Praxis Forschung State of the Art

25 Years Real Options Approach to : Reviewand Assessment

by Philipp N. Baecker and Ulrich Hommel*

Abstract

• During recent years, real options have emerged as one of the most actively researched topics in and related fields. A quarter of a century since the first appearence of the term, this artic1e sets out to review the main contributions to real options theory and assess their impact on our understanding of managerial behavior in an environment characterized by economic uncertainty and flexibility. Next to the pricing foundations, the artic1e emphasizes the role of real options for the extraction and commercialization of natural resources, research and development, corporate risk management and foreign direct investment, production flexibilities, as weH as game• theoretic treatments of corporate investment decisions. In addition, the authors discuss reasons for the reluctance of corporate practitioners to employ real option analysis in their decisions and ways in which the underlying concems have been addressed in the literature so far.

Eingegangen: 28. Januar 2003 Philipp N. Baecker, MaiI: [email protected] Prof. Ulrich HommeI, MaiI: Ph.D .• [email protected] Endowed Chair for and Capital Markets & Center for Entrepreneurial and Small Business Finance European (ebs) 65375 Oestrich-Winkel, Germany Philipp Baecker is a Ph.D. candidate and research assistant at the En• dowed Chair for Corporate Finance and Capital Markets of the EURO• PEAN BUSINESS SCHOOL (ebs). His main interests are in the areas of real options, evolutionary economics, and the intersection of corporate finance and strategy. Ulrich Hommel is a Professor of Finance as weIl as the Academic Director of the Center for Entrepreneurial and Small Business Finance at the EUROPEAN BUSINESS SCHOOL (ebs). His main research areas are real options, entrepreneurial and small business finance as BETAEB.WJI{fS(} weIl as corporate risk management. He is a member of the editorial board of Finanz Betrieb. © GabIer-Vet1ag 2004

ZfB-Ergänzungsheft 312004 1 T. Dangl et al. (eds.), Real Options © Springer Fachmedien Wiesbaden 2004 Philipp N. Baecker and Ulrich Hommel

A. Introduction: Promise and Peril of Real Options

I. From Financial to Real

The real options approach to valuation, or the modem theory of investment under uncer• tainty, is based on a simple but nevertheless profound insight: flexibility creates value. Over the years, an increasing number of academic researchers and corporate practitioners has become dissatisfied with DCF-based valuation methods. These methods seem to con• sistently fall short of capturing key value drivers! and have shown to provide insufficient incentives for managerial actions to conform with the long-term interest of shareholders. 2 Methodological refinements, such as the combination of DCF approaches with decision tree analysis, suffer from similar drawbacks and have likewise failed in capturing the imagination of corporate decision-makers. In contrast, real options theory employs an intuitive analogy between financial options and real decision-making flexibilities, and, in doing so, enables decision-makers to apply standard option pricing techniques to the valuation of real investment projects. The roots of option pricing theory can be traced back to two Nobel-Prize-winning contributions. In 1973 Black/Scholes and Merton lay the foundation for the modem deriv• atives industry by conceiving an analytical pricing model, or closed-form solution, for European call and put options. The Nobel laureates R. Merton and M. Scholes, together with the late F. Black, show that the value of an option is deterrnined by ca1culating the cost to insure against, or hedge, the risk incurred when selling the . A simpli• fied approach to option pricing based on a binomial approximation of the stochastic process for the underlying is later pioneered by Cox et al. (1979). When using DCF methods, risk is usually accounted for by adding a premium to the discount rate (to reflect the ) or by transforrning expected cash flows into their certainty equivalent. Option pricing theory uses the principle of risk-neutral valuation and adjusts the probability measure (i.e. employs an equivalent martingale measure) to obtain option prices in accordance with market valuations. 3 Specifically, option values can be obtained by discounting "risk-neutral" expected cash flows at the risk-free rate. As aprerequisite for the equivalent martingale measure to be unique, financial markets have to be com• plete, i.e. risks are traded and continuously priced.4 In contrast, valuing non-traded assets as contingent claims requires the use of an equilibrium asset pricing model, for instance the generalization of the intertemporal CAPM by Cox et al. (1985).5 In this case, the state variables for the underlying asset are modeled with a risk-adjusted drift so that expected future cash flows can again be discounted at the risk-free rate. The term "real options" goes back to Myers (1977) who analyzes the impact of discre• tionary investment opportunities - in particular growth opportunities - on the value of the firm and its financial policy. His analysis demonstrates that real options will lead to areduction in firm value if the firm issues risky debt. The firm may either be forced to adopt a sub-optimal investment strategy in order to satisfy creditor claims or to force creditors to bear the costs of avoiding the sub-optimal strategy. While the contribution by Myers, from today's perspective, cannot be considered part of mainstream real options research, it nevertheless represents the starting point of a whole new area of research in finance. 6

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In general, real investment projects (or specific features thereot) can be interpreted as option rights if they exhibit all of the following three characteristics: (l) payoffs are subject to some form of (market) risk (uncertainty), (2) management possesses certain degrees of freedom in allocating corporate funds or assets (jlexibility), and, finally, (3) using these de• grees offreedom will lead to some form of sunk costs (irreversibility).7 While the first char• acteristic is generally present in real investments, the second condition requires that management be able to alter its operative strategies in response to changes in the economic environment (Le. has the option but not the obligation to do so). Correspondingly, in the most basic case, the investment-specific payoff profile takes on the form of a "hockey stick", a standard feature offinancial "plain vaniHa" options (simple calls and puts). The third charac• teristic makes option costly. If the actions taken were completely reversible, option rights would carry zero time value and immediate exercise would always be optimal. The validity of the implied analogy between financial and real options is best verified by estab• lishing the equivalence of real investment value drivers and option pricing parameters.8 Over the past 25 years, real options have become one of the most rapidly evolving areas in the academic finance literature. There is ample proof of the still-burgeoning interest in this sub-field, documented by weH over 600 papers, either published or in working paper format. Furthermore, an increasing number of case studies illustrates practical applications of this new valuation too1.9 More than a dozen books and a half-dozen special journal issues have been published in recent years with a specific focus on real options analysis. lO The primary objectives of this study are twofold: a review of the methodological progress leading to a better understanding of the impact of flexibility on firm and project value, and a critical assessment highlighting notable gaps in the literature and deficits in corporate practice. Particular focus lies on the seminal contributions that have either stimulated further research on real option valuation or have generated fundamental insights into how the real options method can be applied in a real-world setting. Although in practice the term "real option valuation" seems to be increasingly understood to also cover methods de facto depending on individual risk preferences (e.g. decision tree analysis), this article will stick to the narrower definition commonly used in the academic literature.

11. Challenges in Real Option Valuation

This article represents an attempt to give a fair and balanced account of the accomplish• ments to date. While the real option method has distinct advantages over alternative approaches, it is no universal problem sol ver. A major issue is the frequent lack of reliable input data. The Internet market bubble coincided with a rising interest in real options, partly because managers, analysts, and investors suffered from the miscon• ception that the mere identification of unrealized growth potential would suffice to ob• tain exact market value estimates. It is not surprising that this illusion has since faded. However, the real options approach can always be applied as a qualitative tool for assess• ing the value impact of managerial discretion. In that sense, it has universal relevance for all corporate decision-makers. To quote Darwin: "It is not the strongest of the species that survive, nor the most intelli• gent, but the one most responsive to change." (1835)

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Equally unjustified is the criticism occasionally raised in German acadernia. By no means is the real options approach "old wine in new bottles"Y Applying option pricing techniques to real investment problems provides decision-makers with an elegant method of breaking down project-related complexity while forcing users to analyze the econornic fundamentals of investment proposals or projects under review. If done properly, most input data is generated as a byproduct of the strategic planning process. In addition, by valuing decision-making flexibilities in accordance with financial markets, real options should bring discipline to strategie planning and thereby enhance shareholder value. 12 Agency theory suggests, however, that in the presence of information asymmetries sophisti• cated capital budgeting techniques are likely to be rnisused and may actually lead to the destruction of shareholder value as they offer additional degrees of freedom when it comes to demonstrating the value proposition of an investment. This is especially true of investments in (intangible) real options with no immediate positive effect on cash flows. They represent "dynarnic capabilities" characterized by causal ambiguity.13 Ignoring such potential sources of value seems equally inappropriate, which underlines the importance of sound practiees for managing confliets of interest and counterbalancing the occasional short-terrnism of capital markets while at the same time providing the right incentives to management. Set aside for a moment the practieal aspects of implemention, the analogy between finan• cial and real options itself is not without its pitfalls. Problems arising in the on illiquid and non-traded assets 14 are aggravated by the fact that, in some respects, real options fundamentally differ from financial options. The main areas of concern are: • The unique individual and systemic (i.e., locational and contractual-relational) charac• teristics of many real assets make their payoffs difficult to replicate and a perfect hedge less likely. Thereby, illiquid and inefficient (not necessarily -free) mar• kets together with high trans action costs could theoretically underrnine the real op• tions approach to investment. The absence of comparable benchmark assets, however, equally mIes out traditional valuation techniques. • Next to market risk, real investments (especially in R&D) are often subject to severe technical risks typieally assumed to be idiosyncratic and diversifiable. 15 For example, the assumption of a fully-diversified investor may not always be appropriate. Moreover, the types and sources of uncertainty deterrnine the valuation approaches managers need to take; 16 and they deterrnine whether additional uncertainty will create or destroy value. 17 • The problem of endogeneity arises as a result of active management and innovation. 18 The very nature of real options lies in the strategic adaptability they create. Inten• tional variability in real options extends far beyond the manipulation of input param• eters19 and includes the creation, evolution, and destruction of these claims together with their underlying assets. • Shared ownership and competition have a significant influence on the structure and value of real options.20 While authors of earlier papers ignored this aspect or lirnited their analyses to a conceptual treatment of the problem, recent research indicates that competitive effects may not only have a non-trivial impact but may even reverse the results of previous investigations. Specifically, a strong first-mover advantage will, under certain circumstances, create a compelling incentive for pre-emption and render the option value of waiting insignificant.21

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• Option interactions and compoundness introduce additional complexity.22 Even if it were computationally feasible to account for all mutual influences, there would still be considerable uncertainty about the exact nature of these effects. In addition, this problem is not necessarily mitigated by the fact that the marginal contribution of addi• tional (compound) options decreases in the number of options already included.23 • Bounded rationality and incomplete information give rise to "uncertainty about uncer• tainty". Investors may for example not be able to observe required input parameters as these are obscured by noise limiting the stock market's ability to correctly price embedded options and to predict proper exercise terms. This may lead to unexpected outcomes in the valuation and exercise of options.24 As illustrated by these examples, practitioners face a multitude of methodological chal• lenges not encountered when using traditional valuation techniques. All this said, the theory of investment under uncertainty still represents the first-best instrument in an environment with considerable uncertainty and flexibility. To use an allegory, valuing managerial flexibilities is like hunting deer grazing in the far distance. The hunter can first try to shoot the animal with his revolver, but the bullet will fall to the ground before even reaching the target. To get a shot at the deer the hunter needs to pull out his hunting rifle. Even if fog happens to blur the hunter's vision, he now has at least a chance of bagging the deer. Alternatively, the hunter could approach the animal to increase the effectiveness of the revolver and get a clearer view. This advan• tage comes at the risk of the deer sensing the threat and escaping into the forest.

