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Risk and Investment in the Global Telecommunications Industry 39




Chapter III



Risk and
Investment
in the Global
Telecommunications
Industry
Irene Henriques
York University, Canada

Perry Sadorsky
York University, Canada




Abstract
In this chapter, quantitative modeling and simulation techniques are used to estimate
various risk measures and the associated cost of equity for the global telecommunications
industry. Our approach is to calculate several different cost-of-equity values and then
use simulation techniques to build up a probability distribution for each company™s
cost of equity. In this way, a clearer picture of where a company™s cost of equity lies is
developed. Closing the Digital Divide could bring many benefits to developing
countries but international investors and development planners must be able to make
their own cost-of-equity calculations so that they can see first hand how their
investment projects compare with other investment projects around the globe.




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40 Henriques & Sadorsky


Introduction
The new economy can be characterized in a number of different ways but one way to look
at the new economy is to identify industries that are undergoing the greatest amount of
structural change and have the greatest opportunity for growth. Three industries stand
out as having particularly promising futures: biotechnology, energy and information
technology (IT). Collectively these three industries may be called the BET economy. Of
these three industries, the IT industry (broadly comprised of the technology, media and
telecommunications (TMT) sub- industries) is the one industry that can contribute the
most to productivity improvements in countries. Technological progress can lead to
process innovation (lower cost ways of producing existing products) or product
innovation. Furthermore, from neoclassical growth theory, technological improvements
are the only way to increase the living standards in countries that have reached the
golden rule. An increase in technology raises the production function and increases the
steady state amounts of capital stock and output. In terms of economic performance,
maximizing productivity growth is the single most important objective for a country to
have since increases in productivity growth lead to higher living standards.
Productivity growth can be influenced by a number of different factors or drivers. Broadly
speaking, these factors include macroeconomic policy, regulatory environment, innova-
tion, industrial structure, human capital, management strategies and policies, trade, and
investment. For large industrialized countries like those in the G7 or G10, economic
performance depends in large part on coordinating the actions between these various
drivers to enhance productivity. The shortage of the necessary resources to accomplish
this objective is not that large of a problem. For developing countries, the situation is
often much more difficult because, in addition to successfully coordinating the actions
of the various drivers, developing countries also face a shortage of financial capital. As
a result, foreign investment is becoming an increasingly important driver behind
productivity growth in developing countries.
Business growth and the overall wealth generation process are hindered in developing
economies by the lack of affordable credit. This is particularly true in the IT industry. New
firms starting out in IT are often very small and face high start-up costs. These firms
usually have no source of financial capital to draw from, which means that they need to
seek external funding. In developing economies, the selection of financial instruments
available to start up companies is very limited (Chong and Micco, 2003). The source of
financial capital -where it exists- tends to be scarce and commands a very high price. This
problem is particularly acute in South America and Africa where domestic savings rates
are much lower than in other developing parts of the world. As a result, foreign investment
into developing countries can provide a much-needed source of financial capital.
Financial capital is probably the scarcest commodity in the world. More people demand
financial capital at any one time than are able to supply it. Financial capital is scarce,
mobile and very sensitive to economic and political conditions. Consequently, those
who have financial capital to invest are very selective about where they invest. Domestic
and global investors typically prefer investments with high returns and low risks. Risk
management is, therefore, an important component of global investing. Savings is the
only source of financial capital and the relationship between savings, investment and


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Risk and Investment in the Global Telecommunications Industry 41


economic growth has been well documented (Miles and Scott, 2002). The role that foreign
direct investment plays in aiding a country™s economic development has also been
extensively studied.
Foreign investment can be categorized as either foreign direct investment (FDI) or
foreign indirect investment (portfolio investment). FDI adds to the receiving country™s
GDP because it involves investment in physical capital (roads, buildings, plants,
machinery and equipment, etc.). Portfolio investment has a less direct impact on
economic growth when it involves the buying and selling of existing equities and bonds.
Of course, portfolio investment used to finance initial public offerings adds directly to
GDP.
There are a number of interesting trends regarding foreign investment (Miles and Scott,
2002). First, richer countries tend to invest more overseas. Second, FDI assets (liabilities)
as a percentage of industrialized countries GDP are 16% (17%). Thus, a lot of FDI is
conducted between the rich industrialized countries. Third, total outward investment by
the industrialized countries is currently around $1500 billion (US) of which one-third is
FDI and two-thirds is portfolio investment. By comparison, FDI in 1990 was $300 billion
(US) and this was approximately equal to portfolio investment. Portfolio investment is
currently the fastest growing part of foreign investment. Fourth, FDI tends to be less
volatile than portfolio investment.
Developing or emerging economies seeking foreign investment must be aware of these
trends and realize that, while FDI might be the preferred choice of foreign investment,
portfolio investment is a much larger pool of money to tap. Portfolio investment, however,
requires risk management on the part of both the seller and the buyer. Provided a
developing or emerging economy can offer attractive risk and return characteristics to
investors of financial capital, portfolio investment should not be overlooked as a source
of investment capital.
Consequently, it is necessary to have good measures of equity risk for managers,
planners and investors. The cost of equity is important in valuing new investment
opportunities and in evaluating the ongoing performance of established business
projects. This is especially true in the new economy IT industry where an understanding
of equity risk aids in the examination of the relationship between the IT sector and
economic development.
The purpose of this chapter is to calculate the cost of equity for the global telecommu-
nications industry using a sample of 26 firms in 19 countries. The companies in the sample
are chosen primarily based on their inclusion in the www.adr.com telecommunications
database and having a reasonable length of market data (five years). Quantitative
modeling and simulation techniques are used to estimate various risk measures and the
associated cost-of-equity values for the telecommunications industry in each country
in the sample. The methodology is similar to that used by Sadorsky (2003) and Sadorsky
and Henriques (2003). The risk measures include systematic risk (Brealey and Myers,
2003; Campbell, Lo and Mackinlay, 1997), total risk (Shapiro, 2003), downside risk
(Estrada, 2000, 2002; Harvey, 2000; Alexander, 2001), regret (Dembo and Freeman, 2001),
and value at risk (JP Morgan/Reuters, 1996). A brief discussion of each of these risk
measures is provided. The risk measures are then used to calculate the cost of equity
(which is equal to a risk-free rate plus the product of a risk measure and a market risk



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42 Henriques & Sadorsky


premium) in the global telecommunications industry for each company in the sample. For
each company, the different cost-of-equity values are compared and contrasted. More-
over, a comparison between different cost-of-equity values is made with companies in
similar regions of the world.




