flows. Copeland et al (2000) also advocated the APV model. Damodaran (1994) argued that business risk is borne by equity alone; that is, the beta of debt is zero. Thus, according to Damodaran, the levered beta depends only on the asset beta (i.e. the unlevered beta) and the debt-equity ratio. This introduced a leverage cost into the valuation (Fernandez, 2002). Fernandez (2002) argued that there were four basic methods for valuing companies using discounted cash flows: using the free cash flow and the weighted average cost of capital; using the expected equity cash flow and the required return to equity (ke); using the capital cash flow and weighted average cost of capital before tax; and using the adjusted present value. Relative valuation models express the value of a firm in terms of the market value of comparable firms. The simplest method for relative valuation uses valuation multiples; for example, the P/E ratio, or the P/BV ratio. Unfortunately, the use of valuation multiples can magnify errors in valuation, and are particularly in appropriate if earnings are negative. To overcome this, Beatty et al (1999) proposed the use of price-scaled regressions, taking a linear combination of value drivers. Liu et al (2002) provided the foundations for this method. Deng et al (2009) extended the model further. The development of sector- or industry-specific valuation models also has a large literature, almost as extensive as that of valuation modeling itself. Industry-specific models help in

identifying key value drivers for the particular industry, and are of great use in guiding strategic formulation in the particular industry (Damodaran, 2002). Hand (2003) examined equity valuation for Internet firms, based on earnings (profits and losses). He found that profits are positively prices, while losses are negatively priced. He suggested that the negative pricing of losses are not due to poor operating performance, but instead reflect massive investments in intangible assets that accounting rules require to be expensed rather than be treated as assets and amortized over time. Vardavaki and Mylonakis (2007) studied equity valuation in the UK food and drug retail sector. They compared the explanatory power of asset-based models, earnings-based models, and other linear regression models (using several determinant factors). They found that two major determining factors were dominant in explaining firm value: earnings (EBITDA) and book value of assets. Joos and Zhdanov (2008) studied equity valuation in the biotech industry. The biotech industry is characterized by long investment cycles with highly uncertain payoffs, and a high volatility of returns on biotech stocks. They suggested that a real options approach (contingent valuation model) would be appropriate, due to the high degree of uncertainty in firms’ earnings and investments over the life cycle. There are generally mixed results from the literature on the contribution of intangible assets to the value of a firm. On one hand, Jenning et al (2001) claimed that goodwill amortization only adds noise to the stock valuation trends, while Barth et al (2001) opined that the analyst coverage is significantly affected by the value of intangible assets. Thus, the role of intangibles in firm valuation may be an area of further investigation, especially in the context of a particular industry/segment.

Data and Methodology The objective of the study was to compare the impact of different value drivers on enterprise value for the Indian ITeS sector. Multiple linear regression was used for this purpose. The independent variables (value drivers) selected for the study were as follows: fixed assets (motivated by asset-based valuation models), working capital (also motivated by asset-based valuation models), long-term debt (motivated by extensions of asset-based valuation models), earnings before interest, tax, depreciation, and amortization (motivated by earnings-based models), and employee cost (representing human capital). Long-term debt was subsequently dropped, as it was highly censored (most of the sample ITeS companies had zero long-term debt). The dependent variable was the enterprise value. Multiple logarithmic regression was not possible, as several observations were zero or negative (especially for earnings before interest, tax, depreciation, and amortization, and working capital).

The sample companies selected for the study were the thirty-seven companies listed in the Indian ITeS sector in the Capitaline database, and the data was extracted directly from the Capitaline database. The data used for the analysis pertained to the year 2007-08, when the ITeS sector could be considered to be at its peak, and as data from subsequent years has been affected by the global financial meltdown, with generally lower valuations and negative enterprise values in many cases. The impact of different value drivers on enterprise value was assessed using stepwise multiple regression without constant term, as the constant term was found to be insignificant.

