USING AN OPTIONS APPROACH TO EVALUATE KOREAN INFORMATION TECHNOLOGY INFRASTRUCTURE

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USING AN OPTIONS APPROACHTO EVALUATE KOREANINFORMATION TECHNOLOGYINFRASTRUCTURELuke Miller a , Sung H. Choi b & Chan S. Park ca Auburn University , Auburn, AL, USAb Kangnung National University , Kangnung, SouthKoreac Auburn University , Auburn, AL, USAPublished online: 12 Aug 2010.

To cite this article: Luke Miller , Sung H. Choi & Chan S. Park (2004) USINGAN OPTIONS APPROACH TO EVALUATE KOREAN INFORMATION TECHNOLOGYINFRASTRUCTURE, The Engineering Economist: A Journal Devoted to the Problems ofCapital Investment, 49:3, 199-219, DOI: 10.1080/00137910490498915

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The Engineering Economist TJ1209-02 August 18, 2004 11:17

The Engineering Economist, 49:199–219, 2004Copyright c© Institute of Industrial EngineersISSN: 0013–791X print / 1547–2701 onlineDOI: 10.1080/00137910490498915

USING AN OPTIONS APPROACH TOEVALUATE KOREAN INFORMATIONTECHNOLOGY INFRASTRUCTURE

Luke MillerAuburn University, Auburn, AL, USA

Sung H. ChoiKangnung National University, Kangnung, South Korea

Chan S. ParkAuburn University, Auburn, AL, USA

This article presents a practical case application of the real optionsframework that addresses a multi-stage investment decision in Koreaninformation technology infrastructure. Traditional net present value andMonte Carlo simulation are initially performed to ascertain a range ofproject values. The investment scenario is then viewed from a growthand compound options perspective in order to explicitly account for theproject’s irreversible capital expenditures, managerial flexibility, and un-certainty. Appropriate sensitivity analyses and discussion for each realoptions framework provide guidelines for improved decision-making. Ingeneral, it is shown that the real options approach identifies value over-looked by discounted cash flow techniques.

INTRODUCTION

A recent trend addressing capital budgeting decisions under uncer-tainty applies option pricing theory to investments on real assets—commonly referred to as real options. Under the real options framework,any corporate decision to invest or divest in real assets is viewed as anoption. Firms have the right but not the obligation to invest, similar innature to a financial call or put option on a traded security. Utilizing anoptions framework, the firm’s flexibility to revise investment strategiesas events unfold becomes quantifiable. In fact, the ability of the realoptions framework to quantify this flexibility makes it a very appealing

Address correspondence to Luke Miller, Industrial and Systems Engineering, Auburn Uni-versity, 301A Dunstan, Auburn University, AL 36849. E-mail: [email protected]

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200 L. Miller et al.

choice when evaluating certain investment scenarios (Trigeorgis, 1993;Kemna, 1993; Miller & Park, 2002; Copeland & Antikarov, 2001). In arecent survey by Graham and Harvey (2001) of 4400 firms ranging insize and business practice as to the popularity of several capital budget-ing methods, 27% had adopted a real options approach.

This article utilizes several variants of the deferral real options frame-work to evaluate investment in information technology (IT) infrastruc-ture in South Korea. Using real world data and analysis, this article doc-uments the decision process of a Korean IT firm as it assesses its role inIT infrastructure capital outlays. The main contribution of this article isthe detailed real options framing, valuation, and discussion that may beused as an illustration for industry practitioners or classroom instruction.

Several other works in the literature describe the real options approachand its applicability to IT investment decisions. Santos (1991) views in-vestment in IT as an exchange option and uses Margrabe’s financialoption framework (Margrabe, 1978). Kumar (1996) demonstrates thatthe relationship between IT costs and benefits may lead to either increas-ing or decreasing option values with increasing risk. McGrath (1997)describes how the relationship between boundary conditions and uncer-tainty influences the value of IT positioning investments. Panayi andTrigeorgis (1998) value IT investment projects in infrastructure devel-opment utilizing a growth options framework. Benaroch and Kauffman(1999, 2000) use a traditional call option framework to evaluate the de-ployment of IT point-of-scale debit services. Taudes (1998) develops ageneral valuation model of IT software growth options using the con-cept of sequential exchange options. Taudes, Feurstein, and Mild (2000)discuss the practical advantages of using option pricing techniques forthe selection of software platforms.

TELECOMMUNICATION INDUSTRY AND REAL OPTIONS

In light of the remarkable growth of the IT industry in the 1990s,investment in today’s stagnant IT industry is considered highly uncertain.The Telecommunications Act of 1996 in the United States was successfulin increasing competition and promoting investment in the informationsector. However, because of the telecom stock crash of 2000, several ITcompanies are currently in bankruptcy proceedings, traditional leadingfirms are struggling with debt, and new competitors are starved for capital(Hundt, 2001).