Background
The term Digital Divide usually refers to the overall gap in information technology and
communication usage between developed and developing countries (Lu, 2002). Cur-
rently there are huge differences in the ability of people around the world to communicate
both locally and globally. The International Telecommunications Union (ITU) provides
free statistics, available on their Web site, on IT usage rates in five broad regions of the
world. These regions are: Africa, Americas (including North, Central and South), Asia,
Europe, and Oceania. Total telephone subscribers per 100 inhabitants in 2002 ranged from
a low of 6.60 in Africa to a high of 89.83 in Europe (Table 1). The number for the Americas
(64.92), represents the average of high wire-line usage in North America and low wire-
line usage in South America. For example, Canada and the United States have 101.26 and
114.70 total telephone subscribers per 100 inhabitants while Peru and Venezuela have
13.67 and 36.78 total telephone subscribers per 100 inhabitants. Statistics on cellular


Table 1. IT usage statistics (2002)

Africa Americas Asia Europe Oceania World



Wireline

subscribers per 100 inhabitants 6.6 64.92 23.89 89.83 88.93 36.35



Cellular subscribers

as % of total telephone subscribers 61 45.8 50.3 55.1 54.6 51

% digital 86.3 53.5 80.2 55.4 80 65.8

subscribers per 100 inhabitants 4.19 29.74 12.19 50.21 48.53 18.77



Internet usage

users per 10,000 inhabitants 99.62 2421.02 557.56 2079 3330.47 972.16




Source: http://www.itu.int/ITU-D/ict/statistics/


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Risk and Investment in the Global Telecommunications Industry 43


subscribers in 2002 show a great deal of variation. Africa had the lowest number (4.19)
of cellular subscribers per 100 inhabitants while Europe had the highest (50.21) number
of cellular subscribers per 100 inhabitants. Similarly, 2002 statistics on Internet usage
also reveals vast differences between regions of the world. Africa only had 99.62 users
per 10,000 inhabitants while Oceania had 3,330.47 users per 10,000 inhabitants. The high
number of Internet users for Oceania is heavily influenced by the widespread usage of
the Internet in Australia and New Zealand. The Americas also have a high number of
Internet users and this number is heavily influenced by the large number of Internet users
in Bermuda, Canada and the United States. Collectively, the statistics reported in Table
1 indicate that IT communication tends to be the highest in the prosperous regions of
the world.
Access to affordable technology to improve the flow of information is crucial to the
development of an economy. Currently four-fifths of the world™s cellular subscribers live
in developed economies. The prospects for growth in cellular phone subscribers is
largest in the developing countries. For many large developing countries, like Russia, it
can take up to ten years to get a fixed line telephone (Economist, 1999). The problem, of
course, is that it doesn™t make economic sense to install wire-line services in regions of
the world where there are very few people. Cellular phone services provide an affordable
alternative to expensive wire-line telephone because cellular phone companies only need
to install transmission towers to send and receive signals and do not have to dig holes
in the ground. It is much cheaper and easier to install transmission towers. With lower
costs, cellular phone companies can break even with a small number of subscribers and
also more easily tailor cellular phone packages to regional tastes. Cellular phone
companies also bring much needed competition and foreign investment to the telecom-
munications industry in many parts of the world.
Internet usage is an important determinant in closing the Digital Divide (Jain, 2002).
Internet access brings many benefits to a country and its citizens. These benefits include
information on health and education, finding lower prices for goods and services,
increased business efficiency, the creation of new jobs, and increased trade. Here,
however, the problems (costs) associated with installing wire-lines re-surface. In North
America and Europe, two regions of the world with high Internet usage rates, the vast
majority of Internet traffic travels across wire-line. Wireless Internet is available but at
a higher cost. In developed countries, wireless products are seen as a luxury. In
developing countries, because of the high cost of laying fixed wire-line, wireless
technology is seen as more of a necessity. As a result, the demand for wireless products
in developing countries may just possibly drive technological innovations in wireless
products.
E-commerce is one area where the Digital Divide is extreme. The statistics are startling.
Currently, three-quarters of all e-commerce is done in the United States and 90% of all
commercial Web sites are located in the United States. There are vast opportunities
awaiting companies and countries that participate in the globalization of e-commerce
(Iyer, Taube and Raquet, 2002). Macroeconomic advantages include innovation (through
bigger rewards in a global market place and knowledge diffusion through the use of the
Internet), efficiency (by transparency through intermediaries and global availability of
information) and trade (via global reach to export goods and the opportunity to export
services). Microeconomic advantages of international e-commerce growth include


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44 Henriques & Sadorsky


service (with customized and integrated services worldwide and global knowledge about
consumer™s preferences), distribution (lower costs by bypassing retailers and global
reach through electronic channels) and costs (create new distribution channels through
electronic supply chain integration and global selection of suppliers). Clearly, consum-
ers and businesses in both developed and developing economies stand to gain from the
globalization of e-commerce.
Closing the Digital Divide could bring many benefits to developing countries. Bringing
the benefits of IT to developing counties is possible but the governments of these
countries need to be aware that the process is going to cost money and require
institutional changes. Bortolotti, D™Souza, Fantini and Megginson (2002) have com-
pleted a study in which they examined the financial and operating performance of 31
national telecommunications companies in 25 countries that were fully or partially
privatized through public share offerings. Firm profitability was measured in several
different ways including return on assets, returns on sales and return on equity. Their
results indicate that the financial and operating performance of telecommunication
companies significantly improved after privatization, but that regulatory changes also
played a major role.




Methodology
This section, which follows the methodology in Sadorsky and Henriques (2003) and
Sadorsky (2003), describes the empirical approach used to calculate the various risk
measures and associated required returns. The cost of equity for a firm is the minimum
rate of return required to induce investors to buy or hold the company™s stock. This
required rate of return consists of a risk-free rate (representing the time value of money)
and a premium for risk. Alternatively, the cost of equity is the rate used to capitalize
corporate cash flows. It can be used to measure the required rate of return of future equity
investments as long as the future investments are very similar to the current projects
being undertaken by the firm.
Any required rate of return on an equity investment consists of a risk-free rate and a risk
premium.


RRi = Rf + (RPM)(RMi) (1)


In equation (1), RRi is the required return on equity i (or alternatively, the cost of equity),
Rf is the risk-free rate, RPM is the market risk premium and RMi is a risk measure for equity i.
Several risk measures are considered and studied. Systematic risk (SR) is measured by
the capital asset pricing model (CAPM) beta (Brealey and Myers, 2003). Systematic risk
has a long history and is one of the most widely used measures of risk (Brealey and Myers,
2003; Campbell, Low and Mackinlay, 1997). Systematic risk calculated from the CAPM
also has its critics and Fama and French (1997) is one well-known example of this. Total
risk (TR) is measured by the standard deviation of stock returns. Total risk is also widely



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Risk and Investment in the Global Telecommunications Industry 45


used and is particularly appropriate in segmented markets (which describes the stock
markets in developing countries). Total risk, the combination of systematic and unsys-
tematic risk, is important to the value of the firm. Total risk may have a negative impact
on the firm™s expected cash flow because financial distress is most likely to occur for firms
with high total risk (Shapiro, 2003). Companies experiencing financial distress face
business uncertainty and this uncertainty imposes costs on consumers, suppliers and
employees. When a company is in financial distress, suppliers often charge higher prices
than normal. In addition, some customers become concerned about the long-term
commitment of the company and cancel orders. Consequently, high total risk is likely to
adversely affect a firm™s value via lower sales and higher costs.
Value at risk (VAR) is a well-known measure of the expected losses in extreme downturns
(Alexander, 2001; JP Morgan, 1996). VAR is the expected loss to be exceeded during a
particular time period with a specific probability. The greater the value of VAR, the greater
the potential loss within the defined time period for a particular confidence interval. It
is usual to assume a 95% confidence interval and a time period of one month. In this case,
VAR is the expected loss to be exceeded with a 5% probability during the next month.
For example, assume that the United States stock market (measured by the S&P 500) has
a VAR of $9. This means that for every $100 invested in the United States stock market,
there is a 5% chance of losing $9 or more in any given month. Equation (2) specifies the
VAR calculation.