Stepwise Multiple Regression of Enterprise Value on Independent Variables (without constant term) Model Summary Model

R .723d

R Square .522

Adjusted R Square .502

Std. Error of the Estimate 443.9843749

d. Predictors: Employee Cost, EBITDA, Working Capital

Coefficientsa,b

Model Employee Cost EBITDA Working Capital

Unstandardized Coefficients B Std. Error 1.875 .672 3.802 1.297 1.363 .502

Standardized Coefficients Beta .330 .257 .311

t 2.790 2.932 2.716

Sig. .007 .005 .008

a. Dependent Variable: Enterprise Value b. Linear Regression through the Origin Excluded Variablesd,e

Model Fixed Assets

Beta In .205c

t 1.552

Sig. .125

Partial Correlation .181

Collinearity Statistics Tolerance .375

c. Predictors in the Model: Employee Cost, EBITDA, Working Capital d. Dependent Variable: Enterprise Value e. Linear Regression through the Origin

Stepwise multiple regression (without constant term) of enterprise value on the independent variables of fixed assets, working capital, EBITDA, and employee cost was found to be significant, explaining 52.2% of the variation in enterprise value. The variable with highest effect on enterprise value was working capital, followed by employee cost, and EBITDA. Fixed assets were found not to have a significant effect on enterprise value, though it was significant in conjunction with working capital and “human capital” (as discussed below).

Multipliers As each of the independent variables were found to be significant drivers of value, the multipliers corresponding to each of the variables were estimated by simple linear regressions (without constant terms). The results were as shown in the table below:

variable EBITDA fixed assets working capital employee cost

multiplier 6.8810 6.9450 2.6940 3.6540

beta 0.4650 0.6240 0.6150 0.6420

R2 21.60% 38.90% 37.80% 41.30%

t-stat 4.5150 6.8690 6.7060 7.2090

p-value 0.0000 0.0000 0.0000 0.0000

The EBITDA multiple, i.e. the enterprise value to earnings before interest, tax, depreciation, and amortization ratio (EV/EBITDA), measures the growth/earning potential of the firm. This is a conventional multiple, but in the present context of ITeS valuation, it explains only 21.6% f the variation in enterprise value. The fixed assets multiple, i.e. the enterprise value to fixed assets ratio (EV/FA), measures the contribution of fixed assets to firm value. This multiple on its own explains 38.9% of the variation in enterprise value. The fixed assets composition of the sample companies varied considerably. The most important component of fixed assets was that of computers, computer software, and other electronic equipment, in the range 16.37% - 91.37% for about 85% of the sample companies, while the remaining 15% of sample companies invested less than 10% of their fixed assets in computers and computer software. In fact, this latter group of companies had a higher level of investment in land and buildings, above 44.83% of their fixed assets, while the remaining 85% of companies had very marginal investment in land and buildings, less than 21% of fixed assets. Another important component of fixed assets was office fixtures and equipment, in the range 1.52% 24.08% of fixed assets for 94.6% of the sample companies, while it was excessively high for the remaining 5.4% of sample companies. The working capital multiple, i.e. the enterprise value to working capital ratio (EV/WC), measures the contribution of working capital to firm value. This multiple on its own explains 37.8% of the variation in enterprise value. The working capital position and composition of the sample companies varied considerably. 94.6% of the sample companies had high liquidity, with a current ratio in excess of 2.0; in fact, 5.4% of the sample companies had a current ratio in excess of 10.0. The most important component of current assets was that of loans & advances, in the range 11.48% - 97.18%, with

an average of 45.06% of current assets. The next most important component of current assets was that of cash & bank balances, in the range 0.17% - 73.62%, with an average of 23.14% of current assets. The third important component of current assets was that of receivables, in the range 0.00% - 73.09%, with an average of 29.68% of current assets. Inventory was a negligible component of current assets for the sample companies. Further, on an average, 78.89% of the receivables were short-term receivables, with duration of within six months. The employee cost multiple, i.e. the enterprise value to employee cost ratio (EV/EC), measures the contribution of employees to firm value. This multiple on its own explains 41.3% of the variation in enterprise value. Combining these three components, viz. fixed assets, working capital, and employee cost, yields the following model: Model Summary Model

R .707b

a

R Square .500

Adjusted R Square .479

Std. Error of the Estimate 454.3121278

a. For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models which include an intercept. b. Predictors: Employee Cost, Working Capital, Fixed Assets Coefficientsa,b