Even though the current state of the industry is depressed, many be-lieve the IT industry will turn itself around in the near- to intermediate-term future. Eventually, analysts expect telecommunication companies

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Evaluation of Korean Information Technology Infrastructure 201

to overcome the current trough by becoming more innovative and ef-ficient, and by successfully utilizing the telecommunication infrastruc-ture assets in-place such as optic fiber and satellites. In fact, despitethe industry’s shortcomings, the growth rate of the telecommunicationmarket is still greater than 8% per annum—significantly greater thanthe general market growth rate of 1–2% per year (Weinberg & Woolley,2002). According to the Department of Commerce, the percentage ofU.S. households using the Internet has steadily risen to over 50% for thefirst time, and the digital divide between wealthy versus poor and urbanversus rural residents has been significantly reduced.

The state of the Korean IT industry is slightly different comparedto that of other countries. The current state of the Korean informationsuperhighway business, initiated by the government, is viewed as an in-ternational benchmark. The rate of internet users in Korea exceeds 80%,and new products and services using the superhighway have developedand maintained commercial success (Korean MIC, 2000). In spite ofKorea’s overall success, the industry has had its share of uncertaintyand losses, with many firms experiencing bankruptcy and mergers in asimilar fashion to the U.S. market. The volatile nature of the Korean ITsector is mostly impacted by the rapid development of new technologies,products, and services, and this has led to short life cycles for firms whosupply existing or dated services.

The Korean firms’ ability to successfully posture themselves in theface of future market and technology changes will define tomorrow’sindustry leaders. The successful navigation of these firms will dependupon their ability to manage IT infrastructure projects defined by:

• Irreversible capital expenditures: Most IT infrastructure projects re-quire significant and partially or fully irreversible capital expendi-tures. Erroneous forecasts of product demand can have serious con-sequences on the health of the firm. Over-expansion and unnecessaryexpenditures force management to deal with unused capacity. In fact,it is this under-utilization of capacity that contributes to IT marketcrashes and the non-competitiveness of the company. One approachto manage these irreversible capital expenditures involves a stepwiseinvestment plan. In early phases, firms may choose to invest perceived“minimum” amounts and then increase expenditures as dictated by de-mand and technology. Through an iterative investment process, firmscan adapt to the changing landscape and help mitigate the significantimpact of erroneous forecasts and unnecessary capacity.

• Required upfront expenditures: Similar to other industries thatrequire upfront investments such as pharmaceutical research and

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development, the IT sector requires firms to invest in their own future.IT firms must invest in infrastructure in order to capitalize on down-stream opportunities. This is particularly true in the Korean marketwhere the government takes an active role in IT market growth. ITKorean infrastructure projects are often performed by a consortiumof several firms because of the large investments required. Firms thatdo not participate in the initial capital outlays usually cannot expectto receive the rights and benefits from the opportunities created bythis upfront investment.

• Time lag between investment and benefits: Many of the benefits ofinvesting in IT infrastructure are not realized until subsequent invest-ments have been made. In other words, the initial investment may notbear fruit itself, but the follow-up opportunities created by this initialinvestment may provide the bulk of the benefits.

Evaluating investment scenarios impacted by uncertainty, delay inproject benefits, and irreversible expenditures is not a trivial task. Nu-merous capital budgeting techniques exist to aid in identifying today’svalue associated with these projects; namely, payback period, discountedcash flow approaches to include net present value (NPV) and internalrate of return, and real options. For certain investment scenarios NPVmay work best, whereas in other cases the real options framework mayprove superior. The NPV approach works best for decisions involvinga moderately straightforward business structure, well-defined projects,and a steady environment allowing for dependable forecasts. The realoptions approach should be utilized for business decisions dependentupon the value of additional information, timing, and uncertainty. Infact, NPV may be viewed as a special case of the real options frame-work. Let Vt denote the present value of project benefits at time t andI denote the present value of investment costs. Under both NPV andreal options the firm will be advised to invest if Vt > I , and not investif Vt < I . Consider the following project payoff (or equivalently realoption payoff):

max(Vt − I, 0) (1)

If t = 0 or today, then the option payoff in Equation (1) is nothingmore than the standard NPV decision rule. Implicitly, the NPV approachassumes management exercises their option today (i.e., t = 0) and viewsthe decision as go–no go today even if the capital outlay is not requiredtoday. The benefit of the real options framework is valuing Equation (1)when t > 0. What the real options framework identifies as a premium

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above and beyond NPV is the firm’s option to walk away from theinvestment obligation if Vt < I at the actual time of the required capitaloutlay. Thus, in general terms, the real options approach identifies theproject’s value factoring in uncertainty and management’s flexibility towalk away from the investment obligation.

