Valuation of a Developmental Drug as a Real Option

Valuing a capital investment as a real option (or series of options) has advantages over standard DCF valuation when the investment creates the future flexibility to delay, abandon, or expand an element of the project based on the resolution of a major source of uncertainty. The uncertainty is generally dealt with using a “volatility” term that aims to reflect the variability in the future value of the underlying asset. But there are certain situations in which the uncertainty has a second dimension. For example, drugs in development can be abandoned either because of bad technical outcomes (the drug doesn't work) or unfavorable resolutions of market risk (though the drug works, its market potential turns out to be too limited). In an article published earlier in this journal, the authors illustrated the valuation of an early‐stage pharma R&D investment using a real options approach in which the market and technical risks were folded together into the volatility parameter. In this article, the authors explain why they have concluded that this is an incorrect approach and then show how to handle market and technical risk as two separate dimensions of risk in valuing an R&D program. The potential use of this technique extends beyond pharma and biotech R&D to any situation in which the outcome of an important uncertainty is independent of the resolution of market risk associated with the underlying asset.     

Assessing Project Risk

Finding the appropriate discount rate, or cost of capital, for evaluating investment projects requires an accurate estimate of project risk. This can be challenging because project risk cannot be estimated directly using the CAPM, but must instead be inferred from a set of traded securities, typically the equity betas of comparable firms in the same industry. These equity betas are then unlevered to undo the effect of comparable companies' financial leverage and obtain estimates of “asset” betas, which are then used to estimate project risk. The authors show that asset betas estimated in this way are likely to overestimate project risk. The equity returns of companies are risky not only because of their existing projects but also because of their growth opportunities. Such growth opportunities often include embedded “real options,” such as the option to delay, expand, or abandon a project. Because such real options are similar to leveraged positions in the underlying project, a company's growth opportunities are typically riskier than its existing projects. Therefore, to properly assess project risk, analysts must also unlever the asset betas derived from comparable company stock returns for the leverage contributed by their growth options. The authors derive a simple method for unlevering asset betas for growth options leverage in order to properly assess project risk. They then show that standard methods for assessing project risk significantly overestimate project costs of capital—by as much as 2–3% in industries such as healthcare, pharmaceuticals, communications, medical equipment, and entertainment. Their method should also be applied to stock return volatility to derive project volatility, an important input for determining the value of a firm's growth opportunities and the appropriate time for investing in these opportunities.     

Brealey,Myers, and Allen on Real Options

Real options are valuable sources of flexibility that are either inherent in, or can be built into, corporate assets. The value of such options are generally not captured by the standard discounted cash flow (DCF) approach, but can be estimated using a variant of financial option pricing techniques. This article provides an overview of the basics of real option valuation by examining four important kinds of real options:

• 1 The option to make follow‐on investments. Companies often cite “strategic” value when taking on negative‐NPV projects. A close look at the payoffs from such projects reveals call options on follow‐on projects in addition to the immediate cash flows from the projects. Today's investments can generate tomorrow's opportunities.
• 2 The option to wait (and learn) before investing. This is equivalent to owning a call option on the investment project. The call is exercised when the firm commits to the project. But often it's better to defer a positive‐NPV project in order to keep the call alive. Deferral is most attractive when uncertainty is great and immediate project cash flows—which are lost or postponed by waiting—are small.
• 3 The option to abandon. The option to abandon a project provides partial insurance against failure. This is a put option; the put's exercise price is the value of the project's assets if sold or shifted to a more valuable use.
• 4 The option to vary the firm's output or its production methods. Companies often build flexibility into their production facilities so that they can use the cheapest raw materials or produce the most valuable set of outputs. In this case they effectively acquire the option to exchange one asset for another.

The authors also make the point that, in most applications, real‐option valuation methods are a complement to, not a substitute for, the DCF method. Indeed DCF, which is best suited to and usually sufficient for safe investments and “cash cow” assets, is typically the starting point for real‐option analyses. In such cases, DCF is used to generate the values of the “underlying assets”—that is, the projects when viewed without their options or sources of flexibility.     