111. Outline of the Paper

The remainder of the paper is structured as follows: Sect. B covers the roots of the real options literature, i.e. the definition and valuation of individual real option rights as weH as ways of accounting for interactions in multi-option valuation exercises. Natural resource applications have historically been one of the main areas of real options research - primarily because of the availability of market data for deterrnining the value of the underlying and its . Sect. C outlines the development of this strand of the litera• ture. Sect. D contains a discussion of real option applications to international business, in particular corporate risk management. Operative flexibility grants management the right to adjust its operations in response to price movements (in particular currency rates). Nu• merous contributions have addressed the issue of how operative flexibility affects a firm's exposure profile and how operative hedging strategies interact with financial pol• icy, especially financial hedging activities. R&D valuation has been at the forefront of real options research in recent years. Sect. E contains an overview of seminal contributions proposing ways of capturing multiple sources of uncertainty (e.g. market and technologi• cal risk) for multi-option projects. Topics at the intersection of real options and the for• mal analysis of strategic decision-making are discussed in sect. F. As described in sect. G, the real options literature has also contributed to the advancement of option pricing theo• ry in various ways. In particular, a variety of refinements of numerical procedures have been proposed to value complex option rights more efficiently. Sect. H provides the read• er with an assessment of the extent to which the real options method has gained accep-

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tance by corporate practitioners and of how the literature has addressed their specific needs. Sect. I concludes.25

B. Foundations: Capturing the Value of Managerial Flexibility

I. Setting the Stage: Classification of Individual Real Options Rights

The real options approach captures management's ability to adapt its strategies in response to unexpected developments or the resolution of uncertainty over time. Identifying and classifying different forms of decision-making flexibility is therefore a logical starting point for academic research and, until today, the very foundation of the real options literature. Several insightful reviews tracing the beginnings of real options research al• ready exist,26 so that the subsequent discussion will focus on introducing the basic real options typology.27 Undoubtedly, the most widely known insight from real option theory is the invalidation of the popular net (NPV) rule. Partial equilibrium analysis shows that in• vestments are only undertaken if a positive NPV compensates for the flexibility foregone by committing to a particular project. Every investment competes for funds not only against alternative investments, but also against itself, postponed into the future. These opportunity costs give rise to the option value of waiting/deferring.28 Due to the asymme• try (convexity) of its payoff profile, the value of a is increasing in the variabil• ity of the underlying asset value. Contrary to common intuition, uncertainty can thus serve as a value driver, arguably the most important of the six levers determining the price of financial and real options. 29 However, as will be described in further detail below, the presence of implementation uncertainty and competition puts this result somewhat in perspective. Project values therefore are to be interpreted as an expanded NPV encom• passing the passive NPVas well as the option value. 3o Most investments are carried out in stages, giving management the flexibility to termi• nate the project if certain milestones have not been reached. The actual generation of cash flows is conditioned on the exercise of a sequence of call options on entering the next investment stage or, simply, a . While MajdIPindyck (1987) con• sider such options to stage in the context of sequential investments which either take time to build or permit only a maximum rate of investment, the applicability of this option type is most obviously given for pharmaceutical R&D projects.J! In most cases, management has the ability to rescale existing activities in response to environmental changes, i.e. it has the option to either expand by exercising a call option on additional fixed inputs or contract by exercising a on existing fixed inputs.32 In the latter case, management can of course go even further by exercising an option to temporarily shut down and restart33 or an option to abandon the project altogether. 34 While the latter is to be interpreted as a put option, the former represents a call option on operative cash flows with the variable costs being the . Kulatilaka (1995) points out that the aforementioned forms of flexibility can also be seen as generalized options to switch corresponding to the joint exercise of a put option on an existing strategy or activity and a call option on another, or a socalled exchange

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option.35 Speeifically, value chains are frequently designed to give firms either product flexibility to alter the output mix in response to demand shifts or process flexibility to modify the input mix in response to factor price changes.36 While the real options approach may be narrowly interpreted as a valuation tool, it actually represents a general management philosophy.37 Real options offer a much richer picture of the factors enabling firms to grow and seize competitive advantage across markets than, for instance, Porter's framework of competitive forces. Firms unlock growth opportunities by carrying out (a sequence 01) preparatory investments eventually leading to market entry. Essentially, these opportunities can be described in terms of (compound call) options to innovate.38 Next to these basic real option types, the more recent literature has generated application• speeific variations, e.g. the option to shelve or accelerate an R&D project,39 and lesser known option types such as the option for corrective action40 to improve an investment's economic prospects. All forms of managerial flexibility discussed in the literature can how• ever be grouped into three categories on the basis of their distributional impact on future cash flows: learning options perrnit management to respond to the resolution of uncertainty (op• tions to wait, to stage investment or to switch), insurance options enable management to shield the firm from negative environmental changes (e.g. options to abandon, to shut down, or to switch) and growth options put management in a position to exploit the emer• gence of new business opportunities (e.g. options to expand or to innovate).

11. Inter-Linkages: Interactions Between Real Option Values

Valuing real options on a stand-alone basis is of limited practical relevance as most projects represent a combination of various learning, insurance, and growth options. Their values may interact in one of two ways when considering the sequence of option rights embedded in a particular project.41 The presence of subsequent options raises the value of the underlying and therefore affects the payoff structure of prior options (first• order or backward-looking interaction) while the exereise of prior options alters the value of the underlying and therefore impacts the payoff structure of subsequent options (second-order or forward-looking interaction). Generally, it holds that • the presence of prior options increases (reduces) the value of subsequent calls (puts), • the magnitude of interaction is positively correlated with the probability of joint exer• eise, • the magnitude of interaction is negatively correlated with the degree of separation of their exereise times,42 • options of the same type (e.g. both calls) interact more strongly than options of the opposite type (e.g. call and put), • the marginal contribution of additional options included in the analysis decreases since the net interaction effect tends to be negative.

Recent contributions primarily distinguish themselves from the early literature by taking a real-world case as the starting point, which typically requires the consideration of multiple options.

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111. Comment on the General Structure of the Advanced Real Options Literature

Before reflecting on the contributions of real options research in particular topic areas, it is helpful to highlight the unique and differentiating aspects of the literature. Overall, contributions can be clustered along eight distinct dimensions. • While most contributions take a normative approach to identifying the value con• tribution of managerial flexibility, an increasing number of papers attempts to analyze empirically the degree to which the presence of option rights is reflected in market prices.43 • As practitioners become increasingly interested in the real options method, general valuation models have been complemented by a considerable number of case studies in the recent past. • The literature reflects the fact that the real options method can be applied at two levels, as a qualitative instrument to characterize managerial degrees of freedom ("option thinking") or as a tool to formally quantify the value of such option rights. • While most contributions focus exclusively on applying the real options method to a particular valuation problem, a number of authors explicitly analyze the performance of real options approaches relative to traditional methods. • Within the narrow class of quantitative real options analysis, the literature can again be classified along four dimensions. - Contributions exarnine application-specific variations of basic real option types con• sidering different combinations of learning options, insurance options, and growth options. - Valuation models capture different sources of uncertainty, incIuding e.g. technological and cost uncertainty next to market risk. - Authors use different techniques to assess the contribution of managerial flexibility to firm value. If the assumption of market completeness holds, the valuation model can employ a straightforward "equivalent martingale" approach to measure option values, otherwise one needs to resort to equilibrium approaches. The applied literature often ignores this aspect and generally uses numerical methods (binomial or trinomial trees, finite differences) without first developing a continuous-time framework. - Finally, real options research is increasingly interlinked with the literature on corpo• rate strategy and industrial organization. Rather than treating the firm as an inde• pendent decision-making unit, recent contributions merge the options view with game-theoretic treatments to capture the effect of strategie interdependence.

C. Integrating Market Signals: Applying the Real Options Approach to Natural Resource and Real Estate Investments

I. Oil and Gas, Mining, and Forestry

At the heart of the real options approach to investment lies the idea of valuing risk and flexibility in accordance with organized markets where continuous trading and liquidity

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provide for reliable (spot and forward) price data. Because commodity markets are - by definition - among the most efficient of markets, it appears only logical that seminal contributions to the real options literature originated in the area of natural resource in• vestments. For example, price data are readily available for crude oil. More importantly, heavily traded derivatives make it possible to compute the implied volatilities needed for accurate option pricing. Schwartz (1997) exarnines the stochastic behavior of commodity prices using three differ• ent models, a one-factor, a two-factor, and a three-factor model. An extension that accounts for the effects of incomplete information44 is proposed by Bellalah (2002). The pecularities of commodity markets typically require the valuation of contracts contingent on assets whose prices follow alternative diffusion or jump processes, e.g. so-called Ornstein-Uhlen• beck processes that exhibt mean reversion.45 Tourinho (1979) is the first to use option pricing to value reserves of natural resources. McDonald/Siegel (1986), Siegel et al. (1987), and Paddock et al. (1988) focus on the petroleum industry. They consider natural resource reserves under oil price uncertainty as an option to delay extraction. McDonald/Siegel (1985) and Brennan/Schwartz (1985) in• vestigate the option to shut down (and restart). Mackie-Mason (1990) presents an option pricing model involving the nonlinear taxa• tion of mining firms. Cortazar/Schwartz (1998) use Monte Carlo simulation to value an undeveloped oil field. SmithlMcCardle (1998) integrate option pricing and decision ana• lysis. They propose modeling the logarithm of the oil price (instead of the price itself) as mean-reverting. 46 Miltersen (2000) carries out an analysis along the lines of AminiJarrow (1991) and shows that stochastic convenience yield may have a significant value impact. Cortazarl Casassus (2000) develop a multistage model of natural resource investments based on pre• vious work by Cortazar/Schwartz (1993). Dias (2001, 2002) explains how risk can be re• duced by purposefully acquiring information that reveals the relevant characteristics of a reserve. Using optimization techniques that involve genetic algorithms and Monte Carlo methods the author derives the optimal information acquisition policy. Cortazar et al. (2001) develop a real options model for valuing natural resource explora• tion investments when there is joint price and geological-technical uncertainty. The authors consider several stages together with their corresponding real options and apply the model to a copper exploration project. McCormackiSick (2001) report how a consulting firm was able to develop a specialized model to value an extensive portfolio of drilling opportunities in the form of proven undeveloped reserves (PUDs). Other contributions in tbis area include papers by Teisberg (1981), MasonIBaldwin (1988), Trigeorgis (1990), Smit (1997), and Cortazar/Schwartz (1997). The partial liberalization of energy markets has quickly created a fast growing market for energy derivatives.47 Although, after the Enron debacle, the industry is now undergoing consolidation, contemporary methods of energy management continue to rely on option pricing techniques. An extensive overview with applications is provided by Ronn (2003). MoellTufano (2000) analyze the right to develop a mine after completing exploration using Monte Carlo methods. Cortazar/Casassus (1998) discuss the optimal timing of mine expansion.48 Forestry resources are the subject of several articles, including, for example, the contributions by Mjijrck et al. (1989), Gjjijlberg/Guttormsen (2002), and Insley (2002).

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11. Real Estate

Another important application of the theory of investment under uncertainty first sug• gested by Titman (1985) lies in real estate markets. Further analyses are carried out, for example, by Williams (1991) and Capozza/Sick (1994). Quigg (1993) shows, using a sampie of 2.700 land transactions in Seattle, that the options approach performs better than the standard neoclassical models in explaining the real investment process. Quigg (1995) uses a real options analysis to value empty land and finds that buyers are willing to pay a premium for undeveloped land in the hope that the market conditions change to make the land more valuable. Recent evi• dence from the U.K. property market is presented by SingaIPatel (2001). Lucius (2001) summarizes the literature on real estate option models and gives abrief intro• duction. Grenadier (1996) extends real estate models to a strategic setting laying the groundwork for future investigations into the cyclical nature of real estate markets. A more recent game-theoretic analysis of commercial real estate lease contracts is pro• vided by Grenadier (2003).

D. Exploiting the General Ability to Switch: Optimal Usage of Strategy Portfolios

I. Adjusting Operations: Valuation of Production Flexibilities

An intuitive way of modeling complex real options is to describe strategies as modes of operation. Option exercise can then be seen as being equivalent to (costly) switching between different modes implying path dependency, or hysteresis.49 This approach reflects the interpretation of Kulatilaka/Trigeorgis (1994) that all real options can be captured in a general switching option framework. The value-enhancing role of the ability to switch is discussed in this section, first, in the context of production flexibilities and, second, by examining the relevance of real options for the choice between financial and operative hedging instruments and the implications for a firm's financial policy. Issues related to production management have appeared at an early stage on the agenda of real option researchers.50 An important strand of this literature has emphasized flex• ibilities associated with a firm's capacity choice or, more generally, its accumulation of capital. Without diminishing the relevance of these contributions,51 this section will rather highlight the value-enhancing role of switching capabilities, either in the form of factor or product flexibilities. Probably the most influential study in this area is the one by Kulatilaka (1993) who analyzes the incentives to invest in energy generation technology with flexible vs. fixed input usage in the face of factor cost uncertainty. The firm may deviate from the technol• ogy choice statically minimizing production cost once input switching costs, economic depreciation and repair as weH as upgrade cyc1es are considered. LilKouvelis (1999) study factor flexibilities in the context of flexible sourcing arrangements and solve for optimal risk-sharing contracts with suppliers.

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A number of authors have focused on investments in technologies with embedded out• put flexibilities. Most prorninently, TriantislHodder (1990) model the ability to switch between different outputs in the presence of downward-sloping demand and rising mar• ginal costs as a complex option. An obvious application of this concept are investments in production platforms common in the automobile industry and technology-focused bio• tech firms. Van Miegham (1998) extends the analysis and shows that multi-product tech• nologies may enable fmns to manage their exposure to demand uncertainty. Baldwinl Clark (1997, 2000, 2001) relate real options theory to the modularity of product design, Le. the adoption of a "Lego-type" design enabling firms to replace individual compo• nents without being forced to redesign the entire product.

