Investment decisions in the wireless industry applying real options

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Abstract

The wireless industry is one of the most capital intensive high-technology industries. This paper applies real options techniques to estimate investments under uncertainty in two new ventures: (a) deferral of the expansion from 2.5G to 3G networks; and (b) expansion of a 2.5G network using Wi-Fi as an alternative technology. The cases are examined and analyzed both qualitatively and quantitatively, using realistic assumptions and parameters. Investment cost, number of subscribers, pricing of services, and risk are at the core of investment decision processing. In both cases, sensitivity analysis of the value of the (real) option considering the above key parameters was conducted, to extrapolate useful findings that should be taken into consideration by the decision makers in wireless companies.

Introduction

The cellular industry has experienced unprecedented growth in the last 25 years and is still growing. In the United States, service providers migrated from AMPS (advance mobile phone system) to TDMA (time division multiple access) and CDMA (code division multiple access), which became the two most popular technological choices. In the meantime, GSM (global system for mobile communications—the European variant of second generation (2G) systems, became the most widely adopted mobile system in the world. 2G networks opened the door for offering new data products (e.g., browsing, email, and interconnection to private networks) and high-quality voice services.

With gradual improvements to 2G systems, two competing third generation technologies (3G) emerged: the UMTS (universal mobile telephone system) (3GPP, UMTS Forum, UMTS World) and the CDMA2000 (Code Division Multiple Access 2000) (3GPP2, CDG, Qualcomm). As 3G mobile systems, they both promised to offer improved voice and broadband access to users. The challenge of providing broadband Internet services and extending coverage areas, while improving quality of service, remains a challenge for operators even today.

Increasing demand for high-speed wireless data services, subscribers’ demand for integrated solutions (voice and data) and higher mobility, coupled with the operators’ need to improve on average revenue per user (ARPU) and provide high-quality wireless services, caused operators to migrate to 3G systems. These systems required additional spectra, resulting in severe competition among service providers. Spectrum auctions for 3G networks started to take place at the end of 2000. In Europe, the first spectrum auctions took place in the United Kingdom (Klemperer, 2002a). Various applications of policy attempted to maximize economic rents from bidders (network operators) and governments across Europe raised revenues ranging from 20 to 650 Euros per capita in Switzerland and the United Kingdom, respectively (Klemperer, 2002a). This prohibited operators from rolling out networks as scheduled. Despite the hype surrounding 3G, operators have not been able to develop a business model that will attract subscribers and garner high revenues. This is evident from the fact that, at the end of the third quarter of 2005, there were only 37.9 million UMTS subscribers in the world (GSM World).

Operators are faced with challenges regarding the cost of deploying new infrastructure. How and when should new applications that will enhance the market be released? Several studies (Klemperer, 2002b; Mansell, Samarajiva, & Mahan, 2002; Ure, 2002) provide enough pointers about what went wrong in European 3G auctions. Bidders knew about the risks involved in bidding for 3G licenses. It was also pointed out that the telecom managers overestimated (hence, overbid) the value of the 3G license (Klemperer, 2002b; Mansell et al., 2002; Ure, 2002). Furthermore, it was shown that the complexity of rules, opacity of information, lack of trust and understanding among strong bidders and concerns about stock market perceptions were some of the key contributors to the outcome of the European 3G auctions.

In the wireless industry, a high percentage of the invested capital goes to network infrastructure. Operators must evolve their infrastructure in a cost effective manner, while meeting forecasted demand due to subscriber growth.

In order for operators to gain markets, they need to exercise flexibility in investment decision making, while taking into consideration uncertainty, which is inherent in high-tech industries and modern capital-market economies. For example, an operator can choose a small scale deployment test to initially gauge the market perception and then either opt for large scale deployments (as encouraging information arrives from economy/markets), or abandon the project (if the investment turns out to be bad).

This paper applies real options methodology to the investment-making process of two wireless companies. The objective of this study is to highlight the strengths of applying the real options valuation approach as a decision-making tool in expanding or delaying 3G network deployments. Real option methods are in vogue because they provide more accurate valuations in different areas, e.g., equity valuation, mining projects, etc. (Alleman & Noam, 1999; Damodaran, 2002; Mun, 2002; Schwartz & Trigeorgis, 2001; Trigeorgis, 1996). The outcome of real options analysis is heavily dependent on the input parameters and the assumptions made, just as in traditional discounted cash flow (DCF) analysis. In this paper, the assumptions are realistic and based on norms widely accepted by the industry and conversations held with executives and technical staff.