A Bayesian Approach to Real Options: The Case of Distinguishing between Temporary and Permanent Shocks

Traditional real options models demonstrate the importance of the “option to wait” due to uncertainty over future shocks to project cash flows. However, there is often another important source of uncertainty: uncertainty over the permanence of past shocks. Adding Bayesian uncertainty over the permanence of past shocks augments the traditional option to wait with an additional “option to learn.” The implied investment behavior differs significantly from that in standard models. For example, investment may occur at a time of stable or decreasing cash flows, respond sluggishly to cash flow shocks, and depend on the timing of project cash flows.     

Leverage, volatility and executive stock options

This paper studies how an optimal wage contract can be implemented using stock options, and derives the properties of the optimal contract with stock options. Specifically, we show how the exercise price and the size of the option grant should change in response to changes in exogenous parameters. First, for a fixed exercise price of executive stock options, the size of the option grant decreases in the riskiness of a desired investment policy, decreases in the volatility of return from the risky project, and increases in leverage. Second, for a fixed size of the option grant, the optimal exercise price of managerial stock options increases in the riskiness of a desired investment policy, increases in the volatility of return from the risky project, and decreases in leverage. Several empirical predictions are drawn from these conclusions regarding the pay-performance sensitivity of management compensation.     

Valuing Companies with Cash‐Flow@Risk

The classic DCF approach to capital budgeting—the one that MBA students in the world's top business schools have been taught for the last 30 years—begins with the assumption that the corporate investment decision is “independent of” the financing decision. That is, the value of a given investment opportunity should not be affected by how a company is financed, whether mainly with debt or with equity. A corollary of this capital structure “irrelevance” proposition says that a company's investment decision should also not be influenced by its risk management policy—by whether a company hedges its various price exposures or chooses to leave them unhedged. In this article, the authors—one of whom is the CFO of the French high‐tech firm Gemalto—propose a practical alternative to DCF that is based on a concept they call “cash‐flow@risk.” Implementation of the concept involves dividing expected future cash flow into two components: a low‐risk part, or “certainty equivalent,” and a high‐risk part. The two cash flow streams are discounted at different rates (corresponding to debt and equity) when estimating their value. The concept of cash‐flow@risk derives directly from, and is fully consistent with, the concept of economic capital that was developed by Robert Merton and Andre Perold in the early 1990s and that has become the basis of Value at Risk (or VaR) capital allocation systems now used at most financial institutions. But because the approach in this article focuses on the volatility of operating cash flows instead of asset values, the authors argue that an internal capital allocation system based on cash‐flow@risk is likely to be much more suitable than VaR for industrial companies.     

The Book-to-Price Effect in Stock Returns: Accounting for Leverage

This paper lays out a decomposition of book‐to‐price (B/P) that derives from the accounting for book value and that articulates precisely how B/P “absorbs” leverage. The B/P ratio can be decomposed into an enterprise book‐to‐price (that pertains to operations and potentially reflects operating risk) and a leverage component (that reflects financing risk). The empirical analysis shows that the enterprise book‐to‐price ratio is positively related to subsequent stock returns but, conditional upon the enterprise book‐to‐price, the leverage component of B/P is negatively associated with future stock returns. Further, both enterprise book‐to‐price and leverage explain returns over those associated with Fama and French nominated factors—including the book‐to‐price factor—albeit negatively so for leverage. The seemingly perverse finding with respect to the leverage component of B/P survives under controls for size, estimated beta, return volatility, momentum, and default risk.     

The Effect of “Private” and “Public” Risks on Oilfield Asset Pricing: Empirical Insights into the Georgetown Real Option Debate

The Spring 2005 issue of this journal featured a “debate” over the best way of applying real options. In “Real Options Analysis: Where Are the Emperor's Clothes?,” Adam B orison criticized most practices that go under the name real options and recommended an “integrated” approach that combines real options techniques with a traditional approach known as “decision tree analysis.” This approach breaks valuation problems into two components—“market” risks (say, oil price changes) and “private” risks (the possibility that actual reserves fall well short of estimated) — and then uses option pricing models to evaluate the market risks and decision trees for the private risks. In response to Borison's article, Tom Copeland and Vladimir Antikarov argued that these two components can be evaluated in a single analysis that uses both DCF (to calculate the value of the “underlying asset”) and Monte Carlo simulation (to estimate the volatility of the underlying), thereby expanding the range of real options applications. In this article, the authors attempt to shed light on this debate with the findings of their extensive empirical analysis of U.K. oilfield expansion options. The bottom line of their study is that size matters in the context of oilfields, presumably because it offers a reliable guide to the kind and size of risks associated with the project. In the case of the larger oilfields, where market risks are likely to outweigh the private risks, the author's findings suggest that both approaches are reasonably effective and provide roughly the same degree of accuracy. In the case of smaller fields, however, where private risks are proportionally larger, the authors conclude that Borison's approach is likely to be more reliable.     