11. Dealing with Risk Exposures: Real-Option-Based Hedging Approaches

Central to this strand of the literature is the question to what extent financial and opera• tive hedging strategies are to be seen as strategic substitutes or complements.52 The value of real options becomes most apparent when analyzing the shift of production activity in the presence of currency risk. 53 As the relative costs (measured in terms of horne currency) of producing in a certain facility is lowered, for instance as a result of cur• rency depreciation, it becomes optimal to relocate production to this site if there is ex• cess capacity. KogutIKulatilaka (1994a) are the first to formally analyze this problem within a real options framework. They show for the two-plant case that the value of the option to shift production may provide for sufficient compensation to set up such a simple production network even if each plant has a negative NPY. Their work is build on CohenILee (1989) who analyze optimallocation and scheduling decisions within produc• tion and distribution networks on the basis of a cost rninirnization rationale but without explicit reference to exchange rate variability. It is extended by Huchzenneier/Cohen (1996) and Huchzermeier (2000) to estab1ish the value of global production and supp1y chain networks enabling the firm to respond dynarnically to exchange rate changes. A10ng sirni1ar lines, DasuILi (1997) exarnine optimal production adjustment policies con• ditional on switch-over costs enabling them to derive an explicit value for investing in excess capacity. Another strand of the literature leading to the issue of how to construct the optimal hedge is the option-based analysis of foreign direct investment decisions. Buckleyffse (1996), Buckley/Casson (1998) and RivolilSalorio (1996) employ a qualitative real op• tions framework to highlight the benefits derived from flexibility implied in FDI. Luehrman (1990), GoldberglCharles (1995) and MillerlReuer (1998) support the argu• ment empirically by showing that the willingness to engage in FDI increases with exchange rate uncertainty and may be further enhanced by additional sources of corre• lated risk. A game-theoretic analysis of FDI under uncertainty is provided by Smets (1991). ChowdrylHowe (1999) are the first to discuss the linkage between financial and opera• tive hedging. They argue - without explicitly referencing real options - that investing in operational flexibility requires the presence of non-hedgeable sources of risk (e.g. local demand risk). Hommel (2003) extends the analysis to show that real option considerations

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may provide sufficient incentive to go beyond mere geographieal diversification and en• gage in production shifting conditional on the availability of financial hedging even in the absence of non-hedgable sources of risk. Doing so leads to asymmetries in the corpo• rate exposure profile and supports the use of asymmetric financial instruments for hedging purposes. Mello et al. (1995) treat the issue in a more general financial contracting frame• work with switching costs to show that the agency cost of debt can create a linkage between operative and financial hedging while BrealeylKaplanis (1995) demonstrate that operational flexibility may also trigger financial hedging with short-term instru• ments.54

E. Expanding the Risk Exposure Universe: Valuation of R&D Activities

I. Safe Bets, Long Shots, and Sequentiality

The valuation of R&D activities based on real options analysis has attracted the partieu• lar attention of practitioners, which in turn has triggered substantial amounts of applied research. Real options applications have frequently stimulated academic discussion by challenging unrealistic assumptions and raising new issues related to important areas of more fundamental research such as incomplete markets, incomplete information and op• tion pricing under competition. SchwartzIMoon (2000a) state that "the analysis of invest• ments in research and development (R&D) is surely one of the most difficult problems in investment under uncertainty". Consequently, this field of research deserves particular attention. R&D managers typieally face considerable uncertainty when allocating available funds to competing projects. Due to the strategie relevance of R&D, it can be dangerous to base managerial decisions on statie pricing models that do not capture the flexibility inherent in these projects and the interactions between them. In search for a holistie approach to R&D management, decision-makers have resorted to more qualitative proce• dures including different types of scoring models and checklists complementing the basie (NPV) criterion. In addition, decision tree analysis (DTA) and Monte Carlo simulation have been proposed as remedies to the increasing gap between static financial models and a strategie view of R&D. However, both methods suffer from some serious drawbacks of their own. A combination of DTA and Monte Carlo simulation delivers results that are sometimes difficult to interpret and, most importantly, rely on often arbitrary assumptions about utility (preference).55 Although finance theory has long recognized that the systematie and unsystematic components of risk should be treated separately, none of these methods distinguishes clearly between technieal and market risk. Nor does the uniform discount rate commonly used in classieal decision trees reflect how the risk profile of an R&D project is altered by the resolution of uncertainty and managerial decisions over time. In contrast, this adjustment is implict in risk-neutral valuation and, at least in theory, makes real options a suitable tool for valuing and managing R&D activities. While discussions surrounding the theory of investment under uncertainty have certainly contributed to the increasing acceptance of conventional decison-theoretic models among R&D managers, real options

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analysis continues to provide significant advantages over related techniques for flexible planning. Lint (2002) makes two important points in favor of real option valuation in R&D. Firstly, the analogy between financial option pricing and the option-based valuation of R&D pro• jects has strong managerial appeal. After all, every successful R&D project represents an option on future market introduction. An initial investment opens up the opportunity for follow-up investments eventually leading to a marketable product. Secondly, the capital markets' growth expectations are high for R&D-focused firms. At the same time, share• holders increasingly demand positive cash flow in the short term. In order to balance these dual requirements, R&D managers need an integrated view of both technical and market uncertainty. Only then both comparatively low-risk projects with immediate positive re• turns ("safe bets") and high-risk projects with significant upside potential ("long shots") receive their fair share of attention. 56 Generally speaking, R&D projects and the commercialization of completed projects are special cases of sequential investment decisions. Beyond R&D, sequential invest• ment decisions have also been discussed in the context of venture capital57 and natural resources.58 A feature common to an of these applications is a potentially considerable lag between the initial investment and later stages of the project (e.g. production) actu• ally generating profit. Earlier models exarnining path dependency within a real options framework are presented by MajdIPindyck (1987, 1989). In the literature, there are sev• eral general frameworks with complex investment decisions.59 More recently, Bar-Hanf Strange (1998) and Friedl (2002) focus explicitly on the issues of time-to-build and sequential investment. Another example is the model by Bar-Han et al. (2002) which combines time-to-build with the problem of capacity choiee originally addressed by Pin• dyck (1988). Game-theoretic models demonstrate that time-to-build is also an important parameter for strategie investment under uncertainty.6O It affects the the trade-off be• tween pre-emption and the option value of waiting and gives rise to novel types of equi• libria. 61

11. Growth and the Theory and Practice of R&D Management

From the very beginning researchers have recognized the dose connection between se• quentiality (staging) and growth.62 On the basis of laboratory experiments, Howell/Jägle (1997) condude that the correspondence between management's intuition and real growth option theory is only weak and approximate.63 In contrast, McDonald (2000) shows that seemingly arbitrary mIes of thumb like hurdle-rates and profitability indices may in fact lead to investment decisions that are dose to optimal. These results under• line the importance of further research. The challenges are twofold: Firstly, real options models will have to be refined to better reflect the intuitive judgement of practitioners, should there be evidence that common practices are value-enhancing. Secondly, man• agers will have to be educated in applying those real options models that are proven to be in line with financially and strategieally sound investment policies. R&D provides an ideal setting for such investigations into the relationship between economic theory and managerial practice.

ZfB-Ergänzungsheft 3/2004 13 Philipp N. Baecker and Ulrich Hommel

MitchelllHamilton (1988) and Mitchell (1990) are among the first to stress the strate• gie importance of a real options approach to R&D. Faulkner (1996) highlights the impor• tance of options thinking as opposed to quantitative methods. Correspondingly, McGrath (1997) tries to bridge the gap between managerial thinking and formal methods by trans• lating verbal intuition into approximate option values. Hurry et al. (1992) conduct one of the first empirical analyses focused on flexibility and venture capital investments. Early papers like the one by NewtonIPearson (1994) are often based on the Blackl Scholes formula. Pennings/Lint (1997) and LintIPennings (1998) consider a modified version of the formula where the variance of the underlying asset is replaced by an indicator representing the frequency and size of business shifts and apply it to R&D projects at Philips.64 Their approach is related to the one taken by Willner (1995) to value start-up venture growth options. GrenadierlWeiss (1997) and, more recently, Bemardo/Chowdhry (2002) focus on the experience gathered from investing: An option to invest always implies an option to leam about a new technology which has an impact on the option value of waiting.65 Bemardo/Chowdhry (2002) predict that a firm will follow a certain life-cycle and offer a novel explanation for the diversification discount based on growth options.66 Grenadierl Weiss model technological progress as a Brownian motion process. The paper represents an early contribution to research on technology adaption which is outlined in subsect. E.III. Weeds (1999) presents an R&D model with economic and technological uncertainty that includes the model by McDonaldlSiegel (1986) and the entry and exit model by Dixit (1989a) as special cases. The Weeds model and specifically the phenomenon of reverse hysteresis is analyzed further by Tsekrekos (2001) who shows under which con• ditions firms actually abandon projects despite positive NPV. Childs/Triantis (1999) employ numerical methods to derive the optimal investment strat• egy for an R&D program consisting of multiple interrelated projects. Using the example of pharmaceutical research, Perlitz et al. (1999) compare three option pricing models with respect to their suitability for R&D applications and arrive at the conclusion that the formula developed by Geske (1979b) is best suited for capturing the value of staged investments, especially if the problem in question can be simplified sufficiently. How• ever, Jägle (1999), who values a similar project using binomial trees, points out that, especially in the case of technology-intensive companies, externat flexibility stemming from strategic alliances is a major value driver. McGrathlMacMillan (2000) investigate the option-based assessment of technology projects from a primarily managerial perspective. They bring attention to the impor• tance of complementing real options with appropriate controls when deploying the approach in organizations. The authors see real options as a valuable complement to other decision-making frameworks such as discovery-driven planning67 and emphasize the need for deliberate learning in order to resolve uncertainty during the course of the R&D project. Angelis (2000) extends ideas by Morris et al. (1991) and develops a simplified method possibly suited for obtaining rough estimates of option prices based on predictions of cost and revenue. Although simple formulae generally are a useful complement to option thinking. Boer (2000) states that R&D projects and venture capital investments are typi-

14 ZfB-Ergänzungsheft 3/2004 25 Years Real Options Approach to Investment Valuation

cally too complex to be captured by such models. Private risk, for example, reflecting the possibility of negative outcomes due to internal factors such as technological failure, poor management, the loss of key staff or liquidity problems should not be neglected. Jacob/Kwak (2001) thereby conclude that additional project risk management processes are required in order to benefit from the potentially significant improvements in project selection, project review, and resource allocation decisions offered by the real options approach. At the textbook level, a framework that allows for an integrated approach in the presence of simultaneous technical and market uncertainties in R&D is described by Copeland/Antikarov (2001). Smith/Nau (1995) and Smith/McCardle (1998) provide a more detailed analysis. Apart from cristicism grounded in theoretical arguments, Doctor et al. (2001) and HuchzermeierlLoch (2001) agree in the necessity of documenting real-world projects and presenting actual R&D options on which a valid and reliable assessment could be based. Essentially clarifying the important distinction between mere choices and options, Huch• zermeierlLoch elaborate on the conditions under which an increase in uncertainty creates additional value. If uncertainty is resolved after all decisions have been made, as in the case of a simple bet, more variability is likely to "smear out" contingencies and thus reduce the value of flexibility. Their model builds some intuition for R&D managers as to when and where it is not worthwhile to delay commitments. A fairly recent issue of R&D Management features several noteworthy articles on the real options approach to investment. For example, LeelPaxson (2001) take an ambitious approach to the problem of R&D valuation and propose American exchange options as a means of pricing multi-staged e-Commerce investment projects. Jou!Lee (2001) examine the relationship between R&D investment decisions and optimal subsidies. Empirical evidence on venture valuation is provided by Seppaa/Laamanen (2001). Ziedonis (2002) uses data on firms' technology licensing decisions to analyze factors influencing option purchase and exercise decisions. Hsu (2002) generalizes the formulae by BlackiScholes and Geske to investigate the staging decision of aventure capitalist in a principal-agent framework and provides an explanation for empirical evidence on the staging of venture capital investment. A case study approach is taken by Lint (2002) who describes how option valuation was used in real R&D settings. The author en• courages the use of more advanced pricing models and suggests a combination with scoring methods in order to keep the analysis realistic, broadly understandable, and man• ageable. Finally, MacMillanIMcGrath (2002) introduce a highly practical and intuitive tool for properly assessing R&D project portfolios while relying on less formal methods. Using an illustrative example they demonstrate how risky R&D projects can be classified into three categories of real options: positioning options, scouting options, and stepping stones.

111. Models of Technological Change and Technology Adoption

Another important strand of the real options literature focuses on R&D deals with the interrelated issues of technological change and technology adoption. Bridges et al.