Within an IT infrastructure investment scenario, the firm must ex-pend an upfront amount today in order to obtain the option to invest indownstream opportunities. As such, the value of the entire investmentscenario today may be expressed as:

Value of IT infrastructure = NPV of upfront expenditure

+ option to invest downstream (2)

From an options perspective, investing in the IT upfront costs todayprovides the option to invest in downstream opportunities. Thus, theNPV of the upfront expenditures can be viewed as the option premiumthe firm pays today to acquire the right, but not the obligation, to investdownstream. From a decision-making perspective today, the firm willinvest in the upfront expenditures if the flexible net present value (FNPV)is positive. Thus, Equation (2) may be alternatively expressed as:

FNPV = CA + C0 (3)

where CA is the actual option premium the firm expends today and C0is the theoretical option premium of the investment scenario. In otherwords, CA represents the actual amount the firm plans on expendingtoday in upfront costs. The theoretical worth of the real option, C0, willbe valued using an appropriate arbitrage-free pricing model. If C0 > CA,then investment in the upfront costs will be recommended.

KOREAN INFORMATION SUPERHIGHWAYINFRASTRUCTURE (KISI)

This section documents a firm’s decision to become a member of anIT consortium and invest in the Korean information superhighway in-frastructure investment project (KISI). Voice-based telecommunicationis evolving into multimedia-based, requiring an overhaul of the exist-ing infrastructure that is unable to support the amount of digitalizeddata transferred through the telecommunication networks. The Koreangovernment is taking an active role in the infrastructure overhaul andplans on establishing a consortium of private companies to take charge

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TABLE 1. The KISI Development Plan of Company H

Phase Development plan

Phase 1Today, T = 0Initial investment

• Dual backbone in 5 metropolitan areas• Multi-gigabit POP on main nodes• Router: 80G ≥ 300G, ATM: 50G ≥ 400G ATM

(asynchronous transfer mode)• 10G Ethernet and 10G POS (point-of-scale) interface

Phase 2Two years from today1st expansion investment

• Expansion of POP to medium- and small-sized cities• Router: 300G ≥ Tera, ATM: 400G ≥ Tera• Development of multicasting and CDN (content

delivery network)Phase 3Four years from today2nd expansion investment

• Supporting end-to-end QoS• Applying IPv6 address system to commercial network

of the KISI development (Korean MIC, 1999). Although the govern-ment will keep a neutral preference to the technologies selected, eachof the participating companies will have their own development planand independently manage its plan within the bounds defined by thegovernment’s global development plan.

Initially, four major telecommunication companies plan on participat-ing in the KISI consortium, as investing today will provide these firmsthe right to take advantage of the completed IT infrastructure. The KISIinvestment scenario for these firms is impacted by a phased implemen-tation plan, irreversible expenditures, uncertainty, and delayed benefits.This case study describes the evaluation of the KISI project from theperspective of Company H, one of the four telecommunication compa-nies in the consortium. To maintain corporate anonymity the company’sreal name and detailed cash flow statement will not be used; however,the actual dollar values and analysis remain intact.

Company H has a stepwise plan utilizing three development phasesranging from a model network within five metropolitan areas to a nation-wide network connecting all cities with a population greater than 100,000residents (Hanaro, 1999). The KISI development plan for Company His summarized in Table 1 and Figure 1.

Traditional NPV Analysis and Simulation

Company H must invest today in Phase 1 to initiate the KISI project.Two years from today Company H will have the option to invest inPhase 2. If the firm invests in Phase 2, then it will have the option in

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FIGURE 1. Evaluation model of the KISI project of Company H.

year 4 to invest in Phase 3. Figures 2, 3, and 4 summarize the expectednet cash flows from Phases 1, 2, and 3, respectively. The benefits ofthis project come from internet business, VoIP/phone, and access ser-vice. The costs mostly consist of the backbone, subscriber, and Internetnetwork. Assuming a minimum attractive rate of return (MARR) of12% (compounded continuously), the NPV of each phase is depicted inTable 2.

The expected present value of the KISI project from Company H’sperspective is negative $703 million and NPV would recommend notinvesting. In order to gauge the potential range of values for the KISIproject, a Monte Carlo simulation was performed on the relevant cash

FIGURE 2. Phase 1 expected net cash flows (in $millions).