A real options perspective on the future of the Euro

A break-up of the Eurozone is no longer regarded as implausible. This will be a costly and irreversible decision in conditions of continuing uncertainty; therefore it is amenable to analysis in the real options framework. We do so by solving as an n-dimensional optimal stopping problem with country-specific shocks and “convergence” of member economies. We compare a complete break-up with individual country departures. In calibrated solutions for a symmetric case we find a non-negligible but small option value. Furthermore, we find a new theoretical result on the non-monotonicity of abandonment threshold with respect to volatility.     

The Effect of Managerial Incentives to Bear Risk on Corporate Capital Structure and R&D Investment

In this study we use estimates of the sensitivities of managers' portfolios to stock return volatility and stock price to directly test the relationship between managerial incentives to bear risk and two important corporate decisions. We find that as the sensitivity of managers' stock option portfolios to stock return volatility increases firms tend to choose higher debt ratios and make higher levels of R&D investment. These results are even stronger in a subsample of firms with relatively low outside monitoring. For these firms, managerial incentives to bear risk play a particularly pivotal role in determining leverage and R&D investment.     

REAL OPTIONS PRIMER: A PRACTICAL SYNTHESIS OF CONCEPTS AND VALUATION APPROACHES

Strategic capital investment decisions are being made every day in an increasingly uncertain world. While the traditional NPV approach does a reasonable job of valuing simple, passively managed projects, it does not capture the many ways in which a highly uncertain project might evolve, and the ways in which active managers will influence this evolution. In cases where managerial flexibility is a major source of strategic value, companies will want to use real options valuation methods.
This article serves as a managerial tutorial on this newer, less understood approach. It uses simple examples to illustrate the essence of four basic categories of real options—timing, growth, production, and abandonment. The examples begin by taking a "binomial" approach to option valuation, in which the value of an investment initiative is allowed to take on two possible future values. Besides being used to illustrate the distinctive features of a real option, the binomial approach also serves to help the reader understand the alternative Black-Scholes valuation approach (though without requiring the reader to master the complex mathematics underlying Black-Scholes). Basic instructions for implementing both approaches are provided, along with a discussion of how to set appropriate discount rates and the important role of volatility assessment in the valuation process.     

Realizing the Potential of Real Options: Does Theory Meet Practice?

The idea of viewing corporate investment opportunities as “real options” has been around for over 25 years. Real options concepts and techniques now routinely appear in academic research in finance and economics, and have begun to influence scholarly work in virtually every business discipline, including strategy, organizations, management science, operations management, information systems, accounting, and marketing. Real options concepts have also made considerable headway in practice. Corporate managers are more likely to recognize options in their strategic planning process, and have become more proactive in designing flexibility into projects and contracts, frequently using real options vocabulary in their discussions. Thanks in part to the spread of real options thinking, today's strategic planners are more likely than their predecessors to recognize the “option” value of actions like the following: ? dividing up large projects into a number of stages; ? investing in the acquisition or production of information; ? introducing “modularity” in manufacturing and design; ? developing competing prototypes for new products; and ? investing in overseas markets. But if real options has clearly succeeded as a way of thinking, the application of real options valuation methods has been limited to companies in relatively few industries and has thus failed to live up to expectations created in the mid‐ to late‐1990s. Increased corporate acceptance and implementations of real options valuation techniques will require several changes coming together. On the theory side, we need more realistic models that better reflect differences between financial and real options, simple heuristic methods that can be more easily implemented (but that have been carefully benchmarked against more precise models), and better guidance on implementation issues such as the estimation of discount rates for the “optionless” underlying projects. On the practitioner side, we need user‐friendly real options software, more senior‐level buy‐in, more deliberate diffusion of real options knowledge throughout organizations, better alignment of managerial incentives with long‐term shareholder value, and better‐designed contracts to correct the misalignment of incentives across the value chain. If these challenges can be met, there will continue to be a steady if gradual diffusion of real options analysis throughout organizations over the next few decades, with real options eventually becoming not only a standard part of corporate strategic planning, but also the primary valuation tool for assessing the expected shareholder effect of large capital investment projects.     