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(1991) provide an overview from a decision-theoretic point of view while Huisman (2001) takes an explicit real options perspective and provides several extensions to pre• vious models. Related is the work of Baldwin (1982) representing an early example of sequential investment analysis in which the option value of waiting is examined. As pointed out by Huisman, different phases in the Baldwin model can be interpreted as technology upgrades and the state variable as an indicator of the current technology's efficiency. Other real options models of this kind inc1ude Purvis et al. (1995), DosiJMoretto (1997), and Ekboir (1997). Purvis et al. as weH as DosiJMoretto examine the case of a single new technology. DosiJMoretto focus on firm incentives to accelerate technological change and find that a lower degree of uncertainty is more likely to result in increased investment than areduction of upfront investment costs. Ekboir analyzes the effect of technological change on a firm's capital stock in a setting with partial irreversibility. As in the somewhat related model by DixitIPindyck (2000), who treat the general case of lirnited expandability and irreversibility under stochastic demand, the author derives upper and lower bounds triggering investment and disinvestment, respectively. These bounds are influenced by the arrival of new technologies. Variations in capital productiv• ity cause the firm to adjust its capital stock accordingly. Farzin et al. (1998) develop a model allowing for multiple technology switches.68 The arrival of new technologies is captured by a Poisson jump process. Taking the option value of waiting into account, there is a signficant difference in the adoption pattern proposed by the NPV criterion and the optimal pattern derived - a widely known implica• tion of the real options approach to investment. Sirnilar results are obtained by Huisman (2001) who presents a generalization of the abovementioned model. Also Baudry (1999) and Mauer/Ott (1995) use Poisson processes to model the arrival of new technologies. In the first case, the new technology happens to be less poHuting; in the second case it enables the firm to reduce maintenance and operating costs. As PawlinaIKort (2oo1a) point out, this approach to modeling is only advisable if the firm has no insight in the innovation process. Continuous observation of technological progress can be accounted for by assurning that technologies are invented as soon as a state variable representing progress crosses a certain threshold.69 Under this assumption firms possess some, albeit imperfect, information. Researchers have recently been investigating ways to integrate such models with game theory. A more detailed account of these contributions is given in sect. F.

IV. Selected Industry Applications

1. Flexibility and Value in Pharmaceutical R&D For slightly different reasons, investments in pharmaceutical R&D as weH as in infor• mation and communication technology (ICT) have been widely studied as possible applications of the real options approach. Biotechnology and the Internet create unpre• cedented challenges for firms operating in an environment characterized by substantial

16 ZfB-Ergänzungsheft 3/2004 25 Years Real Options Approach to Investment Valuation

uncertainty and heightened competition. Strategic and operating flexibilities are numer• ous and difficult to capture in more or less static financial models. In the following, references to practical applications serve to illustrate the progress made and directions for future research. Pharmaceutical R&D is strongly regulated and for a single drug (candidate) best de• scribed as a sequential new product development (NPD) process. Typically, R&D is divided into different stages (pre-clinical trials, clinical phase 1-3, certification stage) at the end of which a continuation decision is made. As shown by LintJPennings (2001), the option approach establishes a foundation for a holistic phase-review NPD system which is ideally suited for dealing with market and technology uncertainty surrounding the new producl. Jägle (1999) discusses in some detail the distinct advantages and disad• vantages of formal stage-gate approaches to R&D management1° and outlines the speci• fic relevance of such models for real option analysis in pharmaceutical R&D. Furthermore, several authors examine capabilities71 and platform investments72 in terms of real options providing the basis for a structured analysis of technology platforms and their role in developing a pharmaceutical fmn's pipeline. Reports of Merck adopting an evaluation procedure partly based on real options73 have certainly augmented awareness among practitioners and academics. Decision-theoretic models and portfolio analysis with Monte Carlo methods represent the current state of the art in pharmaceutical R&D valuation. On the one hand, the industry seems to main• tain its reservations regarding genuine option-theoretic models. On the other hand, as a result of staggering productivity in R&D and mounting competitive pressure exerted by lower cost generics, the importance of licensing has been steadily increasing. Since op• tions are in many cases explicitly built into the deals signed between Big Pharma and biotechnology firms, licensing and various other types of cooperative agreements lend themselves to an option-based analysis. Early contributions in this comparatively new field of research include the work by Ottoo (1998) who values a biotechnology firm using a modified BlacklScholes formula and Micalizzi (1999) who applies the model of sequential investment by DixitJPindyck (1994) to pharmaceutical R&D. Kellogg/Charnes (2000) value a biotechnology company using decision tree analysis and a binomial real options model relying on data collected by MyerslHowe (1997). Investigating comparative advantages of the US to explain its leadership in biotechnology Lavoie/Sheldon (2000) find that differences in the maximum per-period rate of investment and regulatory uncertainty offer a plausible explanation based on the real options approach. Various types of uncertainty are incorporated into a model by SchwartzlMoon (2oo0a) who analyze R&D investments based on Pindyck (1993). The authors use finite differ• ence methods two derive a solution to the resulting two-dimensional equation. Schwartz (2001) examines a similar problem and employs Monte Carlo simulation for American options 74 to determine optimal stopping times. Such novel numerical techniques allow MiltersenlSchwartz (2002) to derive important policy implications by extending the ana• lysis of pharmaceutical R&D to a setting with two firms investing simultaneously. As in the above mentioned models, the R&D process passes through several stages and is sub• ject to risk stemming from technology, input costs, demand, and the possibility of cata• strophic events.

ZfB-Ergänzungsheft 3/2004 17 Philipp N. Baecker and Ulrich Hommel

BracbIPaxson (2001) examine a Poisson option model for a gene to drug venture. LochIBode-Greuel (2001) demonstrate how to integrate decision analysis and option pricing for valuing growth options in pharmaceutical R&D if markets are only par• tially complete. Empirical work on real options in R&D is done by Robinson/Stuart (2002) who conduct a detailed, micro-level study of strategie alliance agreements many of which resemble venture capital contracts involving typieal elements like con• vertible preferred equity. McGrathlNerkar (2002) analyze a large sampie of by firms active in the biotechnology industry and find that R&D investments patterns are consistent with real options theory. As previosly pointed out, MacMillan/McGrath (2002) provide a good option analysis framework at a comparatively low level of technicality. Opportunities for future research include the valuation and management of multiple projects, competitive effects, and the empirical validation of option pricing models in pharmaceutical R&D.

2. Information and Communication Technology What later became known as the "Internet bubble" began to emerge in the early 90s. Managers, investors, and analysts were struggling for a structured approach that would allow them to value companies and technologies in the face of high uncertainty. The strategic significance of investments in information and communication technology (ICT) had increased tremendously. While the associated costs experienced a similar increase, these developments facilitated the creation of continuously evolving networks of alli• ances by providing a robust and scalable infrastructure. In some cases, the advent of Internet technologies started to fundamentally change the way business was being con• ducted. The theory of investment under uncertainty seemed to provide a way to separate the hype from the few real opportunities and bring financial discipline to strategic decision making. The paramount importance of modularity in modem software engineering75 as wen as the incremental nature of most large ICT projects further motivated research in this still-burgeoning field. Today component technologies, new delivery mechanisms76, and the commoditization of bandwidth and computing power are likely to enhance flex• ibility. Increased flexibility, in turn, will continue to produce a strong need for adequate financial models. Early examples of applications to ICT investments include the contributions by Cle• monsIWeber (1990) and Clemons (1991) who identify key risk factors including firm• specific risk, competition risk, and market risk. Dos Santos (1991) is among the first to propose the use of exchange options77 in this context. Kambil et al. (1993) use the Cox et al. model to quantify the benefits of introducing palmtops to improve business pro• cesses in a large hospital. Kumar (1996) again uses the Margrabe formula to quantify the value added from decision support systems (DSS) in a variety of scenarios, including marketing and commodity trading. The results are compared to those obtained with the BlackiScholes formula. McGrath (1997) analyzes the issue of technology investment from a strategic perspective and concludes that traditional capital budgeting tools such as internal rate of return (IRR) or NPV are inadequate because they make it difficult to cope with the high uncertainty inherent in most ICT projects.

18 ZtB-Ergänzungsheft 3/2004 25 Years Real Options Approach to Investment Valuation

Taudes (1998) improves over previous contributions by explicitly considering growth option and thus laying a foundation for the analysis of ICT platform investments. The author presents a real-life case study on upgrading enterprise resource planning (ERP) software. Also Panayiffrigeorgis (1998) account for sequentiality when modeling a two• staged infrastructure project for the state telecommunications authority of Cyprus. BenarochIKauffman (1999) apply the Black/Scholes formula to price a deferral option in the context of a case study involving the deployment of point-of-sale (POS) debit services. Furthermore, they use Black's approximation to incorporate the possibility of early exercise into their mode1. 78 ChatterjeelRamesh (1999) explain how option valuation techniques can improve risk assessment and management in the adoption of technologi• cal innovations and in software development. Kumar (1999) elaborates on DSS value from an option perspective. Kulatilaka et al. (1999) and Balasubramanian et al. (2000) develop a methodology for evaluating information technology infrastructure investments that links operating drivers to business capabilities and investment decisions. In bis dissertation, Zhu (1999) develops real options and game-theoretic models for IT investment decisions. The author is among the first to explicitly account for information asymmetry and endogenous competition. PerottiIRossetto (2000) present a game-theore• tic model of Internet portals as portfolios of entry options. Schwartz/Zozaya-Gorostiza (200ob ) develop separate models for IT acquisition and development projects along the lines of SchwartzIMoon (2oo0a). SchwartzlZozaya-Goro• stiza (2oooa) study investments in disruptive technologies79 by dividing an investment project into two sequential phases representing the evolution of the disruptive technol• ogy from an emerging to a mainstream market. SchwartzlMoon (2ooob) formulate a model for pricing Internet in continuous time and value Amazon using a discrete approximation and simulation techniques. The model is extended by SchwartzlMoon (2001) to inc1ude stochastic costs and future financing as weIl as capital expenditures and depreciation. Taudes et al. (2000) consider the case of software platform decisions as real options. Benaroch (200 1) illustrates the real options approach to investment in the context of a Web-based IT investment. ErdogmuslLa Barre (2000) show how stock prices provide estimates of the risk underlying an investment in software development. The authors give examples of tracking portfolios for Java and XML technologies. Erdogmus (2001) ana• lyzes license cost uncertainty in commercial off-the-shelf (COTS) software products. Erdogmus/Favaro (2002) explore the relevance of real options for a better understanding of extreme programming (XP). Kumar (2002) differentiates between risks that can be resolved by action and risks that can be resolved by hedging. The author calls attention to the fact that, in order to suc• cessfully manage risk in IT projects, managers need to choose risk reduction or risk hedging strategies where appropriate. Other recent applications in the field of ICT in• c1ude the articles by Kim/Sanders (2002) and Campbell (2002). In the aftermath of the Internet shakeout the real options approach to investment continues to be a valuable tool for assessing investments in information and communi• cation technology. For example, Buckley et al. (2002) demonstrate that a thorough analysis based on real options would have revealed a significant overvaluation of Nets• cape at the time of its IPO. The authors conc1ude that real options, if properly used,

ZtB-Ergänzungsheft 3/2004 19 Philipp N. Baecker and Ulrich Hammel

have "a place in the armoury of the equity investment analyst". In addition, the theory of investment under uncertainty brings substantial benefits to the table when it comes to managing and structuring project portfolios. As in the case of pharmaceutical R&D, theoretical and empirical work deserve equal attention among researchers and practi• tioners.

F. Flexibility and Strategy: Linking Real Option Valuation with Game Theory

I. From Partial to General Equilibrium

Although researchers have always been aware of the decisive role competition plays in determining a firm's optimal investment policy, most of them do not go into the details of this intricate issue. Instead, competitive interactions in real options models are largely treated as exogeneous factors and are incorporated by adjusting parameters of conven• tional pricing models. Possible adjustments inc1ude -like payouts80 or decreases in the value of the underlying asset, i.e. the net present value of the completed project. Examples of such partial equilibrium models are GrenadierlWeiss (1997), Childsffriantis (1999) and Schwartz/Moon (2000a). Combining the real options approach to investment with stochastic game theory makes competitive effects endogeneous. In a dynamic game not only the outcome but each agent's decision is contingent on the other agents' choiees. These strategie interactions may take the form of first-mover advantages and second-mover advantages. Correspondingly, investment timing (pre-emption games) and investment c10sure (wars of attrition) have been studied in various forms. Grenadier (2000b) gives an extensive overview of this fairly recent addition to the real options literature. A managerial treatment is provided by Grenadier (2000c). A more technical discussion of the game-theoretic real options ap• proach can be found in recent contribution by Huisman etal. (2003). While the value of real options is heavily influenced by competitive effects, financial options are widely held by agents external to the firm which makes it possible to neglect strategie aspects of option exercise. This peculiarity explains why finance experts, until recently, have shown a distinct lack of interest in combining game theory and option pricing. Few noteworthy exceptions exist in the area of warrants and convertibles.81 Unlike earlier models, most formal strategic analyses of investment under uncertainty cannot build upon concepts of financial option pricing that are readily available. Nonethe• less, researchers can draw from previous work in industrial organization on which most game-theoretic real option models are based. Future research promises to be of great value to academia and industry alike. After all, real options applications were originally con• ceived to bridge the widening gap between corporate finance and corporate strategy. Of particular interest is the trade-off between deferral options, growth options and the threat of pre-emption. As mentioned previously, the arguably most widely known result of real option theory is the invalidation of the NPV rule. Some analyses, however, indi• cate that competition may rapidly erode the option value of waiting.82 Also, as empha• sized by Grenadier (2000c), investment projects initiated with small option premia are more likely to turn out unprofitable because, in general equilibrium, there is less protec-

20 ZfB-Ergänzungsheft 312004 25 Years Real Options Approach to Investment Valuation

tive "cushion" against market downturns. Consequently, strategie real option models are more in line with the boom-and-bust cyc1es observable e.g. in real estate markets. Further research suggests that the net effect of uncertainty in oligopolistic markets is in fact ambiguous and also depends on the specific types of risk involved.