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flows by identifying benefits and costs as random variables with theappropriate dependency relationships. This cash flow statement simu-lation approach was performed with Palisade @Risk software. Usinghistorical data as a benchmark, beta probability density functions weresubjectively assigned to annual demand for end-user Internet business,VOIP/phone, and access service. Annual demand for the services wasprojected to increase with increasing time. In general, the historical dataand the “expert” judgment of the firm indicated a positively skewed pdf(for the beta pdf, α2 > α1 > 1) for each annual demand input. Addi-tionally, annual projected subscription rates were subjectively fit withtriangular probability density functions. In order to capture time depen-dencies in annual demand, a positive correlation coefficient (p = .5)was assigned between annual demand increments. For example, if de-mand in time t is relatively high, then it is assumed that demand intime t + m, where m > 0, is also relatively high, and vice versa. After


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TABLE 2. NPV of KISI Project(in $millions)

KISI phase NPV

Phase 1 −$393Phase 2 −$375Phase 3 $65

Total −$703

100,000 simulation runs, the NPV profile for the KISI project is depictedin Figures 5 and 6 and Table 3.

As is evident from the NPV plots and table, the KISI project will beimpacted by significant uncertainty. The expected return of the simu-lation is negative $703 with a standard deviation of $1485. ObservingFigure 5, the NPV relative frequency indicates a positive skew in theproject’s value ranging from approximately negative $4500 to positive$4300. Figure 6 and Table 3 indicate that there is a 40% chance theproject has a positive NPV and 5% chance the projects value will be inexcess of $2384M. These results indicate the potential for significantvalue if Company H is to participate in the KISI consortium. This alsoillustrates the significant risk.

In order to further investigate the value of this investment scenario,Company H views the KISI opportunity from an options perspective. Thefirm believes the real options framework will identify value overlookedby traditional NPV through explicitly considering the phased implemen-tation plan, irreversible expenditures, uncertainty, and delayed benefits.

FIGURE 5. KISI project NPV relative frequency.

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FIGURE 6. KISI project NPV cumulative frequency.

Real Options Analysis

This section proposes a real options approach to value the incrementaland sequential KISI investment scenario. Assuming arbitrage-free val-uation and an Ito process on demand, the risk-neutral pricing approachderived for financial options is considered appropriate for valuing op-tions on real assets (Mason & Merton, 1985; Trigeorgis, 1993; Birge &Zhang, 1999; Kamrad & Ernst, 2001). As such, two off-the-shelf optioncalculators will be demonstrated in this case study: the Black (1976)futures option and Geske (1979) compound option models. The Blackmodel naively views the multi-phase KISI investment as a two-phasescenario in order to demonstrate the process of framing a problem as abasic growth option. Although the growth option does suffice in valuing

TABLE 3. NPV Profile and CumulativeFrequency

NPV profile In $millions

Minimum −4476Maximum 4228Mean −703Standard deviation 1485Mode −11805th Percentile −245825th Percentile −130550th Percentile −35875th Percentile 72195th Percentile 2384

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the FNPV, the compound option model more accurately captures andidentifies value associated with this three-phase KISI scenario.

Simple Growth Option

Model description. The investment in the upfront costs today, CA,may be viewed as a European call (or growth) option on a futures contractwhere the futures price is equal to the present value of Phase 3 cashinflows at time T , VT , with exercise price I (Kemna, 1993). Thus, thetheoretical worth of the option today, C0, may be determined usingthe standard European call option on a futures contract given in Black(1976):

C0 = e−rT [VT N (d1) − IN(d2)] (4)

where

d1 = ln(VT /I )+0.5σ 2Tσ√

T

d2 = d1 − σ√

TVT = present value of the Phase 3 cash inflows in year 4

I = investment outlay of Phase 3 in year 4σ = volatility of the rate of change of the Phase 3 project valuer = riskless rate of interest

N( ) = univariate normal distribution function

The NPV of Phases 1 and 2 may be viewed as the option premiumthe firm pays today to acquire the right, but not the obligation, to investin Phase 3 in year T = 4. From a decision-making perspective today,the firm will invest in Phase 1 if the FNPV is positive, or:

FNPV = CA + C0 = NPV of Phases 1 and 2

+ option to invest in Phase 3 in year 4 (5)

Figure 7 provides the cash flow diagram for this real options framework.From Table 3, the NPV of Phases 1 and 2 is negative $768M. The presentvalue of Phase 3’s cash flows at T = 4 years is VT = $1922M and theinvestment cost to implement Phase 3 is I = $1817M. If the presentvalue of Phase 3 cash inflows in year 4, V4, is greater than I , theninvesting in Phase 3 will be advised. If, however, V4 < I , then Phase 3will not be pursued. In other words, the value of the option to invest inPhase 3 is the present value of the payoff max(V4 − I , 0).