Model Fixed Assets Working Capital Employee Cost

Unstandardized Coefficients B Std. Error 3.250 1.457 1.326 .515 1.165 .479

Standardized Coefficients Beta .292 .303 .299

t 2.230 2.576 2.431

Sig. .029 .012 .019

a. Dependent Variable: Enterprise Value b. Linear Regression through the Origin

Stepwise multiple regression (without constant term) of enterprise value on the independent variables of fixed assets, working capital, and employee cost was found to be significant, explaining 50.0% of the variation in enterprise value. The variable with highest effect on enterprise value was working capital, followed by employee cost, and fixed assets.

Discussion The results of the study suggest that enterprise value in the Indian ITeS industry can be expressed as a combination of three multiples: the fixed assets multiple, the working capital multiple, and the “human capital” multiple. Together, these three multiples capture the productive capacity, operating finance, and human/intellectual capital dimensions of value creation. The fixed assets multiple represents the long-term value creation of productive capacity. In fact, it can be further refined by considering “operating fixed assets,” and by incorporating capacity utilization. In the context of the Indian ITeS industry, the “operating” fixed assets are essentially the computers, computer software, and other electronic equipment; that is, the technological aspect. Investment in technology, particularly the latest, most advanced technology, is an essential ingredient for growth and value in the ITeS industry. However, technology cannot be expected to create value by itself; it must be complemented with sufficient working capital and a suitable “human/intellectual capital” component. The working capital multiple represents the long-term value creation of operating finance. Though working capital is basically short-term funding of operations, efficient utilization of working capital in balancing liquidity and profitability is a key element in growth and value. In the context of the Indian ITeS industry, the major components of working capital are loans & advances, cash & bank balances, and receivables & payables. In particular, loans & advances represent investment of excess cash generated by the firm in its subsidiaries, usually to finance new projects and/or for research and development, which further enhances value. The employee cost multiple represents the long-term value creation of “human/intellectual capital.” In fact, human capital is the vital ingredient for growth and value creation in the ITeS industry. Effective utilization of productive capacity and working capital is possible only in the hands of committed, motivated employees. In particular, with latest trends in the Indian ITeS industry moving towards KPO, the role of employees in value creation has become even more vital. Actually, Indian ITeS firms must invest to a much greater extent in human and intellectual capital in order to optimize growth and value. The proposed model for enterprise value in the Indian ITeS industry concurs with the Beatty et al (1999), Liu et al (2002), and Deng et al (2009) approach considering weighted harmonic means of valuation ratios, i.e. using price-scaled regressions of a linear combination of value drivers. However, these approaches all started with the conventional earnings multiples, combining other multiples such as sales multiples. In contrast, the present study suggests a different approach to valuation, considering the triad of productive capacity, working capital, and human/intellectual capital. This approach is particularly suitable for the ITeS industry, due to the vital role of human and intellectual capital in the ITeS industry. There is scope to include more variables in the model, to study interaction of these aspects with other value drivers. Also, there is scope to study

its applicability in other industries, particularly such as the IT industry, as well as more generally for services. The study has some inherent limitations. The sample size used for the study was small, and pertained only to one year. Further, only a small set of determinant factors was considered for the analysis, and the model used was a linear regression model. Nevertheless, the results were quite significant, indicating that the results are reliable to a great extent. Another limitation is due to the use of historical data; subsequent trends in the industry, post-global financial meltdown, may not be reflected in the analysis.

Conclusion The ITeS sector in India has made substantial efforts towards the holistic development of the Indian economy and has made a positive impact on the society by generating employment and changing lives. Further, the industry has acted as socially responsible corporations playing an active role in regional development across India, empowerment of diverse human assets, driving technology and innovation to transform client businesses, and enhancing the overall brand image of India. As the business world continues to demand more and more immediate value from IT and progressive strategies that support growth and innovation, the players in the ITeS sector are banking more on innovation, corporate restructuring, renewed partnerships/alliances and new business models. The winning factor for every player in the Indian ITeS sector should be the subtle balance between the innovativeness in adopting technology and complementing it with the right human capital.

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