VAR = (1 “ exp(-1.645σ ))*100 (2)


In equation (2), 1.645 is the critical value for a 95% confidence interval, assuming a normal
distribution for stock returns, and σ is the monthly standard deviation of the monthly
continuously compounded total returns.
Another perhaps lesser known measure of risk is downside risk. Downside risk (DR) is
measured using semi-standard deviation of returns.


T
Σ B = (1 / T )‘ ( Rt ’ B) 2 for all Rt < B (3)
t =1



In equation (3), Rt are monthly returns and B is the benchmark. There are a number of
different benchmark returns and consequently several different measures of downside
risk. A popular choice for B is the arithmetic mean (µ) of the continuously compounded
monthly returns. In this case, equation (3) reads Σ µ. Other measures for the benchmark
include the risk free rate (f) and zero (0) (Estrada, 2000; Harvey, 2000). Downside risk was
discussed by Markowitz (1959) who recognized that investors have asymmetric prefer-
ences towards risk. Most investors like upside risk, but dislike downside risk. Investors
are interested in minimizing risk for two reasons. First, only downside risk or safety first
is relevant to an investor. The idea of safety of principal can be traced to Roy (1952) who
proposed that investors prefer safety of principal first and will set some minimum


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46 Henriques & Sadorsky


acceptable return that will protect this principal. Second, asset returns may be non-
normally distributed. A downside risk measure is an appropriate risk measure to use when
asset returns are non-normal. Nawrocki (1999) and Sortino and Satchell (2001) provide
a good review of downside risk and Bernstein (1998) provides a good historical
perspective of risk.
Recently, Dembo and Freeman (2001) have introduced regret as a measure of downside
risk


Regret = -E(min(0,R-B)) (4)


where E is the mathematical expectations operator, R is return and B is the benchmark.
As in equation (3), the benchmark, B, can be time varying or fixed. Notice that regret has
the same form as the pay-off function to a put option with a strike price equal to the
benchmark return. In this chapter, regret is measured using a risk-free benchmark (f) and
the zero benchmark (0).




Data
The data used in this study consist of monthly data on 26 telecommunications compa-
nies™ total returns (price returns plus dividends) over the period from January of 1997 to
December of 2002. The total return series are expressed in American dollars. The data were
collected from the Center for Research in Security Prices (CRSP) database. Total returns
on the value weighted U.S. market portfolios of NYSE, AMEX and NASDAQ stocks are
also included in the data set. The companies were selected on the basis of their inclusion
in the www.adr.com telecommunications database and a continuous set of stock price
data over the period from January of 1997 to December of 2002.
American depository receipts (ADR) is a name for foreign company shares that trade on
a local stock exchange and have receipts that trade on a U.S. exchange like the New York
Stock Exchange (NYSE). ADRs are an easy way for U.S. investors, or international
investors with a U.S. trading account, to buy the shares of foreign companies. ADRs were
first introduced in 1927 by Morgan Guaranty. Currently approximately 70% of the ADRs
(330 companies) trade on the NYSE. To round out the sample and for comparison
purposes, three U.S. companies and one Canadian company were included in the data
set. All companies in our sample trade on NYSE.




Risk Measures
Monthly summary statistics for the 26 global telecommunications companies are re-
ported in Table 2. Mean monthly continuously compounded returns are very small and



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Risk and Investment in the Global Telecommunications Industry 47


Table 2. Summary statistics

Company Country mean stdev Sharpe ratio rho beta skewness kurtosis



APT Satellite Holdings Ltd. Hong Kong 0.00 0.26 -0.06 0.39 1.76 2.14 7.78

Asia Satellite Telecom Hong Kong 0.01 0.15 0.11 0.62 1.63 0.19 -0.40

BCE Inc. Canada 0.02 0.10 0.70 0.53 0.94 0.39 0.72

BT Group PLC U.K. 0.00 0.11 -0.20 0.59 1.18 -0.21 -0.59

Cable & Wireless U.K. -0.03 0.16 -0.63 0.56 1.55 -0.35 0.38

China Mobile Ltd China 0.02 0.17 0.34 0.61 1.78 1.06 1.44

Deutsche Telekom AG Germany 0.01 0.15 0.09 0.54 1.40 0.34 0.00

France Telecom France 0.01 0.21 0.09 0.47 1.71 0.59 2.00

Indonesian Satellite Corp Indonesia 0.02 0.21 0.22 0.35 1.28 1.87 6.60

Nippon Telegraph & Telephone Japan -0.01 0.11 -0.40 0.47 0.90 0.65 0.11

Nokia OYJ Finland 0.04 0.17 0.64 0.65 1.94 0.07 -0.29

Philippine Long Distance Philippines -0.02 0.13 -0.54 0.51 1.16 0.55 0.33

Portugal Telecom Portugal 0.00 0.11 -0.03 0.48 0.95 0.10 0.57

SK Telecom Korea 0.04 0.21 0.54 0.47 1.67 1.64 3.58

TDC A/S Denmark 0.01 0.13 0.05 0.49 1.11 0.26 1.99

Telecom Argentina Argentina -0.02 0.22 -0.31 0.44 1.65 0.46 1.02

Telecom Italia Italy 0.01 0.10 0.18 0.46 0.79 0.51 0.52

Telefonica de Argentina Argentina -0.02 0.20 -0.39 0.43 1.47 0.83 2.80

Telefonica del Peru Peru -0.03 0.15 -0.89 0.54 1.38 0.47 1.21

Telefonica SA Spain 0.01 0.12 0.07 0.61 1.24 0.49 0.61

Telefonos de Mexico Mexico 0.02 0.11 0.63 0.66 1.21 -0.04 0.47

Telekomunidasi Indonesia Indonesia 0.02 0.20 0.29 0.46 1.59 0.92 2.05

Vodafone U.K. 0.01 0.11 0.24 0.47 0.91 0.01 -0.65

Nextel U.S. 0.03 0.24 0.32 0.54 2.19 0.50 0.64

AT&T U.S. -0.01 0.12 -0.25 0.45 0.96 0.62 0.85

Verizon U.S. 0.01 0.10 0.13 0.31 0.55 1.10 2.39

Average 0.01 0.16 0.03 0.50 1.34 0.58 1.39

US Market 0.00 0.06 -0.15 1.00 1.00 -0.46 -0.46



Notes: Means (rates not percentages), standard deviations, rho, skewness, and kurtosis
reported for monthly stock return values. Sharpe ratios calculated for annual values.