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FIGURE 7. Cash flow diagram for simple growth option.

Estimating a volatility parameter. Before proceeding with valuingthe option using Equation (4), estimating a volatility parameter thatrepresents the project value uncertainty is necessary. For a real optionsanalysis, four techniques may be used to estimate volatility: the twinsecurity argument (Trigeorgis, 1993), implied volatility (Hull, 2000),modified scenario analysis (Miller & Park, 2004), and Monte Carlosimulation (Copeland & Antikarov, 2001; Herath & Park, 2002).

• Under the twin security approach, for projects where an appropriatetwin security can be identified in the market (such as the futures marketfor natural resource projects or the firm’s stock price), the historicalreturn distribution of the twin security can be used as a proxy for thereal asset volatility. This argument pre-supposes that if the projectitself were publicly traded, then it too would vary in a similar fashionto the twin security.

• If the firm has traded financial options, then the implied volatility of thefinancial option can be used to gauge project volatility. Conceptually,the implied volatility represents the uncertainty that investors haveplaced on the future value of the firm.

• The modified scenario analysis utilizes project present value estimatesand properties of geometric Brownian motion to determine volatility.

• The Monte Carlo simulation approach simulates the volatility by de-termining the return distribution for the project’s value between twotime periods. The standard deviation of this return distribution thenrepresents the volatility of the project.

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TABLE 4. Determining the Volatility of Project Value

Phase 3 cash flow statement (in $millions)

Year 4 5 6 7 8 9 10

Revenues 0 0 R6 R7 R8 R9 R10

Expenses 0 0 E6 E7 E8 E9 E10

Net cash flows I (−1817) 0 CF6 CF7 CF8 CF9 CF10

(expected value) (441) (592) (659) (718) (741)Present value V4 (1922) V5 (2167)

Step 1: Calculate the expected present value for years 4 and 5 using MARR = 12% (compoundedcontinuously)

V4 = 1922 V5 = 2167.

Step 2: Determine the continuous rate of return between years 4 and 5

µ = ln

(V5

V4

).

Because of the completed simulation setup in the previous sectionto determine the NPV profile, this case study utilizes the Monte Carlosimulation approach to estimate volatility (see Copeland and Antikarov,2001, for a detailed example). Table 4 pictorially demonstrates the ap-proach. Denote V4 and V5 as the expected present value of Phase 3 cashinflows in years 4 and 5, respectively. In other words, V4 is the expectedpresent value of Phase 3’s revenues and expenses in year 4, and V5 isthe expected present value of Phase 3’s revenues and expenses in year5. Because annual revenue are random variables, so too are the net cashflows and present value of these cash flows in years 4 and 5. By defini-tion, volatility is the annual standard deviation of project value returns.For this analysis, the continuously compounded expected Phase 3 return,µ, between years 4 and 5 (and consequently between any consecutiveyears) is:

µ = ln

(V5

V4

)= ln

(2167

1922

)= 12%

The purpose of the Monte Carlo simulation is to capture the varianceassociated with µ by calculating numerous simulated values for V4 andV5. The square root of this variance of returns is known as the annualvolatility or σ . After 100,000 simulation runs of this scenario, the meanof the return distribution was 11.8% with a standard deviation of 63%.The return distribution frequency chart is plotted in Figure 8. Notice that

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FIGURE 8. Return distribution frequency chart.

the return distribution is somewhat normally distributed, as is assumedby most option pricing models. Thus, for this case study a baselinevolatility of 63% will be assumed.

Valuation and sensitivity analysis. Let us return to the option valu-ation. Using the inputs VT = $1922, I = $1817, T = 4 years, σ = 63%,and r = 5%, in Equation (4), the value of the growth option isC0 = $765M and the FNPV = −$768 + $765 = −$3M. Comparing theFNPV with the traditional NPV calculated in the previous section, thefirm’s option to walk away from the Phase 3 investment in year 4 isworth $700M. In other words, the real options framework identified anadditional $700M overlooked by the traditional NPV approach.

Although the FNPV is slightly negative, the KISI investment op-portunity appears significantly more appealing. Consider the sensitivityanalysis performed on the key parameters in Figure 9. Minor shifts in theestimates for VT , σ , and I will yield a positive FNPV and alter CompanyH’s decision to participate in the KISI consortium. Notice that the cashflow estimates for Phases 1, 2, and 3 significantly impact the FNPV. Thisis an important observation because as time passes these estimates arecertain to change with the dynamic business climate.