11. Origins and Computation

The paper by Smets (1991), which Dixit/Pindyck (1994, pp. 309-314) summarize in simpli• fied form in their textbook, is an early example of game-theoretic real option models. Based on the concept of Markov-perfect stopping equilibria developed by DuttaIRustichini (1993), the author extends the basie real options models by Brennan/Schwartz (1985), McDonald/Siegel (1986) and Dixit (1989a, b) to a strategic setting. Recently, the model has been extended in a variety of ways. For example, Weeds (2002) considers a model in which two firms may invest in competing R&D projects with uncertain returns. Similar models are investigated by Tsekrekos (2002a) and PaxsonIPinto (2002). While the dynarnics of oligopolistie industries are also studied by Fudenbergffirole (1986), their analysis is restricted to the deterministic case. Other deterministie models that form the game-theoretic foundation of many real options models considered here inc1ude papers by Reingaum (1981) and, partieularly, Fudenbergffirole (1985) who de• velop a formalism that permits the continuous-time representation of the limit of mixed• strategy equilibria. Extensions to the Fudenbergffirole model are proposed by Stenbackal Tombak (1994) and Hoppe (2000).83 Probably the first rigorous analysis of subgame-perfect equilibria in real options games is conducted by Williams (1993). Several authors propose extensions to the Wil• liams paper inc1uding Grenadier (1996) who considers a two-person model of the real estate market with two types of equilibria emerging: a sequential equilibrium in which development occurs over time and a simultaneous equilibrium, or "development cas• cade", in which both developers build at the exact same moment. Generally, game theory allows researchers to formalize many of the intuitive reservations which have led practi• tioners to reject predictions of the simpler models. The importance of strategie analysis in a real options context is made evident by CaplinILeahy (1998) and Moelffufano (2000) who discuss practical applications. SmitJAnkum (1993) introduce discrete-time game-theoretic models of sequential in• vestments into the literature. Their analysis of growth options provides simple numerical examples that illustrate many important results without using advanced mathematics. Baldursson (1998) assurnes linear demand and presents a simplified central-planning approach to solve a similar problem in continuous time. KulatilakaIPerotti (1998) discuss another discrete-time model of growth options and conc1ude that increasing uncertainty encourages investment in growth options if a strong strategic advantage exists. Their results are put into perspective by Pawlina/Kort (2002) who consider a slightly modified model in continuous time. Leahy (1993) finds that, under perfect competition, the equilibrium investment policy of a single firm equals the myopie policy of a firm that chooses to ignore the competi• tors' actions and their effect on the price process. The myopie trigger requires infinitely

ZfB-Ergänzungsheft 3/2004 21 Philipp N. Baecker and Ulrich Hommel

divisable investments and eannot be applied to diserete investments analyzed e.g. by Smets (1993) and Grenadier (1996).84 Grenadier (2002) extends the model by Baldursson (1998) ineorporating time-to-build into his analysis of eapacity options under eompetition. The author also gives a general deseription of how equilibria in oligopolistie markets ean be determined by transforming the demand eurve and proceeding as in the ease of perfeet eompetition. Slade (1994) provides the eonditions under whieh the equivalenee of Nash equilibrium strategies and the solution to a single optimization problem faeed by the the social planner holds. Possible applieations of this method inc1ude the analysis of stoehastie interest rates85 and exogeneous priee eeilings86 under eompetition. In addition, other authors propose numerieal methods as a method of analyzing timing games. 87

111. Current Issues and Opportunities

Many models require unrealistie assumptions about the simoltaneous exercise of options.88 This issue is addressed by HuismanlKort (1999). Thijssen et al. (2002) generalize the analysis to mixed strategies. Grenadier (1999) and LambreehtIPerraudin (2003), among others, eonsider models un• der asymmetrie information. Grenadier (1999) provides a Bayesian Nash equilibrium for real options markets in whieh agents update their believes by observing their eompeti• tors' exercise strategies. They find that the resulting information easeades provide a plau• sible explanation for herding behavior. LambreehUPerraudin (2003) foeus on pre-emption and attrition in investment games with winner-takes-it-all outeomes in whieh investment eosts are private information. Boyer et al. (2001), who also investigate the ease of multiple eapacity unit aequisitions, find that volatility and expeeted speed of market development play a erueial role in determining eompetitive behavior. They further prove the existenee of a taeit-eollusion equilibrium for highly volatile or fast-growing markets. Thijssen et al. (2001) put a parti• eolar focus on poliey implieations. They eonstruet agame with ineomplete information where nature determines the state of the world only at the beginning of the game and uneertainty resolves itself in the eourse of time. Pawlina/Kort (2oo1b) examine the ease of asymmetrie eompetitors and prove that profit uneertainty delays investment, regardless of the threat of pre-emption. Addressing specifie issues of pharmaeeutieal R&D sueh as the importanee of intellee• tual property, MiltersenlSehwartz (2002) introduee a finite time horizon and distinguish between one development and several marketing phases. In partieolar, the authors allow for abandonment before eompletion and thus eapture learning while investing in eompe• titive markets. Examples of real option models studying the timing and size of lumpy, not inere• mental, investments, are Dixit (1995), Bar-Dan/Strange (1999), and Dangl (1999). Murto et al. (2002) extend their analyses to a strategie setting. Finally, the influenee of strategie eonsiderations on financial eontraets has reemerged as a topie in the eontext of real options games. AndersonlSundaresan (1996) and Mella-

22 ZfB-Ergänzungsheft 3/2004 25 Years Real Options Approach to Investment Valuation

BarraUPerraudin (1997) raise the issue of strategie debt serviee and present cash-flow based extensions of the risky-debt pricing model by Merton (1974). Acharya et al. (2002) refine these models by relaxing assumptions about dividend payouts and the pos• sibility of raising additional cash. Opportunities for future research lie in models that go beyond the analysis of static equilibria and help us develop theories of industry evolution. Evolutionary game theory and agent-based models provide the means to explain and predict learning behavior and dynamies while further relaxing restrictive assumptions about markets, strategies, and information structures.

G. Valuing Real Options: Advances in Pricing Theory

I. Origins and Analytical Solutions

Research on quantitative methods applied to real options has drawn from a large body of literature in the area of financial option pricing and risk management. Although careful consideration of the distinct qualities of real options is required, these models still pro• vide the framework for formal analysis. While an exhaustive treatment of derivatives pricing is beyond the scope of this text, the following subsections highlight those aspects that are of particular importance for real option analysis. Bachelier (1900), today rightfully considered one of the most influential researchers in ,89 observes that "the expectation [i.e. the conditional expectation given the past information] of the speculator is zero". In other words, he accepts as an axiom that the market evaluates assets using a martingale measure. Even before Einstein (1905) and Markov (1906) he initiates the theory of Brownian motion and uses it for the mathematical modeling of price movements and the evaluation of contingent claims in financial markets.90 With his work on option pricing he lays the foundation for future research in the field. While previous authors mainly rely on expected value, Black/Scholes (1973) and Mer• ton (1973b) are the first to focus on the mitigation of risk through continuous rebalanc• ing of holdings in stocks and bonds. They describe how it is possible to create synthetic derivatives with self-financing portfolio strategies in dynarnically complete markets and obtain a closed-form solutions for the valuation of European options, Le. contracts that do not permit early exercise. Option prices in arbitrage-free markets are thus a by-product of effective hedging. Most noteworthy, the partial differential equation (PDE) derived by Black/Scholes depends on the volatility of the underlying asset and the risk-free rate, but is independent of the drift of the price process, Le. expected return. Under appropriate terminal and boundary conditions, it applies to any derivative written on a single under• lying asset whose price process follows a geometric Brownian motion. In addition, not only futures, forwards, options, and swaps, but many other claims including equity and debt can be thought of as contingent claims. The applicability of option pricing to real assets should require no further emphasis at this point. CmuRoss develop an alternative to the "replicating portfolio" approach. The alternative approach, commonly referred to as "risk-neutral valuation", allows the drift of the under-

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lying stock price process to be replaced with the risk-free rate of interest.91 A rigorous discussion of the mathematical foundations is provided by HarrisonlKreps (1979) and HarrisonIPliska (1981) who show that martingale representation theory is a suitable framework for the analysis of financial markets. Furthermore, risk-neutral valuation has become the standard approach for assessing real investment opportunities based on the options analogy. Following these seminal papers, American options in the presence of discrete are analyzed by Roll (1977), Geske (1979a), and Whaley (1981). Johnson (1983), Geske/ JOhnson (1984), Barone-AdesiIWhaley (1987), and Ho et a1. (1997) provide analytical approximations. Because American options may be exercised prior to maturity, they bet• ter reflect the flexibilities inherent in many real options. As stressed by Trigeorgis on several occasions, analytical formulae for more complex, or exotic, derivatives can be seen as building blocks that facilitate frarning and, in some cases, valuing investment problems under uncertainty.92 Analytical formulae also provide "ballpark" solutions serving as a starting point for further analysis. The option to exchange one risky asset for another,93 the option on the maximum or minimum of two risk as• sets,94 and the compound option95 are important examples. Widely used extensions and generalizations include the option on the maximum or minimum of several assets96 and compound exchange options.97 Standard pricing theory can be applied to obtain closed• form solutions for many exotic derivatives. Due to the high complexity of real invest• ments, however, numerical methods remain an indispensable valuation too1.

11. Numerical Methods

1. Discretization of the Underlying Assets' Price Processes: Binomial and Multinomial Trees Numerical methods rely on a discretization of either the underlying assets' price pro• cesses or the pricing PDE. In the case of binomial and multinomial trees, continuous• time models are transformed into their discrete-time equivalents assuming that the under• lying assets' prices increase or decrease at certain intervals, so that the resulting graphi• cal representation takes the form of a directed graph (tree).98 Cox et a1. (1979) present a discrete-time pricing model which uses binomial trees with equal jump sizes for upward and downward movements of the asset price. Probabilities are adjusted to reflect the dynarnics of the underlying price process. JarrowlRudd (1983) develop a similar model using equal probabilities where jump sizes are adjusted accord• ingly. Common techniques to improve the efficiency of the algorithm include e.g. binom• ial Black/Scholes and Richardson extrapolation.99 NelsonIRamaswamy (1990) exarnine binomial trees for alternative univariate stochastic processes and overcome the problem of non-recombination by adjusting the conditional probabilities of the binomial process over time. These models have been extended to the case of two1OO or morelOl underlying assets. The resulting trees are trinomial or multinomial, respectively. An alternative to the Boyle et al. (1989) model is discussed by He (1990) who proposes a multinomial tree in which the stocks and the also form a dynarnically complete securities mar-

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ket and the implicit Arrow-Debreu state price processes converge to the corresponding continuous-time limit. Trigeorgis (l991b) introduces a more accurate and efficient log• transformed procedure which is extended to several underlying assets by GambaITrigeor• gis (2001).102 Binomial and multinomial trees are particularly attractive as they make real option pricing conceptually similar to decision tree analysis and thus easy to communicate. Furthermore, it is straightforward to integrate technical risk and market risk in the manner of Smith/Nau (1995). Lander/Shenoy (1999) show how influence diagrams pro• vide a more compact representation of the information contained in trees that can also be analyzed independently.103 Consequently, although trees sacrifice some analytical clarity for versatility, they have become the most popular valuation tool for real op• tions in practice. KulatilakaITrigeorgis (1994) are among the first to approach valuation from a disctinct real option perspective. They discuss the general flexibility to switch between operating "modes" (switching option) which encompasses many other (real) options as special cases. Because trees suffer from the curse 0/ dimensionality, using them to tackle more complex switching options can be time-consuming or even practically impossible. Brekke/0ksendal (1994) also point out that, in a continous-time setting, not every optimal switching problem has a solution.