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48 Henriques & Sadorsky


range from a low of -3.00% to a high of 4.00%. The sample average for the mean monthly
return was 1.00% and the mean monthly return on the U.S. market index was 0.00%. The
five-year period from January of 1997 to December of 2002 was one characterized by no
capital appreciation in the broad-based stock market. Most of the stock returns had some
evidence of either skewness or kurtosis. Most of the companies in the sample had high
standard deviations and, as a result, the values for the Sharpe ratios are low. Risk-averse
individuals prefer high values of the Sharpe ratio. BCE had the highest value of the Sharpe
ratio in the sample (0.70) and this value was 23 times larger than the Sharpe ratio for the
sample company average. On a risk-adjusted basis, BCE was a better investment than the
value-weighted U.S. market. In comparison, Telefonica del Peru, with a Sharpe ratio of
-0.89, was a particularly risky investment. The variable rho measures the correlation
between monthly company total returns and monthly U.S. market total returns. All of the
rho values are positive indicating that each company™s stock returns are positively
correlated with the broad based U.S. market. U.S. companies do not necessarily have the
highest correlation with the U.S. market. For example, Telefonos de Mexico had the
highest correlation with the U.S market while Verizon had the lowest correlation. This
result is useful to investors interested in building a portfolio of global telecommunica-
tions stocks.
The second column in Table 3 reports company beta values. Company beta values are
calculated from a single factor market model. More specifically,


Rit = ±i + βi Rmt + uit (5)


where Rit is the company return, R mt is the return on the U.S. stock market index, and uit
is the error term. Systematic risk (SR) is measured by the beta, βi, and idiosyncratic risk
^
(IR) is measured by the standard deviation of the residuals uit .
Many of the company betas are larger than unity (Table 3). Company beta values show
considerable variation and range from a low of 0.55 for Verizon to a high of 2.19 for Nextel.
Notice that in this sample, two U.S. companies have the lowest and highest values for
systematic risk. Based on these risk measures, Verizon stock is much less risky than the
U.S. market while Nextel stock is much more risky than the U.S. stock market. The
systematic risk values indicate that there is a wide variation in the risk of these
telecommunications companies. The company average systematic risk measure is 34%
larger than the systematic risk value for the U.S. stock market.
As discussed in Harvey (2000) and Estrada (2002), the single factor model in equation
(5) can also be used to calculate two downside beta measures. The first measure,
downside beta 1 (DB1), is calculated as the coefficient on the market return using
observations when company returns and U.S. market returns are both negative. The
second measure, downside beta 2 (DB2), is calculated as the coefficient on the market
returns when U.S. market returns are negative.
For each company, ten risk variables are calculated (Table 3). The risk variables are
systematic risk (SR) measured by beta, total risk (TR) measured by the standard deviation
of returns, value at risk (VAR), downside risk (DR) measured by the semi-standard


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Risk and Investment in the Global Telecommunications Industry 49


Table 3. Risk measures

Σf Σ0
Σµ
Company SR TR VAR DB1 DB2 REGf REG0




APT Satellite Holdings Ltd. 1.76 0.26 -35.00 0.13 0.16 0.13 2.33 2.74 0.12 0.08

Asia Satellite Telecom 1.63 0.15 -22.20 0.10 0.12 0.10 2.02 2.35 0.08 0.06

BCE Inc. 0.94 0.10 -15.33 0.07 0.08 0.05 1.04 1.38 0.05 0.03

BT Group PLC 1.18 0.11 -17.19 0.08 0.11 0.08 1.57 1.94 0.07 0.05

Cable & Wireless 1.55 0.16 -22.99 0.12 0.16 0.13 2.14 2.28 0.10 0.07

China Mobile Ltd 1.78 0.17 -24.19 0.10 0.11 0.09 1.59 2.49 0.08 0.05

Deutsche Telekom AG 1.40 0.15 -21.63 0.10 0.12 0.09 1.63 2.03 0.08 0.06

France Telecom 1.71 0.21 -29.24 0.14 0.16 0.13 2.64 2.12 0.09 0.07

Indonesian Satellite Corp 1.28 0.21 -29.41 0.12 0.13 0.11 2.27 1.82 0.09 0.06

Nippon Telegraph & Telephone 0.90 0.11 -16.74 0.07 0.10 0.08 1.05 1.25 0.08 0.05

Nokia OYJ 1.94 0.17 -24.51 0.12 0.12 0.10 1.70 2.51 0.07 0.05

Philippine Long Distance 1.16 0.13 -19.42 0.08 0.12 0.10 1.52 1.60 0.09 0.06

Portugal Telecom 0.95 0.11 -17.17 0.08 0.10 0.08 1.45 1.55 0.07 0.04

SK Telecom 1.67 0.21 -28.64 0.11 0.11 0.09 1.44 2.35 0.08 0.05

TDC A/S 1.11 0.13 -19.19 0.09 0.11 0.08 1.96 1.62 0.07 0.04

Telecom Argentina 1.65 0.22 -30.12 0.15 0.18 0.15 2.12 2.28 0.11 0.09

Telecom Italia 0.79 0.10 -15.06 0.06 0.08 0.06 0.97 1.22 0.06 0.03

Telefonica de Argentina 1.47 0.20 -27.75 0.13 0.16 0.14 1.96 2.07 0.11 0.08

Telefonica del Peru 1.38 0.15 -21.34 0.10 0.14 0.12 2.21 2.41 0.10 0.07

Telefonica SA 1.24 0.12 -17.55 0.08 0.10 0.07 1.41 1.83 0.06 0.04

Telefonos de Mexico 1.21 0.11 -15.99 0.07 0.08 0.06 1.16 1.80 0.05 0.03

Telekomunidasi Indonesia 1.59 0.20 -27.95 0.12 0.13 0.11 1.89 2.37 0.09 0.06

Vodafone 0.91 0.11 -16.76 0.08 0.10 0.07 1.25 1.32 0.06 0.04

Nextel 2.19 0.24 -32.13 0.16 0.17 0.14 2.55 3.07 0.10 0.08

AT&T 0.96 0.12 -18.29 0.08 0.11 0.08 1.09 1.38 0.08 0.05

Verizon 0.55 0.10 -15.68 0.06 0.08 0.06 0.69 0.65 0.06 0.03

Average 1.34 0.16 -22.36 0.10 0.12 0.10 1.68 1.94 0.08 0.06

US Market 1.00 0.06 -9.04 0.04 0.07 0.04 0.98 1.47 0.05 0.02




deviation of returns, using three different benchmarks, two downside beta measures, and
two measures of regret.
The TR values range from a low of 0.10 to a high of 0.26. The sample average for TR is
almost three times larger than the TR for the U.S. market.
The -35.00 VAR value for APT Satellite Holdings means that for every $100 invested in
this company there is a 5% probability of losing $35.00 or more in any given month. Based
on the VAR measure, this company is very risky to invest in. Telecommunications
companies are, as a group, very risky and this is clearly demonstrated by the average VAR
value of -22.36. By comparison, the U.S. stock market has a VAR value at -9.04 and this


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50 Henriques & Sadorsky


value is well above the telecommunications industry average. The average downside risk
measures are each larger than twice the corresponding values for the U.S. market (Table
3). The average values for downside beta 1 (DB1) and downside beta 2 (DB2) are each
larger than the average value for systematic risk. The results in Tables 3 show that the
global telecommunications industry is a very risky industry to invest in.
A correlation matrix between mean stock returns and the ten risk variables indicate that
company stock returns are most highly correlated with the two measures of regret (Table
4). Market returns are the least correlated with systematic Σµ. Total risk and downside
risk are very highly correlated with each other and downside risk, and VAR are very highly
correlated with each other. Total risk is perfectly correlated with VAR. The risk measures,
downside beta 1 and downside beta 2, are each less highly correlated with market returns
than is systematic risk.
A cross section regression analysis is used to investigate the relationship between mean
returns and various risk variables (Table 5). The adjusted R2 values for these regression
models range from -0.03% to 20%. The probability value associated with the estimated
coefficient on the risk variable indicates that each of the risk variables (Σf, Σ0, REGf, and
REG0) is statistically significant. None of the regression models exhibit evidence of mis-
specification as evident from the RESET test. These results provide evidence of a linear
relationship between market returns and these four risk variables.