Framing the KISI phased investment project from a simple growthoption perspective identifies some value overlooked by traditional NPV.However, because of the three-phase scenario, the project would best beviewed as a compound option. This is accomplished in the next section.

Compound Option

Model description. When compared with the simple growth op-tion, the compound options framework more accurately captures value

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FIGURE 9. Sensitivity analysis for simple growth option.

associated with the KISI three-phase investment scenario. When the firminvests in Phase 1 today, it acquires the option to invest in Phase 2 in year2. If and when the firm invests in Phase 2, it then acquires the option toinvest in Phase 3 in year 4. As such, the decision to invest in Phase 3 isdependent upon the option to invest in Phase 2. These types of contingentinvestments may be valued utilizing a compound options framework andwill be valued utilizing the Geske (1979) compound option model:

C0 = e−rT2 [VT M(k, h; ρ) − I1 M(k − σ√

T1, h − σ√

T2; ρ)]

−I2e−rT1 N (k − σ√

T1) (6)

where

h = ln( VTI1

)+ 12 σ 2T2

σ√

T2

k = ln( VTVc

)+ 12 σ 2T1

σ√

T1

VT = present value of Phase 3 cash inflows in year 4Vc = critical value above which the first call option will be

exercisedI1 = NPV of Phase 2 in year 2I2 = investment outlay of Phase 3 in year 4σ = volatility of the rate of change of the Phase 3 project valuer = riskless rate of return

N( ) = univariate normal distribution functionM(a,b; ρ) = bivariate cumulative normal distribution function

ρ = ( T1T2

)1/2

T1 = time to maturity of the option to invest in Phase 2T2 = time to maturity of the option to invest in Phase 3

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FIGURE 10. Compound options framework KISI investment scenario.

Valuation. The NPV of Phase 1 may be viewed as the option premiumthe firm pays today to acquire the right, but not the obligation, to investin Phases 2 and 3 in years 2 and 4, respectively. From a decision-makingperspective today, the firm will invest in Phase 1 if the FNPV is positive,or:

FNPV = CA + C0 = NPV of Phase 1 + compound option to invest

in Phases 2 and 3 (7)

Figure 10 provides the cash flow diagram for this real options framework.From Table 3, the NPV of Phase 1 is negative $393M. Upon investingCA = $393M, Company H receives the option to invest in Phase 2 inyear T1 = 2. Also from Table 3, the cost of the option to invest in Phase2 will be the NPV of Phase 2 in year 2 or I1 = 375e(0.12∗2) = $476M.If and when the firm invests in Phase 2, it then receives the option inyear T2 = 4 to invest the Phase 3 outlay, I2 = $1817M, and receive thepresent value of Phase 3 cash inflows, VT = $1922M.

Using the inputs VT = $1922, I1 = $477, I2 = $1817, T1 = 2 years,T2 = 4 years, σ = 63%, and r = 5%, in Equation (6), the value ofthe compound option to invest in Phases 2 and 3 is C0 = $520M andthe FNPV = −$393 + $520 = $127M. Comparing this FNPV with thetraditional NPV calculated in the previous section, the firm’s option toinvest in Phases 2 and 3 is identified to be worth an additional $830M. Inother words, identifying the value associated with proactively managing

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the KISI investment scenario is worth $830M more than traditional NPVwould indicate. Because the FNPV is positive, the firm should chooseto become members of the KISI consortium.

The critical value of Phase 3 cash inflows above which the option onPhase 3 should be acquired at T1 is obtained by solving the followingexpression for Vc:

C(Vc, I2, σ, T2 − T1, r ) = I1

where C is the Black (1976) futures option model provided inEquation (4). Through an iterative approach, Vc = $1666M. In otherwords, if two years from today the estimate for the present value ofPhase 3 cash inflows is greater than $1666M (i.e., VT > $1666M), thencontinued investment in Phase 2 will be recommended.

Sensitivity analysis. To get an indicator of the most influential inputsin the compound options framework, a sensitivity analysis is conductedin Figure 11. Notice that the present value and volatility of Phase 3 cashinflows significantly impact the FNPV, as a slight decrease in VT and σalter the decision to join the KISI consortium. Also, note that increasesin I1 and I2 do not appear to impact the decision, as the FNPV remainspositive over the relevant range. Finally, observe that I2 influences theFNPV more than I1. This indicates the downstream investment outlayin Phase 3 impacts the multi-phased investment scenario more than thePhase 2 NPV.

To gauge the potential range of values for the KISI project utilizing thecompound options framework, a Monte Carlo simulation was performedon the relevant cash flows by identifying benefits, costs, and timing as

FIGURE 11. Sensitivity analysis for compound option.