2. Discretization of the Underlying Assets' Price Processes: Monte Carlo Simulation As outlined previously, the value of an option corresponds to the risk-neutral expectation of its discounted payoff. In Monte Carlo simulation, a computer randomly creates a large number of realizations of the price process and obtains an estimate of the fair value by calculating the average present value of payoffs (under the risk-neutral measure). Monte Carlo methods for option valuation are first discussed by Boyle (1977) and, more recently by Broadie et al. (1997) and Boyle et al. (1997). Monte Carlo simulation is very flexible but also computationally intensive. For this reason, various methods for improving the efficiency and accuracy of Monte Carlo simulation have been proposed. These techniques include antithetic variables, control variates, importance sampling, stratified sampling, moment matching and quasi-random sequences. 104 Longstaff/Schwartz (2001) present a comparatively simple procedure for valuing American Options (Least Squares Monte Carlo, LSM) which is applied to real options by Schwartz (2001) and others. 105 Rasmussen (2002) discusses the use of control variates for the Monte Carlo valuation of American options. Gamba (2002) extends LSM to multiple options and implements a modular valuation approach for complex real options. The author also presents various cases based on models by Schwartz/Moon (200ob ), LintIPennings (2002), and Martzoukosffrigeorgis (1998). He concludes that "simulation seems to be the most suited numerical technique for real options". In fact, most other approaches have shown to be inadequate for dealing with large option portfolios and complex interactions. Ju (1998) and, in a real options context, Dias (2001) demonstrate how Monte Carlo simulation can be combined with genetic algorithmS. 106 Other applications are the valua• tion of influence diagramsl07 and so-called "swing"108 or "flow"l09 options, which are of

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particular relevance in energy finance. Further research is likely to enable the application of more sophisticated option pricing techniques in R&D, venture capital, and natural resources.

3. Discretization of the Pricing PDE Although it may be sometimes difficult to even derive the pricing PDE for a particular problem, numerical solution techniques for these equations continue to be highly rele• vant. Frequently, analyses are carried out as far as possible analytically. Only the final solution is obtained numerically. The equations in question are usually parabolic, of second order, and sometimes non-linear. Solution methods for PDEs can be roughly categorized into finite differences, finite elements and spectral methods. Because spectral methods can be problematic if the payoff function or its derivatives are discontinuous, only the first two techniques are generally considered. Brennan/Schwartz (1978) are the first to apply finite differences to option valuation. Differential equations are replaced by difference equations and then solved using itera• tive procedures. Procedures in common use include (projected) successive overrelaxation and, more recently, multigrid algorithms,uo Depending on the difference equations cho• sen, explicit and implicit schemes can be distinguished. The unconditionaHy stable Crank-Nicolson scheme is essentially an average of the the implicit and explicit meth• ods. 1l1 More advanced approaches include the generalized Crank-Nicolson or Douglas scheme (O-scheme) and the Alternating Direction Implicit (ADI) scheme suitable for higher dimensionalities.ll2 Interestingly, the simple but unstable explicit scheme is equivalent to a that approximates the diffusion process. Examples for the many applications of finite differences to real investment decisions under uncertainty in• clude SchwartzIMoon (2000a) and Cortazar/Casassus (2000). Finite elements are widely used in the engineering disciplines but have never really gained popularity among finance professionals. The domain of the differential equation is divided into small non-overlapping parts (finite elements) in each of which the solu• tion is approximated by aseparate algebraic function. Because finite elements provide several advantages over finite diffences the method deserves to playamore important role in the future. As Dangl/Wirl (2003) point out, coHocation methods, first applied in the context of endogenous growth models by Judd (1992), offer another fast and robust alternative to the common finite difference techniques.

4. Innovative Concepts and Future Directions There is no single best approach to option pricing. However several points are worth mentioning. Previous discussions of switching options hint at the wide applicability and flexibility of (controlled) Markov chains for the numerical valuation of derivative con• tracts. Markov chain models have been used in financial option pricing113 and seem to be particularly weH suited for real options. For example, Martzoukos (2000) employs a Markov chain numerical method for valuing real options with multi-dimensional random controls under incomplete information. The method, pioneered by control engineers and operations researchers, is very likely to receive more attention in the future,u4 Of parti• cular interest in economic research is the Markov chain approximation approach devel• oped by Kushner (1977, 1990).115

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Taudes et al. (1998) show how artificial neural networks (ANN) can be used to value real options. A more recent treatment is provided by CharalambouslMartzoukos (2001). Especially the value of large-scale projects with very long maturites is affected by and interest rates. The application of models that account for these variations has largely been lirnited to financial options and natural resources.1 16 How• ever, more advanced models with jump risks are analyzed by Martzoukosffrigeorgis (2002) and others. Furthermore, several authors exarnine the effects of transaction costs or constrained portfolios on the value of options in continuous-time and discrete-time settings. 1l7 One example is Monoyios (2004), who uses the abovementioned method of Markov chain approximation. Other analyses concerning this important issue inc1ude the articles by Cvitanic/Karatzas (1993), Edirisinghe et al. (1993), Soner et al. (1995), and others. Although, in principle, these contributions are highly relevant to real options, they have had so far litde impact on this strand of the literature. Sirnilarly, two areas which have long been a major concem to practitioners are the treatment of taxes118 and option pricing in incomplete markets. 119 Finally, numerical techniques for deterrnining equilibria in games and agent-based models for analyzing social behavior have recendy become increasingly popular. Besides portfolio approaches to the valuation of real options, the development of numerical meth• ods for dealing with real option games could prove to be a fruitful area for further research. 120

H. Bridging the Gap to Corporate Practice

Wide-spread interest in real options was triggered by the "New Economy" boom in the 1ate 1990s. Capital markets were posed with the question of how to value IPO candi• dates that possessed apparent growth potential but no established sources of cash flow. DCF-based assessments typically suggested a negative added value during the planning horizon, so that in order to justify investment assumptions in the residual value calcu• lation had to be adjusted accordingly.121 Sirnilarly, multiple-based methods tended to carry over bloated expectations from benchmark companies to the valuation objects - a practice often blamed for the recent stock market bubble. The initial enthusiasm of practitioners concerning the potential of real options as a management tool has, however, by now ceded its place to profound scepticism. Correcdy so, as "New Economy" invest• ments represent largely non-traded assets which can be valued using either the so-called equilibrium approach or the cash-flow-based simulation approach. While no c1ear-cut techniques exists to operationalize the former, the latter shares many problems with DCF methods. As mentioned previously in this artic1e, HommellPritsch (1999a) and Pritsch (2000) point out that the real options method can be applied at two levels, as a purely quali• tative instrument to characterize optionalities and as a formal valuation tool. The authors choose to disagree with Tom Copeland who c1aimed in 1998 that real options would become the standard valuation tool by 2010 while DCF would be reduced to a special case for investments without time value. 122 Most fmns will benefit from using

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"option language" to characterize varying degrees of managerial flexibility; few will turn to real options as a formal valuation tool to help them in their capital budgeting process. The fact that real options have made their way into mainstream corporate finance theory is documented by standard textbooks which include sections on real options. 123 In addition, a considerable number of monographs has appeared in recent years. Typi• cally, the authors seek to give practitioners a head start on the learning curve. Arnram/ Kulatilaka (1999) and Boer (2002) extend the managerial valuation literature and pro• vide a qualitative overview of the real option method. Arnram (2002) gives a balanced introduction and outlines how real options can be combined with traditional methods (DCF, decision analysis) to value corporate growth opportunities. More formal treat• ments can be found in the texts by Trigeorgis (1996a), CopelandlAntikarov (2001), and Mun (2002). While Trigeorgis (1996b) primarily summarizes his earlier research, the text is still considered the most thorough introduction to the technical aspects of real options theory.1 24 In contrast, Copelandl Antikarov (2001) present an accessible intro• duction to option valuation which is largely based on variations of the binornial method. The text most relevant for practitioners is Mun (2002) who provides detailed methodolo• gical guidelines for obtaining meaningful estimates of input parameters. Dixit/Pindyck (1994) are positioned at the other end of the spectrum. They provide an advanced and self-contained treatment of investment under uncertainty as well as its mathematical foundations. Furthermore, the authors describe a variety of applications ranging from the simple model of irreversible investment by McDonaldlSiegel (1986) to more advanced industry-specific examples. A number of preconditions need to be met for corporate practitioners to embrace the real options method. Firstly, and most importantly, option-based valuation must deliver correct results with a high degree of precision so that a systematic rnisalloca• tion of funds can be avoided. Secondly, the approach needs to be highly versatile in order to perrnit the implementation of standardized decision-making frameworks. Thirdly, the method has to be simple enough to circumvent the "white coat" syn• drome, signifying a situation in which the decision-making tools employed are merely understood by option pricing specialists rather than by project managers themselves. A high degree of transparency allows management to become involved and provide fun• damental input data. Finally, the option pricing technique must enable users to capture application-specific complexities such as structures (e.g. rainbow options for projects with multiple sources of uncertainty), interproject and intra-project interac• tions, strategic interdependencies with competitors, and controls available to manage• ment prior to option exercise. After reflecting on these points, one has to conclude that the successful adoption of real option valuation critically depends on the use of simple, albeit inaccurate, techniques to gain the acceptance of top management. Exarnined in the light of these insights, it is understandable why the applied real options literature generally advocates - as a second• best solution - the use of binornial option pricing models. The modeling process forces users to analyze econornic fundamentals and therefore avoids the "black box" problems, for instance associated with analytical solutions. Binornial trees are also reasonably flex• ible in handling different cash payout patterns, option interactions, and competitive ef-

28 ZfB-Ergänzungsheft 312004 25 Years Real Options Approach to Investment Valuation

fects. At the same time, they have clear limitations in dealing with complex option rights. The gap to state-of-the-art real option valuation will definite1y narrow in the com• ing years as software solutions become available covering every step from "option fram• ing" to the valuation and management of real option portfolios. 125 Acceptance will be further enhanced as university graduates with formal real options training advance to the upper echelons of corporate hierarchies. Using real options is however not primarily a matter of selecting the appropriate toolkit. Results always hinge on the reliability of the underlying economic assumptions, the quality of the input data used and the investor's ability to draw the correct conclusions - this caveat applies regardless of the valuation method chosen. In recent years, academics have increasingly turned towards "bridging the gap to cor• porate practice" by developing general guidelines for the implementation of option-based thinking in a firm's capital budgeting process. According to HommellPritsch (1999a), real option analysis involves three interrelated steps: the identification, valuation, and management of real options. Identification of real options requires management, above all, to spot project features with option characteristics: uncertain payoffs, managerial degrees of freedom and at least partial irreversibility of investment expenditures. DixitJ Pindyck (1995) explain most succinctly that the presence of sunk cost is of particular importance as the exercise of options could otherwise be reversed at no cost thereby eliminating any time value. In addition, one also needs to carefully distinguish between "options" and simple "bets". Bets represent the choice between alternative strategies prior to the resolution of uncertainty and therefore also lack any time value. As argued by HommellPritsch (1999b), the valuation stage can again be structured as a four-step process involving (1) the identification of a11 project-related real option rights as well as their prioritization, (2) the selection of the (option or alternative) valuation model, (3) the option-based valuation of the project which includes estimat• ing the model parameters, valuing the stand-alone options deemed to be of sufficient relevance for the overall project value, and quantifiying interaction effects and, finally, (4) the fine-tuning of the calculations based on a sensitivity analysis to check for critical assumptions, are-evaluation of economic conjectures regarding the viability of the project and, lastly, stress testing to account for the possible occurrence of extreme events. 126 The management of real options requires corporate decision-makers to take a portfolio view in order to evaluate how strategy adjustments can enhance individual option values as weH as the aggregate value of real decision-making flexibilities at the level of the firm. According to LeslielMichaels (1997), a portfolio view requires identification and continuous monitoring of option levers using sensitivity analysis, which subsequently can be used to develop strategies that involve manipulating those option parameters that have the biggest impact on firm value. A number of further conceptualizations have demonstrated the value of real options analysis to potential users. Most famously, the tomato garden described by Luehrman (1998) illustrates how option-based investment opportunities can be ranked according to cumulative variance and in-the- in order to deve10p a portfolio view and to select those projects most likely worthy of pursuing. Along the same lines, Kester (1984) and Trigeorgis (1988) propose capital budgeting techniques that reflect the option char-