Table 4. Correlations

Σf Σ0
Σµ
MEAN SR TR VAR DB1 DB2 REGf REG0

MEAN 1.00 0.21 0.10 -0.09 0.01 -0.37 -0.36 -0.17 0.11 -0.48 -0.42

SR 0.21 1.00 0.82 -0.82 0.86 0.73 0.74 0.77 0.96 0.60 0.70

TR 0.10 0.82 1.00 -1.00 0.93 0.83 0.84 0.80 0.79 0.80 0.81

VAR -0.09 -0.82 -1.00 1.00 -0.93 -0.83 -0.85 -0.80 -0.78 -0.81 -0.82

Σµ 0.01 0.86 0.93 -0.93 1.00 0.92 0.92 0.87 0.81 0.78 0.87


Σf -0.37 0.73 0.83 -0.83 0.92 1.00 0.99 0.88 0.72 0.91 0.97


Σ0 -0.36 0.74 0.84 -0.85 0.92 0.99 1.00 0.87 0.73 0.93 0.97

DB1 -0.17 0.77 0.80 -0.80 0.87 0.88 0.87 1.00 0.78 0.75 0.82

DB2 0.11 0.96 0.79 -0.78 0.81 0.72 0.73 0.78 1.00 0.63 0.71

REGF -0.48 0.60 0.80 -0.81 0.78 0.91 0.93 0.75 0.63 1.00 0.95

REG0 -0.42 0.70 0.81 -0.82 0.87 0.97 0.97 0.82 0.71 0.95 1.00




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Risk and Investment in the Global Telecommunications Industry 51


Several risk measures (REGf, REG0) exhibit a high degree of explanatory power as
indicated by the high R2 values. These R2 values range from 18% to 23% and there is no
evidence of mis-specified functional form. Notice how little of the variability in market
returns can be explained by systematic risk.




Table 5. Cross section regression analysis

c1 c2 R squared R squared adj RESET

SR -0.01 0.01 0.05 0.01 0.21
0.41 0.21

TR 0.00 0.04 0.01 -0.03 0.26
0.96 0.59

VAR 0.00 0.00 0.01 -0.03 0.15
0.93 0.61

Σµ 0.00 0.00 0.00 -0.04 0.95
0.69 0.97

Σf 0.03 -0.22 0.13 0.10 0.99
0.02 0.05

Σ0 0.03 -0.23 0.13 0.09 0.77
0.01 0.05

DB1 0.01 -0.01 0.03 -0.01 0.77
0.16 0.38

DB2 0.00 0.00 0.01 -0.03 0.57
0.87 0.56

REGf 0.04 -0.46 0.23 0.20 0.93
0.00 0.00

REG0 0.03 -0.44 0.18 0.14 0.93
0.00 0.02


Regression equation is: MRi = c1 + c2 RVi + ui


Notes: MR is the mean stock return and RV is the risk variable. White™s (1980)
heteroskedasticity corrected probability values are reported below coefficient estimates.
RESET shows the probability values for a Ramsey (1969) regression specification F test
with 2 and 22 degrees of freedom.



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52 Henriques & Sadorsky


The Cost of Equity
The cost of equity is important in valuing new investment opportunities and evaluating
the ongoing performance of existing business projects. From equation (1) any required
return consists of a risk-free rate and a risk premium.


RRi = Rf + (RPM)(RMi) (6)


In equation (6), RRi is the required return on equity i, Rf is the risk-free rate, RPM is the
U.S. market risk premium, and RMi is a risk measure for equity i. This section focuses on
ten risk measures based on systematic risk (RMSR), total risk (RMTR), value at risk
(RMVAR), three measures of downside risk (RMDR), two measures of downside beta, and
two measures of regret. The appendix contains the equations for the cost of equity
calculations.
In order to calculate the cost of equity for each company, numerical values for a risk-free
rate and market-risk premium are required. In the calculations of the cost of equity, a risk
free rate of 1.19% was used as this was the value of the three-month U.S. Treasury bill
rate at the end of December 2002. A market risk premium of 4.0% was employed. With
globalization and capital market convergence, the U.S. market-risk premium may by the
best proxy for the global market risk premium (Pettit, Gulic and Park, 2001). Estimates of
the U.S. market-risk premium range from 3% to 8%. In this chapter, we choose a market-
risk premium of 4%. This value is slightly higher than the 3% market-risk premium now
being advocated (Tully, 2003), but 3% is also lower than the 5.5% global market-risk
premium previously used by other authors (Estrada, 2000, 2002).
For almost all companies, the cost of equity based on systematic risk is the lowest of the
cost-of-equity measures (Table 6). The cost of equity based on systematic risk varies
from a low of 3.41% (Verizon) to a high of 9.95% (Nextel). Telecom Italia has the lowest
cost of equity using total risk or value at risk. APT Satellite Holdings has the highest cost
of equity using total risk or value at risk. For most companies, the cost of equity ranking
is CETR > CEDRj > CESR (j = µ, 0, f). In some cases CEDRj > CEVAR and, in other cases, the
ranking is reversed. The differences between these risk measures can be very large. For
example, Telefonica de Argentina has a CESR of 7.08% per year while CETR and CEDR0 are
14.92% and 14.25% per year respectively. This suggests that an investor in Telefonica
de Argentina stock would be expecting to earn 7.08% per year if the cost of equity was
calculated using systematic risk. By comparison, this investor would expect to earn
14.92% per year if the cost of equity was calculated using a total risk measure. In other
words, there is a substantial difference in risk-adjusted expected returns. The cost of
equity sample averages for the telecommunications companies are 6.56% for systematic
risk, 12.00% for total risk, 10.49% for downside risk (CEDRµ) and 11.09% for value at risk.
These cost of equity measures are much higher than the corresponding values for the
U.S. stock market as a whole. The cost of equity calculated using downside beta measures
are almost always greater than the cost of equity calculated using systematic risk. On
average, the cost of equity calculated using downside beta 1 (beta 2) is 21% (36%) larger
than the average cost of equity calculated using systematic risk.