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FIGURE 12. KISI project FNPV relative frequency.

random variables with the appropriate dependency relationships. Usingthe same approach as the NPV simulation profile determined in the pre-vious section, this section determines the FNPV relative and cumulativefrequency plots. This is accomplished by using the same inputs for theNPV simulation, but utilizing the compound option framework in deter-mining the FNPV. After a 100,000 run simulation, the FNPV profile forthe KISI project is depicted in Figures 12 and 13.

Figure 12 plots the FNPV relative frequency. Observe that the FNPVis positively skewed and has a mean of $130M and standard deviationof $389M. This is an interesting observation when compared with thestandard deviation of the traditional NPV of $1485. The significantly

FIGURE 13. KISI project FNPV cumulative frequency.

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smaller variance of the real options approach is explained by the optionsframework ability to identify the value associated with the right, but notthe obligation, to invest in Phases 2 and 3. Because the traditional NPVapproach implicitly assumes management will invest in Phases 2 and 3,the NPV profile includes the variance associated with the “do not invest”states. In contrast, the FNPV and options approach explicitly removesthe variance associated with these “do not invest” states by providing theoption to not invest. The real options framework identifies and valuesmanagement’s flexibility to invest when benefits exceed costs at eachmulti-phased decision point. Finally, observe in Figure 13 that there is a60% chance the KISI project will have a positive FNPV and 5% chanceit will be in excess of $865M. These results indicate the potential forsignificant value if Company H is to participate in the KISI consortium.

CONCLUDING REMARKS

In this article, a real options framework is used to value an IT in-frastructure investment in South Korea. Initially, a traditional NPV andMonte Carlo simulation were performed to ascertain the KISI projectvalue. However, because of the project’s irreversible capital expendi-tures, required upfront outlays, time lag between investment and bene-fits, managerial flexibility, and uncertainty, traditional NPV was unableto properly identify project value. A real options framework was thenemployed to explicitly capture the firm’s option to invest in this multi-phase scenario. To begin with, the scenario was framed as a growthoption and valued as a two-phased investment. This simple growth op-tions framework identified an additional $700M that was overlookedby NPV. However, because of the three-phased nature of the invest-ment scenario, the KISI project was then framed as a compound option.Using the compound options framework, the approach identified an ad-ditional $830M in project value overlooked by NPV and recommendedthe firm to participate in the KISI consortium. Appropriate sensitivityanalysis and Monte Carlo simulation of the flexible net present valuewere performed. In conclusion, Company H did decide to participate inthe Korean KISI consortium and Phase 1 is underway.

This case study is performed on a multi-phased IT infrastructure in-vestment. However, its approach and discussion are quite general and canbe applied to any investment impacted by uncertainty, irreversible expen-ditures, and managerial flexibility. Such situations may include valuingnatural resource properties, multiple product lines, flexible manufactur-ing cells, franchises, initial public offerings, research & development,and other technology opportunities.

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REFERENCES

Benaroch, M. and R.J. Kauffman, “A case for using real options pricing analysis toevaluate information technology project investments,” Information Systems Research,Vol. 10, No. 1, 1999, pp. 70–86.

Benaroch, M. and R.J. Kauffman, “Justifying electronic banking network expansionusing real options analysis,” MIS Quarterly, Vol. 24, No. 2, 2000, pp. 197–225.

Birge, J. and R. Zhang, “Risk-neutral option pricing methods for adjusting constrainedcash flows,” The Engineering Economist, Vol. 44, 1999, pp. 36–49.

Black, F., “The pricing of commodity contracts,” Journal of Financial Economics, Vol. 3,1976, pp. 67–179.

Copeland, T. and V. Antikarov, Real Options: A Practitioner’s Guide, New York:TEXERE LLC, 2001.

Geske, R., “The value of compound options,” Journal of Financial Economics, Vol. 7,1979, pp. 63–81.

Graham, J. and C. Harvey, “The theory and practice of corporate finance: Evidence fromthe field,” Journal of Financial Economics, Vol. 60, 2001, pp. 187–243.

Hanaro Telecommunications, “The construction plan of information super highway.”1999.

Herath, H. and C.S. Park, “Multi-stage capital investment opportunities as compoundreal options,” The Engineering Economist, Vol. 47, No. 1, 2002, pp. 1–27.

Hull, J., Options, Futures, and Other Derivatives, 4th edition, Upper Saddle River, NJ:Prentice Hall, 2000.

Hundt, R., “Weathering Telecom’s dark and stormy night,” McKinsey Quarterly, No. 4,2001, pp. 118–128.