ZfB-Ergänzungsheft 312004 29 Philipp N. Baecker and Ulrich Hommel

acteristics of growth opportunities as weH as the potential of shared ownership of these options in environments with varying intensities of competition. In order to evaluate the benefits generated by particular real options, Copeland/Keenan (1998) suggest c1assifying them into three categories: learning, growth and insurance options. Learning options are available before as weH as during an investment phase and enable management to respond to the resolution of uncertainty. Growth options can be exercised during and after an investment phase and enable the firm to sc ale up existing activities or to access new value chains in response to positive market developments. In contrast, insurance options permit scaling down or terminating investment or production if "bad news" is received.127 Real investment projects typically represent a combination of these option types so that interaction effects must be explicitly accounted for. Unfortunately, the applied literature has so far offered little insights into how this issue is best approached from a practical level. What has been addressed extensively, however, are the equally relevant issues of how to deal with multiple sources of uncer• tainty and the compoundedness of option rightS. 128 In addition, besides simple market risk, some other forms of (technological, regulatory, cost) risk drive the performance of most investments. Performance can thus be further enhanced by subjecting investments to periodic review exercises. Case studies docurnenting the successful implementation of the real options method still represent the most effective means of illustrating the advantages of option-based decision-making to corporate practitioners. In recent years, a number of case study (or "how to" artic1e) collections have been published, among them the texts edited by Tri• georgis (1999) and Hornrnel et al. (2001, 2003). The discussion of Airbus' approach to long-term production planning by Stonier (1999) offers a hands-on step-by-step approach to the implemention of option-based business strategies. MüHer (2000) combines real options with simple game-theoretic modeling to evaluate investments in Polish retail banking. An illustration of how auction theory can be integrated into a real options frame• work is offered by Moelffufano (2000) with their analysis of a Peruvian mine privatiza• tion program. A considerable number of case studies focus on pharrnaceutical R&D, among them Micalizzi (1999), to illustrate the interaction between hedgeable and non• hedgeable forms of risk. BasicaHy aH case studies available to date suffer from the same shortcomings. 129 While substantial effort is typically spent on motivating the applicability of the real options method, practitioners fail to pick up the necessary skiHs to replicate the solutions presented in these case studies and to apply the same logic to related problems. In parti• cular, valuation tools are often not explained adequately or even incorrectly.130 Users also learn very little about capturing interaction effects for multi-option applications. Most defeating, however, is the apparent inability of the respective authors to demon• strate how users can generate relevant input data in day-to-day decision-making. "Wav• ing one's hands" at the details and referencing non-specific data sources for the determination of critical information such as current project values, market volatility and other sources of uncertainty does little to address the practitioners' reservations. 13l Loosely speaking, putting "sugar coating over an academic black box" may make the message initiaHy more palatable for the practitioner's taste - it does not change the basic fact that it is still a "black box".

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Practitioners frequently respond to questions about their view on real options: "Inter• esting idea but has anybody ever used it?" Indeed, the record should speak for itself. Real option models have been widely adopted in resource-intensive industries (e.g. mining, oil drilling, energy generation) given the availability of market data as the basis for estimating the value of the underlying and its volatility.132 As mentioned earlier, Merck's coming out as a real option user133 has done much to attract the attention of pharmaceutical and substance-generating Biotech. In this case, the methodological fit results from the availability of indication-specific R&D success and market data as well as the existence of a uniform R&D process enabling the use of a standardized valuation model. The uniqueness of the R&D target result however often restricts companies to merely apply qualitative options thinking,134 especially if orphan drug status is to be awarded. The real options approach is also ideally suited for a number of other applications but is currently not actively used in these instances. Platform technologies are common in automobile production and can be interpreted as perpetual exchange options between different car models. The same holds true for multiple sourcing arrangements as weH as intra-company supply and production networks. Contractual options playa critical role for economic performance in many sectors (e.g. automobile leasing, life insurance industry, venture capital contracting, M&A contract provisions) but are typically not valued as such. In addition, any market, product line or brand name extension investment repre• sents a basic call option on future cash flows. The lackluster acceptance of the real options approach by corporate practitioners135 is further substantiated by empirical evidence. Survey results for the U.K. by BusbylPitts (1997) indicate that managers are largely aware of investment-related optionalities but rarely quantify them. There also appears to be a bias towards recognizing learning and growth options as opposed to insurance options - probably because the latter carries the stigma of failure. Also Vollrath (2001), who provides empirical evidence from the Ger• man market, finds that managers value real optionalities largely on an intuitive level. Howell/Jägle (1997) go one step further and gather laboratory evidence on whether U.K. managers are actually capable of deriving proxies elose to the correct option values. The results indicate that managers tend to be overly optimistic (in particular in the case of growth options) but that performance is positively associated with the prevalence of real options in their respective sector. As the simulation analysis by McDonald (2000) never• theless demonstrates, making explicit use of real option techniques is not a precondition for optimal investment decisions. Common "mIes of thumb" such as hurdle rates and profitability indices tend to perform quite weH for considerable parameter ranges. Real option valuation is best understood as a walk along the thin line between oversimplifica• tion and overengineering. Overall, one can conelude that the real options approach so far has enjoyed only limited success in penetrating managerial thinking. Explanatory factors are, above all, the scarcity of thorough textbook treatments offering a complete toolbox for option framing and valuation, the lack of standardized software solutions with user• friendly frontends and the absence of a convincing storyline under what conditions traditional valuation methodologies should be abandoned in favor of real options analysis.

ZfB-Ergänzungsheft 312004 31 Philipp N. Baecker and Ulrich Hommel

I. Closing Remarks

Real options research has covered substantial ground over the previous quarter of a cen• tury. Following the increasing, "economization" of research in the social sciences, the theory of investment under uncertainty is likely to branch out further into other fields such as the public choiee theory of politieal decision making. This documents the metho• dology's overall versatility and general relevance for analyzing decision-making flexibil• ities. The following examples serve as an illustration: • In the future, lawmakers will increasingly rely on implementing choiee-enhancing regu• lations,136 Le., company shareholders (in response to a proposal by management) can choose between a finite number of regimes and can subsequently also switch between existing regimes. Doing so will involve irreversible transaction costs related to explain• ing such a move to financial analysts, portfolio managers and the financial press. • The deregulation of German labor law enables management to employ its labor force flexibly Gob assignments, working hours within a certain time period, etc.). Flexibil• ities represent real options, if the exercise involves sunk cost, otherwise choices can be explained with least-cost dealing. 137 • Marketing research has so far failed to discover the potential of the real options ap• proach. Obtaining trademark protection for instance grants the firm the right to subse• quently introduce an entire product family under the umbrella of a powerful brand name whieh gives rise to numerous deferral, growth, and abandonment options. • Adopting an option-based view also facilitates the analysis of household behavior. Take for instance the ability of individuals to select between public and private health insurance in Germany.138 As personal income increases, they may switch from publie to private coverage and have the subsequent possibility (conditional on the personal health status) to switch between private insurers or back to the public system. A thorough analysis of such issues, however, requires the integration of real options with utility theory. On a more negative note, formal real options research appears to become increasingly detached from real-world applications. While for instance the integration of the option perspective into game-theoretic treatments of firm behaviour certainly represents - next to option-based R&D valuation - the most important innovation in the literature in recent years, the analyses rarely bear any resemblance with typieal strategie planning processes encountered in corporate practiee today. There exist ample signs that general managers are rejecting the real options approach: (1) a number of leading consulting firms have discontinued to market real options as a core competence, (2) if used by consultants, they generally shy away from using real options terrninology in their communication with clients, (3) investment bankers and venture capitalists still do not employ the real options method except for the occasional valuation of contract provisions with insurance features. One can hardly fail to notiee that truly original contributions in the applied field have become rare, especially in recent years. While this may be viewed as a natural process of a maturing literature increasingly focused on the refinement of pioneering publications of previous years, it is actually a defining pattern of the last 25 years of real options research and may therefore reflect more fundamental reservations of the finance

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community. Nevertheless, real options theory has had a profound impact on the way we think about management by capturing the essence of corporate decision-making in an environment of uncertainty: "It would be an error to believe that, to achieve a higher civilization, we have merely to put into effect the ideas now guiding uso If we are to advance, we must leave room for a continuous revision of our pre• sent conceptions and ideals which will be necessitated by further experience." (von Hayek, 1978)

Endnotes

* This paper is based on a presentation by u. Hommel held at the Conference on "Recent Topics in Real Option Valuation" at the Donau-Universität Krems (Austria) in July 2002. The authors would like to thank two anonymous referees and the editors for helpful comments, while retain• ing responsibility for all remaining errors and omissions. 1 Cf. Hayes/Abemathy (1980), Hayes/Garvin (1982). 2 Cf. Pettit (1995), Stark (2000). 3 Cf. HarrisonlKreps (1979), HarrisonIPliska (1981). 4 Cf. HubaleklSchachermayer (2001). 5 Cf. Merton (1973a). 6 Flexibility in deterministic settings has been studied by researchers for a considerably longer period of time, as exemplified by the asset replacement problem. Postponing areplacement in• vestment for an additional period results in higher operating costs and reduces the second-hand or salvage value. At the same time, postponement also reduces the present value of cash out• flows associated with the chain of investments that subsequently follow. Cf. e.g. Hotelling (1925), Terborgh (1949), Smith (1961), Merrett/Sykes (1965). A more recent analysis of the asset replacement problem under uncertainty is conducted by Pawlinal Kort (2002). See subsect. EIl. 7 Cf. DixitlPindyck (1994). 8 Considering the case of an option to wait/defer (equivalent to a financial call), one uses the present discounted value of gross cash flows (value of the underlying), its volatility (volatility of the underlying), investment costs (exercise price), cash pay-outs (dividends), time until investment opportunity expires (time to maturity) and the risk-free rate of interest. Cf. for instance Trigeorgis (1996b, p. 125). 9 A collection of seminal real options papers has been edited by Schwartzffrigeorgis (2001). 10 These books incIude Amram (2002), Amram/Kulatilaka (1999), Boer (2002), Brach (2002), Brennan/Trigeorgis (2000), CopelandlAntikarov (2001), Dixit/ Pindyck (1994), Grenadier (2oooc), Hommel et al. (2001, 2003), Moore (2001), Mun (2002), Newton et al. (2001), Schwartzffrigeor• gis (2001) and Trigeorgis (1995, 1996b, 1999) as well as a number of others still forthcoming. Special issues have for instance appeared in the Midland Corporate Finance Journal (Spring 1987), Managerial Finance (Vol. 17, No. 3), Financial Management (Vol. 22, No. 3), R&D Management (Vol. 31, No. 2), the Journal 01 Applied Corporate Finance (Vol. 14, No. 2), the Journal 01 Property Investment and Finance (Vol. 19, No. 1) and the Quarterly Review 01 Economics and Finance (Vol. 38, Special Issue). 11 Cf. for instance Ballwieser (2002) for a rather critical assessment of the real options method. 12 Cf. Triantis, 1999, Amram/Kulatilaka, 2000, Damodaran, 2000. 13 Cf. Kogut/Kulatilaka (2001), Bamey (1991), LippmanIRumelt (1982). 14 Cf. HendersonIHobson (2002). 15 Cf. Smith/Nau (1995). Similarly, financial options may be subject to various types of risk (e.g. credit risk) which need to be incorporated into valuation models. 16 Cf. SchwartzIMoon (2ooob), Tsekrekos (2001, 2oo2b).

ZfB-Ergänzungsheft 312004 33 Philipp N. Baecker and Ulrich Hommel

17 Cf. HuchzermeierlLoch (200 1). 18 Cf. Adner/Levinthal (2002). 19 Cf. LeslieIMichaels (1997). 20 Cf. Kester (1984), Grenadier (2002). 21 Cf. MasonIWeeds (2001), Weeds (2002). 22 Cf. Trigeorgis (1993). 23 Cf. Trigeorgis (1993). 24 Cf. Childs et al. (2001), Bellalah (2001). 25 When writing a review article on a topic as broad as real options, one is bound to do injustice to certain authors by failing to adequately acknowledge their contributions. This problem is inherent in the task of summarizing a diverse field of research within the page limits of a journal article. While many articles present the obvious core of the real options theory and can therefore not be overlooked, others are left out as a result of the reviewers' personal research bias. In those cases, we plead for leniency of judgement by openly forfeiting the claim of having completed the task in an exhaustive manner. 26 Cf. e.g. Trigeorgis (1996b), Schwartztrrigeorgis (2001). 27 Cf. Trigeorgis (1996b) for a discussion of stand-alone options on the basis of intuitive numer• ical examples. 28 Cf. McDonaldiSiegel (1986), Paddock et al. (1988), IngersolllRoss (1992), Tourinho (1979). McDonaldiSiegel (1986), and Paddock et al. (1988) apply the concept to the valuation of off• shore pertroleum leases while Tourinho (1979) analyzes the value of natural resource reserves in general. IngersolllRoss (1992) exarnine the option value from deferring investments in the presence of interest rate risk. Cf. DixitJPindyck (1994, pp. 2655) for the probably most widely cited numerical illustration. 29 Cf. LeslieIMichaels (1997). See en. 8. 30 Cf. Trigeorgis (1996b). 31 Cf. Pritsch (2000). 32 Cf. TrigeorgislMason (1987), Pindyck (1988). 33 Cf. BrennanlTrigeorgis (2000), McDonaldiSiegel (1985). 34 Cf. MyersIMajd (1990). 35 Cf. Margrabe (1978). 36 Cf. Kensinger (1987), Kulatilaka (1988, 1993). 37 Cf. BaeckerlHommel (2003). 38 Cf. Myers (1977), Kester (1984). Options to innovate represent a sub-category of the wider class of options to grow. 39 Cf. Childsrrriantis (1999). 40 Cf. HuchzermeierlLoch (2001). 41 Cf. Trigeorgis (1996b). 42 American options can therefore expected to interact more strongly than European options. 43 Berger et al. (1996) for instance characterize equity as a put option on the firm's assets and show that its value is negatively correlated with the degree of specialization. A number of has analyzed the value of waiting options, e.g. in the context of real estate development, natural resource extraction of leasing contracts. Cf. Quigg (1993), Paddock et al. (1988). 44 Cf. Merton (1987). 45 Cf. Sarkar (2003). 46 Cf. SmithIMcCardle (1999). 47 Cf. Jaillet et al. (1998). 48 Cf. also Schwartz (1997), Slade (2001). 49 Cf. Dixit (1992). 50 Cf. Baldwin (1982). 51 A number of papers look at the issue from an economics angle: Bertola (1988), Pindyck (1988), AbellEberly (1994, 1996, 1997), and AbeI et al. (1996) describe the impact of irreversi• bilities on capital accumulation. Cf. also Pindyck (1991) for an early review of this topic. Related to the issues of capital accumulation and capacity choice is the decision of when to enter or exit markets which, for instance, is discussed by Dixit (l989a). Other contributions are