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Risk and Investment in the Global Telecommunications Industry 53


Table 6. Cost of equity

CEDR¬
Company CESR CETR CEVAR CEDRf CEDR0 CEDB1 CE DB2 CE REGf CE REG0 AVERAGE




APT Satellite Holdings Ltd. 8.23 19.38 16.68 13.55 10.65 13.77 10.50 12.15 11.03 15.23 13.12

Asia Satellite Telecom 7.72 11.79 11.02 10.84 8.46 10.48 9.27 10.59 8.03 10.92 9.91

BCE Inc. 4.95 8.22 7.97 7.52 5.76 6.38 5.34 6.71 5.28 5.73 6.39

BT Group PLC 5.93 9.15 8.80 8.97 7.63 9.23 7.47 8.94 7.23 9.08 8.24

Cable & Wireless 7.38 12.23 11.37 12.13 10.34 13.59 9.74 10.32 9.54 13.59 11.02

China Mobile Ltd 8.32 12.89 11.90 10.51 7.84 9.45 7.56 11.16 7.63 9.77 9.70

Deutsche Telekom AG 6.79 11.48 10.76 10.47 8.25 10.18 7.71 9.29 7.80 10.39 9.31

France Telecom 8.02 15.80 14.14 14.03 10.34 13.73 11.75 9.67 9.18 12.87 11.95

Indonesian Satellite Corp 6.32 15.90 14.21 12.03 8.90 11.21 10.25 8.45 8.49 11.36 10.71

Nippon Telegraph & Telephone 4.79 8.93 8.60 7.72 7.31 8.37 5.39 6.17 7.70 9.36 7.43

Nokia OYJ 8.94 13.07 12.04 12.25 8.33 10.55 7.99 11.21 7.24 9.65 10.13

Philippine Long Distance 5.84 10.31 9.79 9.13 8.39 10.22 7.28 7.61 8.52 11.11 8.82

Portugal Telecom 4.97 9.14 8.79 8.59 7.20 8.55 6.99 7.40 6.86 8.19 7.67

SK Telecom 7.87 15.44 13.87 11.59 7.94 9.58 6.96 10.58 7.82 9.99 10.16

TDC A/S 5.61 10.19 9.69 9.31 7.52 9.15 9.05 7.69 7.05 8.44 8.37

Telecom Argentina 7.80 16.33 14.52 14.78 11.73 15.80 9.67 10.29 10.92 15.91 12.77

Telecom Italia 4.36 8.08 7.86 7.20 6.18 6.82 5.08 6.08 6.18 6.66 6.45

Telefonica de Argentina 7.08 14.92 13.48 13.07 10.82 14.25 9.02 9.46 10.43 14.87 11.74

Telefonica del Peru 6.70 11.33 10.64 10.27 9.70 12.30 10.05 10.84 9.89 13.44 10.52

Telefonica SA 6.17 9.34 8.96 8.35 6.94 8.14 6.83 8.52 6.63 7.80 7.77

Telefonos de Mexico 6.01 8.55 8.27 8.06 6.12 7.06 5.84 8.39 5.45 6.01 6.98

Telekomunidasi Indonesia 7.57 15.03 13.56 12.46 9.04 11.45 8.75 10.68 8.56 11.64 10.88

Vodafone 4.83 8.94 8.61 8.49 6.85 7.97 6.18 6.46 6.44 7.84 7.26

Nextel 9.95 17.56 15.41 15.88 10.96 14.67 11.38 13.47 9.67 14.51 13.35

AT&T 5.04 9.72 9.28 8.55 7.59 8.95 5.55 6.71 7.66 9.53 7.86

Verizon 3.41 8.39 8.13 6.92 6.14 6.56 3.95 3.78 6.44 6.79 6.05

Average 6.56 12.00 11.09 10.49 8.34 10.32 7.91 8.95 7.99 10.41 9.41

US Market 5.19 5.19 5.19 5.19 5.19 5.19 5.12 7.09 5.19 5.19 5.37




While systematic risk is a very well known and widely used measure of market risk, the
results in this chapter suggest that a prudent investor in global telecommunications
companies might also wish to calculate risk measures based on total risk, value at risk,
downside risk, and regret. Unfortunately there is no simple answer to the question,
“which is the appropriate risk measure to use?” nor is the use of downside risk without
its™ critics (Nawrocki, 1999). Different individuals have different attitudes and prefer-
ences towards risk (Bernstein, 1998). One possible approach would be to use the simple


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54 Henriques & Sadorsky


average value of the ten cost-of-equity calculations. The average value for each company
is presented in the last column of Table 6. These cost-of-equity average values range from
a low of 6.05 for Verizon to a high of 13.35 for Nextel. The South American and Indonesian
companies have fairly large values for the average cost of equity. Contrary to what one
might expect, the average cost of equity for telecommunications companies in develop-
ing countries is not always greater than the average cost of equity for telecommunica-
tions companies in developed countries. This is borne out by the high average cost-of-
equity values for Cable & Wireless, France Telecom and Nextel. In general, it is difficult
to find evidence of regional differences in the average cost of equity of telecommunica-
tions companies.
In order to more fully develop the usage of the cost-of-equity values (Table 6) and risk
measures (Table 3), we perform two simulation experiments to build distributions for each
company™s cost of equity. In the first simulation, we assume that for each company in
our sample, the cost of equity can be approximated by a normal distribution with a mean
and standard deviation calculated from that company™s ten cost-of-equity values. A
number of different distribution functions (exponential, extreme value, normal, logistic,
triangular, Weibull, uniform and Pareto) was fit to each company™s cost-of-equity values.
For each company, the normal distribution ranked high (usually in the top four) for
chosen fit. For each company, 5,000 simulations were performed and the minimum, mean,
maximum, 5% value, and 95% values recorded. The results are reported in Table 7.
These results show not only the average cost-of-equity value for each company, but also
show the 90% confidence interval. For example, the average cost-of-equity value for
Telecomm Argentina is 12.78%. There is one chance in 20 that the cost of equity will be
below 8.01%. There is one chance in 20 that the cost of equity will be above 17.53%. Thus
we are 90% certain that the true cost of equity will be in the 8.01% to 17.53% range.
Simulation two is more elaborate in that the uncertainty is modeled directly into the
components of equation (6). A triangular distribution function with parameters 0.5, 1.19
and 2.25 was assumed for the risk-free rate. A normal distribution with mean 4 and
standard deviation 1 was assumed for the market-risk premium. A normal distribution was
assumed for each company™s risk measures using company sample values for the mean
and standard deviation. For each company, 5,000 simulations were performed and the
minimum, mean, maximum, 5% value, and 95% values recorded. The results are shown in
Table 8.
The mean values recorded in Tables 7 and 8 are for each company, fairly similar. The 5%
and 95% values for each company vary between the two tables and in some cases the
90% confidence interval is larger in Table 8 than it is in Table 7. The results in Table 8
are more precise because the randomness was modeled directly into the components of
equation (6). Our suggestion is therefore to not rely on just one cost-of-equity value but
to calculate several different cost-of-equity values and then use simulation techniques
to build up a probability distribution for each company™s cost of equity. In this way, a
clearer picture of where a company™s cost of equity lies is developed. After all, two
different distributions can have the same mean values but have very different shapes.
For project evaluation and investing, for example, distributions that are skewed to the
right are much preferred to distributions that are skewed to the left.