Kamrad, B. and R. Ernst, “An economic model for evaluating mining and manufacturingventures with output yield uncertainty,” Operations Research, Vol. 49, No. 5, 2001,pp. 690–699.

Kemna, A., “Case studies on real options,” Financial Management, Vol. 22, 1993,pp. 259–270.

Korean Ministry of Information and Communication, “The comprehensive diagnosis ofKorean information superhighway infrastructure,” 1999.

Korean Ministry of Information and Communication, “Korean telecommunication sta-tistical yearbook 2000,” 2000.

Kumar, R. L., “A note on project risk and option values of investments in informationtechnologies,” Journal of Management Information Systems, Vol. 13, No. 1, 1996,pp. 187–193.

Margrabe, W., “The value of an option to exchange one asset for another,” The Journalof Finance, Vol. 33, 1978, pp. 177–186.

Mason, S. and R. Merton, “The role of contingent claims analysis in corporate finance,”in Recent Advances in Corporate Finance, E. Altman and M. Subrahmanyam (Eds.),Homewood, IL: Richard D. Irwin, 1985.

McGrath, R. G., “A real options logic for initiating technology positioning investments,”Academy of Management Review, Vol. 22, No. 4, 1997, pp. 974–996.

Miller, L. and C.S. Park, “Decision making under uncertainty: Real options to the res-cue?” The Engineering Economist, Vol. 47, No. 2, 2002, pp. 105–150.

Dow

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Miller, L. and C.S. Park, “Economic analysis in the maintenance, repair, & overhaulindustry: An options approach,” The Engineering Economist, Vol. 49, No. 1, 2004,pp. 21–41.

Panayi, S. and L. Trigeorgis, “Multi-stage real options: The cases of information tech-nology infrastructure and international bank expansion,” The Quarterly Review ofEconomics and Finance, Vol. 38, 1998, pp. 675–692.

Santos, B., “Justifying investments in new information technologies,” Journal of Man-agement Information Systems, Vol. 7, No. 4, 1991, pp. 71–90.

Taudes, A., “Software growth options,” Journal of Management Information Systems,Vol. 15, No. 1, 1998, pp. 165–185.

Taudes, A., M. Feurstein, and A. Mild, “Options analysis of software platform decisions:A case study,” MIS Quarterly, Vol. 24, No. 2, 2000, pp. 227–243.

Trigeorgis, L., “Real options and interactions with financial flexibility,” Financial Man-agement, Vol. 22, No. 3, 1993, pp. 202–224.

Weinberg, N. and S. Woolley, “Telecomeback,” Forbes Magazine, 21 January, 2002.

BIOGRAPHICAL SKETCHES

LUKE T. MILLER has experience as an operations research analyst and program manageras a captain in the United States Air Force ([email protected]). His work experienceincludes statistical modeling and analysis, scheduling, optimization, and decision anal-ysis. He is a graduate of the University of Virginia (BS) and Auburn University (MS,PhD). His main research interests are in real options, financial engineering, economicdecision analysis, and corporate finance.

SUNG HO CHOI ([email protected]) is currently an associate professor of In-dustrial Engineering at Kangnung University. Dr. Choi received his BA in economicsfrom Yonsei University, MS and PhD degrees in Industrial Engineering from KoreaAdvanced Institute of Science and Technology. Before joining the faculty at KangnungUniversity, he was a senior research scientist at Electronics and TelecommunicationsResearch Institute (ETRI). His current research interest lies in the area of engineeringeconomics, financial engineering and telecommunication economics. He is a co-authorof Engineering Economics (Korean) published by Youngchi, 2004.

CHAN S. PARK ([email protected]) is currently a Samual Ginn Distinguished Pro-fessor of Engineering at Auburn University. Over his 25-year academic career, he hasbeen actively involved in a variety of research, teaching, and professional consulting onthe subject matters. As a leading authority on engineering economics, his work has beenrecognized internationally in the fields of engineering economics. His current researchinterest ranges from strategic and economic decisions, financial engineering (real op-tions valuation), to energy modeling (electrical power). He also authored or coauthoredseveral leading textbooks on the subjects including Fundamentals of Engineering Eco-nomics (Prentice Hall, 2004), Contemporary Engineering Economics, 3rd ed. (PrenticeHall, 2002), and Advanced Engineering Economics (John Wiley, 1990). He received hisIndustrial Engineering degrees from Purdue and Georgia Institute of Technology (PhD),respectively. He is also a licensed Professional Engineer in the State of Florida, and wasthe Editor-in-Chief for The Engineering Economist between 1997 and 2003.

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Documents

USING AN OPTIONS APPROACH TO EVALUATE KOREAN INFORMATION TECHNOLOGY INFRASTRUCTURE