34 ZfB-Ergänzungsheft 3/2004 25 Years Real Options Approach to Investment Valuation

more closely associated to production management such as treatment of capacity choices over the product life cyc1e by Bollen (1999). The article contains an analysis of optimal switching from a growth to a decay regime in response to stochastic demand shifts where an initial upward trend is later followed by structural dec1ine. KamradlErnst (2001) analyze the problem of valuing a production unit exposed to market as weIl as input yield uncertainty; they show that adjustments of the rate of production can be viewed as "nested" real options. 52 A managerial discussion of these aspects can be found in the article by Aggarwal/Soenen (1989). 53 Cf. de Mezalvan der Ploeg (1987). 54 In this context, Chang (1998) notes that hedging entails an additional cost by reducing the value of implicit abandonment options. As a consequence, firms have an incentive to focus on short-term hedging activities. 55 A detailed assessment of these techniques in comparison to real options is provided by Trigeor- gis (1996b). 56 Cf. Morris et al. (1991). 57 Cf. Sahiman (1988). 58 Cf. Siegel et al. (1987). 59 Cf. e.g. KulatilakaIMarcus (1988), KulatilakaIMarks (1988), TriantisIHodder (1990), Trigeorgis (1993). 60 Cf. Grenadier (20ooa), de Almeida/Zemsky (2003). 61 Oe Almeida/Zemsky (2003) introduce the terms of Delay, Incremental Coumot, Commit-delay, and Commit-incremental equilibria to denote the subgame-perfect strategies of duopolists invest• ing into productive capacity under demand uncertainty. 62 Cf. Kester (1984). 63 A more detailed discussion and criticism is provided by Pike (1997). 64 Cf. LintIPennings (1999). 65 Cf. MiltersenlSchwartz (2002). 66 Cf. Bernardo et al. (2000). 67 Cf. McGrathIMacMillan (1995). 68 Note the necessary corrections pointed out by Ooraszelski (2001). 69. Cf. GrenadierIWeiss (1997). 70 Cf. Cooper (1993). 71 Cf. BaldwinlClark (1992). 72 Cf. KogutIKulatilaka (1994b). 73 Cf. Nichols (1994). 74 Cf. Longstaff/Schwartz (2001). 75 Cf. BaldwinlClark (2002). 76 Cf. Erdogmus/La Barre (2000). 77 Cf. Margrabe (1978). 78 In a foIlow-up paper the authors provide additional information on the case and discuss metho- dological issues. Cf. Benaroch/Kauffman (2000). 79 Cf. Christensen (1997). 80 Cf. Trigeorgis (1991a). 81 Cf. Emanuel (1983), Constantinides (1984), SpattlSterbenz (1988). 82 Cf. Williarns (1993), Grenadier (2002). 83 Cf. also Hoppe/Lehmann-Grube (2002) for a general framework for the analysis of such timing games. 84 Cf. also BaldurssonIKaratzas (1997). 85 Cf. Oixit (1989b). 86 Cf. Dixit (1991). 87 Cf. Murto et al. (2002), BalmannIMußhoff (2002). 88 Cf. Grenadier (1996), Outta et al. (1995), Weeds (2002). 89. Cf. Courtault et al. (2000) for an overview. 90 Note that Brownian motion was originally referred to as "Wiener-Bachelier process". Cf. FeIler (1957). 91 Cf. COlURoss (1976b,b).

ZfB-Ergänzungsheft 312004 35 Philipp N. Baecker and Ulrich Hommel

92 Cf. Trigeorgis (1996b). 93 Cf. Margrabe (1978). 94 Cf. Stulz (1982). 95 Cf. Geske (1979b). A generalization of the Geske fonnula possibly more appropriate for the val- uation of compound (growth) options is presented in arecent article by ElettraIRosseIla (2003). 96 Cf. Johnson (1987). 97 Cf. Carr (1988). 98 For a textbook treatment of special cases such as adaptive mesh models cf. Wilmott et al. (1993). 99 Cf. BroadielDetemple (1996). 100 Cf. Boyle (1988). 101 Cf. Boyle et al. (1989), KamradlRitchken (1991). 102 A good overview of binornial and multinornial trees as weIl as other numerical and analytical procedures is provided by Shaw (1999). 103 Cf. Charnes/Shenoy (2000). 104 Cf. Wilmott et al. (1993). 105 Another example is the earlier paper by Barraquand/Martineau (1995). 106. Cf. also Keber (2000). 107 Cf. Charnes/Shenoy (2000). 108 Cf. Jaillet et al. (2001). 109 Cf. ShackletonIWojakowski (2001). 110 Cf. Brandt/Cryer (1983). 111 Cf. Crank/Nicolson (1947). 112 Cf. Peaceman/Rachford (1955). 113 Cf. Duan/Simonato (2001). 114 Cf. Hou et al. (2002). 115 Cf. KushnerlDupuis (2001). 116 Cf. HulllWhite (1987), Amin (1993), Scott (1997). 117. Cf. Leland (1985), BoyleNorst (1992). 118 Cf. SurethlNeimann (2002). 119 Cf. HendersonIHobson (2002). 120 Cf. Kushner (2002), DawidIKopel (1998), BalmannlMußhoff (2002). 121 Damodaran (2oo1b), for instance, proposes to calculate the residual value in two stages based on aperiod of "high" and "nonnal" growth. 122 Keynote speech held at the 2nd Conference "Real Options: Theory Meets Practice" (Chicago, June 1998). 123 Cf. for instance BrealeylMyers (2000, eh. 21) Damodaran (2001a, eh. 27), and Ross et al. (2002, eh. 23). The same obviously holds true for textbook treatments of corporate valuation: Copeland et al. (2000, eh. 20), Damodaran (2002, chs. 5 and 2730), Loderer et al. (2002, chs. 2122), and PeemöIler (2001). 124 The Gennan textbook literature consist mostly of variations of Trigeorgis (1996b) such as Meise (1998) and Kilka (1995). 125 Several real option software packages have been developed recently, e.g. the Decision Pro• gramrning Language (DPL) by Applied Decision Analysis (ADA) and the Real Option Valua• tion Toolkit to be used with Crystal Ball by Decisioneering. 126 Alternative approaches have for instance been suggested by Amram/Kulatilaka (1999, eh. 7) as weIl as Copeland/Antikarov (2001, eh. 8). 127 These aspects are discussed more extensively in Hommel/Pritsch (1999a). 128 Cf. CopelandIKeenan (1998), Copeland et al. (2000). 129 Cf. in particular the case collection edited by Newton et al. (2001). 130 Case illustrations employing the binornial model typically rely on a small number of time steps and fail to point out that reasonable approximations require in excess of 300 jumps per year. Cf. for instance Clewlow/Strickland (1998, pp. 1720). 131 The one notable exception is Pritsch (2000) describing how real options can be applied to the valuation of pharmaceutical R&D activities.

36 ZfB-Ergänzungsheft 3/2004 25 Years Real Options Approach to Investment Valuation

132 The most illustrative introduction can still be found in a paper by BrennanlSchwartz (1985) and, more recently, also in a paper by Cortazar et al. (1998). 133 Cf. Nichols (1994). 134 Cf. Koch (1999). 135 Cf. Smith/McCardle (1998), TriantisJBorison (2001). 136 Cf. Romano (2002), Bebchuk (2002). 137 Cf. HommellRiemer-Hommel (1999). 138 Cf. Riemer-Hommel et al. (2003).

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Zusammenfassung

Seit der Einführung des Begriffs "Realoption" in die Finanzierungsliteratur durch sind 25 Jahre vergangen. Umfangreiche Forschungsarbeiten haben in der Folge die Auswirkung von Entscheidungsspielräumen des Managements auf Geschäftsgebaren und Unternehmenswert analysiert. Das Ziel dieses Forschungsüberblicks liegt sowohl im Aufspüren der Ursprünge als auch in der Bestimmung der tragenden Säulen der Real• optionsliteratur. Der Realoptionsansatz findet in den unterschiedlichsten Bereichen Anwendung: Investi• tionen in Rohstoffe, Risikomanagement und Auslandsdirektinvestitionen, Produktions• flexibilität, F&E sowie Immobilien. Für jedes dieser Gebiete werden die wegweisenden Beiträge aufgezeigt und Impulse für die Forschung bewertet. Die noch existierenden For• schungslücken werden identifiziert. Die Verbindung von Realoptionsanalyse und der spieltheoretischen Behandlung strate• gischer Wechselbeziehungen zwischen Unternehmen bildete in jüngster Vergangenheit

52 ZfB-Ergänzungsheft 3/2004 25 Years Real Options Approach to Investment Valuation

für viele Autoren den Brennpunkt ihrer Forschung. Diese Verbindung zieht sich auch als roter Faden durch die in dieser Sonderausgabe versammelten Beiträge. Die Autoren be• werten die Errungenschaften und Perspektiven dieses Literaturstranges. Der Realoptionsansatz muss seine Bedeutung für die Untemehmenspraxis noch unter Beweis stellen. Erfolgsgeschichten sind dünn gesät und mögliche Anwender durchweg zurückhaltend. Dieser Beitrag zeigt auf, welche unterschiedlichen Wege in der Literatur beschritten wurden, um den Anforderungen der Praxis zu genügen. Der Beitrag nennt zudem die Hindernisse, die einer weiten Verbreitung der Realoptionsbewertung noch ent• gegenstehen.

Summary

25 years have passed since Stewart Myers has introduced the term "real options" to the finance literature. A vast body of research has subsequently analyzed how managerial decision-making flexibilities impact firm behavior and shareholder value. The objective of this review artic1e is to trace out the roots as well as identify the main pillars of the real options literature and illustrate its universal applicability to all forms of corporate decision-making. Real options analysis has been applied to topics as diverse as natural resource invest• ments, risk management and foreign direct investments, production flexibilities, R&D, and real estate. In each case, the seminal contributions and their role for spawning further research is assessed and still existing gaps in the literature identified. The linkage between real options analysis and game-theoretic treatments of strategie interdependence between firms has been the focus of wide array of authors in the recent past and is also the common thread of the papers inc1uded in this special issue. The authors eValuate the accomplishments and prospects of this strand of the literature. The real options method still has to pass the relevance test of corporate practice. Suc• cess stories are scarce and reservations of potential users pervasive. This artic1e outlines in what ways the literature has attempted to address the needs of practitioners and what barriers to the wide-spread use of real option valuation still exist.

JEL: G31, G13, D81

ZfB-Ergänzungsheft 312004 53 Erfolgreiche Implementierung er wertorientierten

Jürgen Weber/Urs Bramsemannl Carsten Heineke/Bernhard Hirsch Wenorientiene Kennzah len Wertorientierte Unternehmenssteuerung Konzepte -Implementierung• PralCisstatements 2004. XVI, 391 S. Geb.EUR 44,90 ISBN 3-409· 12433·0

Wertorientierte Unternehmenssleuerung wird nur dann erfolgreich sein, wenn sie ganzheitlich, strukturiert und an den Flihigkeilen und Zielen der betroffenen Mitarbeiter ausgerichtet ist Oie Autoren verstehen wertorientierte Unlerneh· menssleuerung als Herausforderung der Implementierung wertorientierter Kennzahlen. Sie gehen auf die Zielplanung. die Anreizgestaltung, das interne Berichtswesen, die Kommunikation mit dem Kapitalmarkt und den aigentlichen Implementierungsprozess ausfiIhrlieh ein. Statements von Praktikern geben Einblick in die Erfahrungen erfolgreicher Unternehmen bei der Einführung der Wenorientierten Unterne hmensste uerung.

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