清洁能源发电项目投资决策的实物期权模型及其应用

由于理论方法的假设与实际情况往往存在差异,传统DCF分析方法具有本质缺陷。同时,清洁能源发电项目投资具有很高的不可逆性、不确定性、竞争性和可持续发展特性,因此传统DCF分析方法不太适合清洁能源发电项目投资决策。基于此,本文提出了在清洁能源发电项目投资决策中引入实物期权的思路,并初步讨论了清洁能源发电项目投资的实物期权模型及其与企业策略相结合的方法。     

Valuing an Early‐Stage Biotechnology Investment as a Rainbow Option

In an article published in this journal in 2003, Richard Shockley and three of his students presented a detailed valuation of an early‐stage biotechnology investment using a binomial lattice option pricing model. The article demonstrates how investments with multiple stages can be treated as “compound sequential options”—that is, as series of options in which investments in one option provide the opportunity to invest in the next in the series. In this article, the author uses the same business case analyzed by Shockley et al. to demonstrate how to value this early‐stage biotechnology investment by separately modeling the two types of risks: technology and product market. An option that has two distinct kinds of risk that develop differently over time is known as a “rainbow option.” The key adjustment to the option pricing model required to value such an option is that, instead of the standard binomial option pricing model with two outcomes at each point in time, the author uses a “quadranomial” option pricing model with four outcomes at each point in time. By distinguishing technology risks from product market risks and allowing them to develop differently over time, the author's analysis leads to a very different valuation and, indeed, a different decision about the initial investment than the one produced by Shockley's model.     

CARE/CEASA Roundtable on Managing Uncertainty and Risk

Two distinguished Morgan Stanley “alumni” discuss how their management of risk and uncertainty has not only preserved but increased the profitability of their businesses. In both cases—one involving a commodities trading operation and the other a long‐short hedge fund—the key has been to find cost‐effective ways to “cut off the left tails” of the distribution by avoiding naked long or short positions and creating option‐like payoffs with limited downside. In the case of the hedge fund, the combination of longs and shorts with the use of other risk‐reducing strategies has enabled the fund's managers to produce twice the market's returns with only half the volatility (and only one losing year) during the 18‐year life of the fund. In the case of the commodities trading operation, the strategy is described as combining ownership of physical assets with the use of option pricing models to create what amount to “long gamma positions in the asset” that “produce payoffs regardless of whether the asset goes up or down in value.”     

Technological advances and the decision to invest

Technological advances impact a firm’s investment decision, as they affect the investment cost. They can also affect the profitability due to demand shocks. We study a firm’s optimal investment decision when technological advances occur as surprises and induce uncertain reductions in the investment cost and in earnings. Despite this complex setting we derive closed-form solutions for the investment option value and the investment threshold. When technological advances only impact the investment cost, we demonstrate significant contributions compared to existing research, which restricts the analysis by keeping the expected investment cost path constant. For example, we show that, albeit the investment threshold is constant, the option value is very sensitive in the expected impact of technological advances. Leaving the restrictive setting, we obtain more intuitive results, e.g. that more frequent technological advances increase the option value. When technological advances impact future earnings we find important long-term effects: the investment threshold increases, whereas the option value decreases. Finally, earnings volatility postpones investment, while uncertainty due to technological advances expedites investment.     