The structure of the paper is as follows: in Section 2, the basic theoretical work of the investment decision process (discount cash flow analysis, financial and real options), as well as examples from the literature, where real options have been proposed for strategic and technical problems in the telecommunications industry, are reviewed. In Section 3, two hypothetical cases regarding the wireless industry are studied and real options theory is applied to both cases. Finally, Section 4 summarizes the results and concludes. It was found that investment cost and uncertainty about subscriber growth are the key parameters in the analysis. Furthermore, volatility plays a key role in the valuation of the two cases.

Section snippets

Background

Traditional DCF analysis values an investment in present value terms, assuming that future cash flows are known and discounted at a risk-adjusted factor, e.g., the weighted average cost of capital (WACC) of the company (Damodaran, 2002).

For example, consider a project that has a life of five years, and an initial investment cost K. Initial investments in projects generate cash flows during the project life cycles, which are discounted at a respective discount rate. To value an asset, the net

Two cases of real options in wireless industry

In the current section the strengths of the real options valuation approach as a decision-making tool are highlighted, by considering two cases of hypothetical wireless companies. Company A has the option to defer expansion of 2.5G to 3G wireless networks for a predetermined time period. Company B has the option to expand its 2.5G network using alternative technologies, such as WLANs. Several cellular service providers have shown interest in WLAN deployment, since they are hesitant to invest in

Discussion and conclusion

This research was motivated by the fact that the path to 3G is dependent not only on the fee paid to acquire the spectrum license, but on key factors such as infrastructure costs, number of subscribers, and uncertainty around the market conditions and regulatory policies in future years. Real options theory provides an appropriate framework to study investment decisions in the wireless industry.

Hypothetical cases regarding two different companies were presented and analyzed in the paper. In the

Acknowledgements

The authors would like to thank Prof. J. Alleman and Prof L. Trigeorgis for the fruitful discussions on real options. Dr. A. Curtis, Dr. K. Ryan and Dr. C. Smith, all at Stevens, for their valuable insights on technology and telecommunications management issues. Finally, the editors and anonymous reviewers as well as the participants at the following conferences, where the paper was presented at its earlier stages: 15th Biennial Conference of International Telecommunications Society (ITS 2004,

References (44)

  • J. Alleman

    A new view of telecommunications economics

    Telecommunication Policy

    (2002)
  • P. Klemperer

    How (not) to run auctions: The European 3G telecom auctions

    European Economic Review

    (2002)
  • 3rd Generation Partnership Project (3GPP),...
  • 3rd Generation Partnership Project 2 (3GPP2),...
  • J. Alleman et al.

    Real options: The new investment theory and its implications for telecommunications economics

    (1999)
  • J. Alleman et al.

    Modeling regulatory distortions with real options

    Engineering Economist

    (2002)
  • M. Amram et al.

    Real options: Managing strategic investment in an uncertain world

    (1999)
  • AT&T Wireless,...
  • Athwal, B., Harmantzis, F., & Tanguturi, V. P. (2005). Replacing centric voice services with hosted VoIP services—a...
  • M. Basili et al.

    The option value of the UK 3G telecom licenses

    Info: The Journal of Policy, Regulation and Strategy for Telecommunications

    (2003)
  • S. Benninga et al.

    Real options—an introduction and an application to R&D valuation

    Engineering Economist

    (2002)
  • F. Black et al.

    The pricing of options and corporate liabilities

    Journal of Political Economy

    (1973)
  • Boingo Wireless,...
  • E.H. Bowman et al.

    Real options analysis and strategic decision making

    Organization Science

    (2001)
  • CDMA Development Group (CDG), 〈www.cdg.org〉,...
  • Y. d’Halluin et al.

    Managing capacity for telecommunications network under uncertainty

    IEEE/ACM Transactions on Networking

    (2002)
  • d’Halluin, Y., Forsyth, A. P., & Vetzal, R. K. (2002b). Wireless network capacity investment. Working Paper, University...
  • A. Damodaran

    Investment valuation: Tools and techniques for determining the value of any asset

    (2002)
  • N. Economides

    Real options and the cost of the local telecommunications network

  • Edelmann, J., Kylaheiko, K., Laaksonen, P., & Sandstorm, J. (2002). Facing the future: Competitive situation in...
  • GSM World, The Website of the GSM Association,...
  • Harmantzis, F., & Tanguturi, V. P. (2004a). Delay in the expansion from 2.5G to 3G wireless networks: A real options...
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