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Risk and Investment in the Global Telecommunications Industry 55


Table 7. Cost of equity (simulation 1)

Co mpany M inim um M ean M aximum 5% 95%



APT Satellite H oldings Ltd. -0.59 13.12 24.42 7.94 18.29

A sia Satellite Telecom 4.35 9.91 14.73 7.69 12.14

B C E Inc. 1.95 6.39 10.77 4.55 8.22

BT G roup PLC 4.17 8.24 12.34 6.50 9.98

Cable & W ireless 4.22 11.02 17.99 7.98 14.05

China M obile Ltd 2.80 9.70 16.21 6.76 12.65

D eutsche T eleko m AG 3.98 9.31 14.77 6.85 11.77

France Telecom 2.99 11.95 21.89 7.96 15.94

Indonesian Satellite Corp -0.49 10.71 21.40 6.22 15.19

N ippon T elegraph & T elephone 1.81 7.43 12.67 5.04 9.82

N okia O Y J 2.91 10.13 16.92 7.01 13.25

Philippine Long D istance 3.24 8.82 14.85 6.30 11.33

Portugal Telecom 3.43 7.67 12.07 5.72 9.61

SK T elecom 0.77 10.16 19.75 5.81 14.52

T DC A/S 3.06 8.37 13.43 6.19 10.55

T elecom Argentina 2.13 12.78 24.19 8.01 17.53

T elecom Italia 2.57 6.45 10.46 4.65 8.25

T elefonica de Argentina -0.78 11.74 22.80 7.45 16.03

T elefonica del P eru 3.50 10.52 17.08 7.74 13.28

T elefonica SA 3.96 7.77 11.93 6.09 9.44

T elefonos de M exico 2.73 6.98 11.22 5.06 8.89

T elekomunidasi Indonesia 2.18 10.88 19.12 7.12 14.63

V odafone 2.58 7.26 12.08 5.21 9.31

N extel 3.94 13.35 24.45 9.12 17.58

AT &T 1.66 7.86 13.54 5.27 10.44

V erizon -0.68 6.05 12.46 3.29 8.80



Notes: For each company 5,000 simulations were calculated from a normal distribution
using that company™s cost of equity mean and standard deviation.


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56 Henriques & Sadorsky


Table 8. Cost of equity (simulation 2)
Company Minimum Mean Maximum 5% 95%



APT Satellite Holdings Ltd. -0.14 12.88 30.40 6.27 20.66

Asia Satellite Telecom 2.50 9.74 19.35 6.00 13.86

BCE Inc. 1.53 6.34 12.75 3.83 9.24

BT Group PLC 1.14 8.13 15.08 5.18 11.22

Cable & W ireless 1.18 10.86 22.50 6.52 15.69

China Mobile Ltd 1.93 9.52 21.90 5.36 14.32

Deutsche Telekom AG 2.20 9.19 19.68 5.51 13.43

France Telecom 1.96 11.82 25.80 6.74 17.91

Indonesian Satellite Corp 0.55 10.61 29.02 5.12 17.04

Nippon Telegraph & Telephone 1.67 7.40 15.67 4.50 10.70

Nokia OYJ 1.82 9.94 22.00 5.79 14.67

Philippine Long Distance 0.92 8.74 18.37 5.23 12.68

Portugal Telecom 1.73 7.60 14.68 4.72 10.75

SK Telecom -1.01 10.00 33.01 4.62 16.39

TDC A/S 2.09 8.30 17.27 5.09 11.87

Telecom Argentina 2.37 12.62 27.30 7.38 18.68

Telecom Italia 2.01 6.42 14.23 3.99 9.20

Telefonica de Argentina 1.83 11.61 25.74 6.79 17.15

Telefonica del Peru 1.80 10.34 22.43 6.01 15.29

Telefonica SA 1.40 7.66 14.76 4.72 10.95

Telefonos de Mexico 1.50 6.88 13.63 4.21 9.88

Telekomunidasi Indonesia 1.38 10.71 27.16 5.83 16.77

Vodafone 0.49 7.23 16.25 4.40 10.41

Nextel 1.91 13.10 30.72 7.36 19.85

AT&T 1.42 7.82 15.75 4.78 11.21

Verizon 1.37 6.10 14.42 3.46 9.12


Notes: 5,000 simulations were performed for each company in the table. A triangular
distribution function with parameters 0.5, 1.19 and 2.25 was assumed for the risk free
rate. A normal distribution with mean 4 and standard deviation 1 was assumed for the
market risk premium. A normal distribution was assumed for each company™s risk
measure using company sample values for the mean and standard deviation.



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Risk and Investment in the Global Telecommunications Industry 57


Future Trends
Countries around the world are experiencing an increase in the convergence of global-
ization, financial markets and technology. Economic growth is tied closely with this
convergence and it is expected that this convergence will continue into the foreseeable
future. This convergence comes at a time when equity market volatility is high, thereby
emphasizing the need for the practice of good risk management techniques. Policy makers
in both developed and developing counties realize that economic growth is driven by
enhancements in productivity and productivity growth, in turn, is driven by increases
in technology. Affordable and reliable telecommunications are essential to the wealth
and creation process of any country (Landes, 1998). Privatization and favourable
regulatory changes in the global telecommunications industry can lead to greater
productivity and profits. Privatization also requires well functioning capital markets, as
new sources of debt and equity are required for those companies in industries being
privatized.
The telecommunications industry is characterized by high fixed costs, increased compe-
tition due to privatization and huge opportunities for growth in developing countries.
As in all industries, companies can compete on price or product differentiation (including
quality). In some regions of the world, it is just too costly to install wire-line services.
In these instances, wireless services present a viable alternative. The build-out of new
telecommunications services requires financial capital. Globalization (broadly character-
ized as in increase in the trade of goods and services and an increase in foreign
investment) can help by increasingly matching owners of scarce financial capital with
those who need it. But foreign investment comes at a price. Namely, that investors
compare various investment projects in a particular risk class and choose the one with
the highest return. Telecommunications companies in developing countries can attract
foreign capital if they offer a competitive risk-adjusted expected return.
Equity investors, in particular, because they rank after debt holders for claims on
company assets, must have good measures of risk-adjusted, expected returns. In the
future, investors and policy makers alike should make the cost-of-equity calculations
made in this chapter to help gain a better understanding of the tradeoffs between risk and
return in the global telecommunications industry.




Conclusions
Access to affordable technology to improve the flow of information is essential to the
development of an economy. Closing the Digital Divide could bring many benefits to
developing countries. In many ways, developing countries have the most to gain from
improvements in telecommunications and information technology. Bringing the benefits
of IT to developing countries is possible, but the governments of these countries need
to be aware that the process is going to cost money and require institutional changes.