Valuation of Corporate Innovation and the Pricing of Risk in the Biopharmaceutical Industry: The Case of Gilead

Much of a firm's market value derives from expected future growth value rather than from the value of current operations or assets in place. Pharmaceutical companies are good examples of firms where much market value comes from expectations about drugs still in the development “pipeline.” Using a new osteoporosis drug being developed by Gilead Sciences, Inc., the author combines discounted cash flow methods values and real option models to value it. Alone, discounted cash flow (DCF) calculations are vulnerable to the assumptions of growth, cost of capital, and cash flows. But by integrating the real options approach with the DCF technique, one can value a new product in the highly regulated, risky and research‐intensive Biopharmaceutical industry. This article shows how to value a Biopharmaceutical product, tracked from discovery to market launch in a step‐by‐step manner. Improving over early real option models, this framework explicitly captures competition, speed of innovation, risk, financing need, the size of the market potential in valuing corporate innovation using a firm‐specific measure of risk and the industry‐wide value of growth operating cash flows. This framework shows how the risk of corporate innovation, which is not fully captured by the standard valuation models, is priced into the value of a firm's growth opportunity. The DCF approach permits top‐down estimation of the size of the industry‐wide growth opportunity that competing firms must race to capture, while the contingency‐claims technique allows bottom‐up incorporation of the firm's successful R&D investment and the timing of introduction of the new product to market. It also specifically prices the risk of innovation by modeling its two components: the consumer validation of technology and the expert validation of technology. Overall, it estimates the value contribution per share of a new product for the firm.     

BANK OF AMERICA ROUNDTABLE ON THE LINK BETWEEN CAPITAL STRUCTURE AND SHAREHOLDER VALUE

A number of popular business magazines have recently run cover stories describing the “return of leverage.” Although full of interesting details about individual leveraged deals and the investment bankers who put them together, they are largely silent on several issues of economic importance: Why is this happening now? What are the most important benefits as well as costs of debt financing? Is there such a thing as a value-maximizing, or “optimal,” capital structure for public corporations? No financial economist has thought and written as much about corporate capital structure and its relationship to shareholder value and corporate governance as Harvard professor Michael Jensen. The first economist to see the value-adding potential of LBOs in the 1980s, he was also the first to identify the source of the problems with the late-'80s deals. In this roundtable discussion, Professor Jensen explores the “real” effects of corporate financial policies on managerial decision-making and shareholder value with a distinguished group of corporate executives and financial advisors.     

Investment,consumption, and hedging under incomplete markets

Entrepreneurs often face undiversifiable idiosyncratic risks from their business investments. We extend the standard real options approach to an incomplete markets environment and analyze the joint decisions of business investments, consumption/savings, and portfolio selection. For a lump-sum investment payoff and an agent with a sufficiently strong precautionary savings motive, an increase in volatility can accelerate investment, contrary to the standard real options analysis. When the agent can trade the market portfolio to partially hedge against investment risk, the systematic volatility is compensated via the standard CAPM argument, and the idiosyncratic volatility generates a private equity premium. Finally, when the investment payoff is a series of flows, the agent's idiosyncratic risk exposure alters both the implied option value and the implied project value, causing a reversal of the results in the lump-sum payoff case.     

THE RISE IN MANAGERIAL STOCK OWNERSHIP

Despite the widespread view from Berle and Means onward that ownership of U.S. companies has become increasingly separated from managerial control, the authors report that managerial ownership of public corporations is markedly higher today than in 1935. Using a comprehensive sample of the 1,500 publicly traded firms in 1935 and a comparable sample of 4,200 firms in 1995, their study finds that managerial ownership increased from an average of 13% in 1935 to 21% in 1995. In terms of real (1995) dollar values, average managerial ownership increased from $18 million to $73 million over the same 60‐year period. One potential explanation for this increase is that greater reliance on managerial ownership has substituted for less reliance on other incentive alignment devices, such as pay‐for performance and the market for corporate control. The authors, however, report just the opposite. The use of such other corporate governance mechanisms has generally also increased over time, suggesting that the top managements of today's publicly traded corporations face greater pressure from investors and boards of directors than managements earlier in the century. An alternative explanation concern possible changes over time in the effects of certain company characteristics on the costs and benefits of using managerial ownership as a control device. While most of the characteristics the authors examined had the same relationship to managerial ownership in both periods, the role of volatility was different. In 1935, managerial ownership was inversely related to firm volatility; that is, higher volatility was associated with lower managerial ownership. In 1995, however, the relationship of managerial ownership to volatility was “nonlinear”; managerial ownership was positively related to firm volatility at low and moderate levels of volatility but the relationship turns negative when firm volatility is high. The overall lower level of volatility today, together with advances in capital markets and financial theory that have reduced the costs of hedging, appear to have reduced the costs of managers holding large stakes in their firms.