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58 Henriques & Sadorsky


The basic economic production function mixes capital with labour, energy and materials
to produce output. Capital consists of physical capital (buildings, plants, machinery),
human capital (knowledge) and social capital (customs and institutions) (Landes, 1998;
Helliwell, 2002). Smooth, well functioning feedback between these three types of capital
is essential to promoting a fertile economic environment for entrepreneurs and business
and provides the most direct path to productivity improvements and high living
standards.
International investors will frequently calculate the cost of equity for their existing
investments and their proposed investments. Development planners must be able to
make their own cost-of-equity calculations so that they can see first hand how their
investment projects compare with other investment projects around the globe.
Consequently, it is necessary to have good measures of equity risk for managers,
planners, policy makers and investors. The cost of equity is important in valuing new
investment opportunities and in evaluating the ongoing performance of established
business projects. This is especially true in the new economy IT industry where an
understanding of equity risk aids in the examination of the relationship between the IT
sector and economic development.
Estimates of the cost of equity for a particular company vary widely and depend upon
the methodology used. For a particular company, cost-of-equity values based on
systematic risk tend to be lower than cost-of-equity values calculated from downside risk
measures. For some companies, downside cost-of-equity values are twice as large as
cost-of-equity measures based on systematic risk. This is true, even though all of the
cost-of-equity values use the same risk-free rate and same risk premium.
The cost of equity is an essential ingredient for investors seeking estimates of risk-
adjusted returns from an equity investment. The cost of equity is also crucial to project
evaluation and project evaluation is crucial to the success of any firm. Small changes in
the cost of equity can have large impacts on the net present-value calculation of a project
valuation. Large cost-of-equity values lower present-value calculations while small cost-
of-equity values raise present-value calculations. The results in this chapter suggest
that, while systematic risk is a well known measure of equity risk, a prudent investor in
global telecommunications companies might also wish to calculate risk measures based
on total risk, value at risk, downside risk, and regret. Our approach is not to rely on just
one cost-of-equity value but to calculate several different cost-of-equity values and then
use simulation techniques to build up a probability distribution for each company™s cost
of equity. In this way, a clearer picture of where a company™s true cost of equity lies is
developed. Simulation results are insightful because different distributions can have the
same mean values but have very different shapes. For project evaluation and investing,
for example, distributions that are skewed to the right are much preferred to distributions
that are skewed to the left.
One of the insights that emerges from our study is the fact that the average cost of equity
for telecommunications companies in developing countries is not always greater than the
average cost of equity for telecommunications companies in developed countries. This
is borne out by the high cost-of-equity calculations for Cable & Wireless, France
Telecom and Nextel. In general, it is difficult to find evidence of regional differences in
the average cost of equity of telecommunications companies. This is useful to a



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permission of Idea Group Inc. is prohibited.
Risk and Investment in the Global Telecommunications Industry 59


development planner who can then use a portfolio approach in which high-risk invest-
ments are combined with low-risk investments to promote an investment in a developing
country™s telecommunications industry. Provided a developing or emerging economy
can offer attractive risk and return characteristics to investors of financial capital, funds
from portfolio investment should not be overlooked as a source of financial investment
capital.




Acknowledgments
We thank an anonymous reviewer and the editor for helpful comments.




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permission of Idea Group Inc. is prohibited.
Risk and Investment in the Global Telecommunications Industry 61


Appendix
The risk measures (RM) and associated cost of equity (CE) are calculated as follows:


RMSR = βi/ βM = βi


CESR,i = Rf + (RPM) βi (A1)


RMTR = σi/ σM


CETR,i = R f + (RPw) σi/ σM (A2)


RMDRj = Σji/ ΣjM , j = µ, 0, f


CEDRj,i = Rf + (RPM) Σji/ ΣjM (A3)


RMVAR = VARi/VARM


CEVAR,i = R f + (RP M) VARi/VARM (A4)


RMDBj= βji/ βjM = βji j = f, 0


CEDB,ji = Rf + (RPM) βji (A5)


RMREG J= REGji/ REGjM j = f, 0


CEREG,ji = Rf + (RPM) REGij/REGMj (A6)




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62 Pfahler & Grebe




Chapter IV



Reduction of
Transaction Costs
by Using
Electronic Commerce
in Financial Services:
An Institutional and
Empirical Approach
Thomas Pfahler
University of Bayreuth, Germany

Kai M. Grebe
University of Bayreuth, Germany




Abstract
This chapter introduces the Transaction Cost Approach as a means of analyzing
specific transactions in financial services by using the theoretical framework of New
Institutional Economics. It argues that transaction costs can be assessed and used to
compare different business processes. Furthermore, these costs allow a detailed
explanation why certain underlying technologies which form the basis for transactions
become widely accepted whereas others do not prevail. The authors emphasize the


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permission of Idea Group Inc. is prohibited.
Reduction of Transaction Costs by Using E-Commerce in Financial Services 63


relevance of this approach and its application to the field of electronic commerce both
on a theoretical and practical level to document and to interpret current trends in this
sector on the one hand, and to predict future developments on the other hand.




Introduction
The authors analyse the impact of the increasing utilization of information and commu-
nication technology (ICT) and electronic commerce on the coordination of specific
transactions in financial services. In particular, two business processes commonly
occurring in the contractual relationship between a financial institution and its customers
will be considered: bank transfers and stock purchases. The chapter focuses explicitly
on the relationship between a bank and its customers which, in contrast to internal and
inter-bank processes that have already been subject of intensive research, has been
neglected so far.
The basic principles of New Institutional Economics and the instruments developed in
the context of the Transaction Cost Approach serve as theoretical background for the
study and further discussion. The chapter develops and implements a proposal how to
exemplify and to compare these processes under the varying influence of certain
technologies. Therefore, a cost model is developed that will be used in the following to
assess two basic transactions in this specific area. The intention is to reveal the basic
phenomenon and to document the reasons for the current utilization of ICT in this sector
by emphasizing relative reductions of transaction costs by means of electronic com-
merce. The basic statements and conclusions are underlined and illustrated for Germany
in an empirical section. At the end of the chapter, future perspectives and impacts on the
chosen topic will be given and derived.




Electronic Commerce
The need to explain the most important terms and definitions in this context arises directly
from the topic chosen. Choi/Stahl/Winston (1997) define electronic commerce as “a new
market offering a new type of commodity, such as digital products through digital
processes.” This specification already indicates the potential scope and the enormous
consequences which result from the use of electronic commerce.
More fundamentally, electronic commerce can be seen as any economic activity on the
basis of electronic connections (Picot/Reichwald/Wigand, 2001). Hence, it follows that
the underlying technology is crucial to promote the acceptance and the use of electronic
commerce. The use of digital lines and early devices to generate and to exchange
information between participants in the economic cycle was a first step. The introduction
of telephone and telefax services can be seen as the advent of a massive development
which turns out to be the “digital revolution.” Phone lines can be used to connect



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permission of Idea Group Inc. is prohibited.
64 Pfahler & Grebe


computers to the Internet, and digital data highways have been implemented to overcome
limitations and to assure rapid processing. Mobile phones or Personal Digital Assistants
(PDA) enable users to interact and to participate in new as well as in established markets
from almost anywhere at any time.
Electronic commerce can generally take place between two businesses, between a
business and an administration, or between a business and a consumer. In the following,
only the relationship between a business and its customers will be investigated. The
object of the analysis is the financial sector.
Financial institutions hold special positions in the business cycle and differ in many ways
from other corporations or firms. Economically, they perform the functions of liquidity
equalization, of processing information and of conducting several transformations.
Special laws and directives are applied and the services offered are abstract and
immaterial (Büschgen, 1998). Moreover, these services need explanations and need to
integrate an external factor: the bank customer.
In view of these facts it seems evident that the financial sector is likely to be more affected
by the emergence of new technologies than other sectors might be. Consequently, banks
have internally been using information and communication technologies for a long time
to process a large number of highly standardized operations. In the last few years,
especially the core business of banks has been at the center of attention”and it has
changed in several ways. The interface between the institution and its customers has
become increasingly important. New ways of contacting and transacting have been

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