Real Options Analysis and Strategic Decision Making
Abstract:
The real options approach is frequently advocated as an approach that offers a positive and radical reassessment of the value of risk and exploration. We examine a recent case where Merck used the real options approach to justify an investment in an R&D project. This case is used to highlight some of the problems associated with using real options. We note that the assumptions incorporated in most standard option valuation models can conflict with the conclusions reached by strategic analysis. As a result, users of real options models should understand the quantitative aspects of these models, and may often need to create a customized model for each situation. The difficulty of developing customized models may explain, in part, the limited use of the real options approach in strategic analysis.
Introduction:
The investment decision represents a stylized description of the critical process by which organizations commit resources to future growth. Though such decisions are subject to a variety of internal pressures (see Cohen et al. 1972), companies nevertheless portray the investment decision as the outcome of a formalized process that employs explicit rules of valuation. It is unlikely that these rules are language games without real consequences (Astley 1985). Indeed, the teaching of financial methods is a pivotal component in the education of a MBA student.
Organizational theories and financial theories of investment valuation are rarely considered in tandem. Yet, they both have shared common treatments of risk as undesirable. Traditional corporate finance theory suggests that firms should use a discounted cash flow model (DCF) to analyze capital allocation proposals. Under this approach, the estimated cash flows from an investment project are discounted to their present value at a discount rate that reflects the market price of the project’s risk. Higher systematic risk reduces the attractiveness of a project. If a proposal has a positive present value, then the project should be funded. Unfortunately, this approach does not properly account for the flexibility that may be present in a project. For example, managers can increase the size of a production operation in response to higher-than- expected levels of demand, or cut funding for a research project that is not inventing marketable products. This flexibility has value—a value that is not captured by the traditional DCF methodology.
The real options approach has been suggested as a capital budgeting and strategic decision-making tool because it explicitly accounts for the value of future flexibility (Trigeorgis 1996, Amram and Kulatilaka 1999). Real options models are based on the assumption that there is an underlying source of uncertainty, such as the price of a commodity or the outcome of a research project. Over time, the outcome of the underlying uncertainty is revealed, and managers can adjust their strategy accordingly.
Despite the theoretical attractiveness of the real options approach, its use by managers appears to be limited. In some commodity-based operations, such as oil and mining, the real options approach appears to have gained some use by sophisticated companies (Coy 1999). In more strategic contexts, however, options’ analytic techniques are less frequently used (Copeland and Keenan 1998, Lander and Pinches 1998). In fact, when Merck & Co., Inc. recently used the Black-Scholes option valuation model to assess a proposed investment, its use of this analytic technique received attention in business publications, which found this to be newsworthy (e.g., Nichols 1994, Sender 1994, Thackray 1995).
This article examines some of the practical organizational issues associated with the use of real options analysis. We note many of the practical difficulties managers face in using real options techniques in strategic decision making. We illustrate some of these problems using Merck’s recent real options calculation as an example. This example shows how the results of strategic analysis can differ from the assumptions of a typical options model. As a result, the use of a standard options model in a strategic analysis could lead to poor strategic decisions. We conclude with a discussion of the role of op- tions analysis in strategic planning.
Project Gamma Option Valuation Analysis:
The first is the stock price. For this project, Merck took the expected present value of the cash flows from the project (i.e., the DCF value), assuming that the technology is successful and the plant is built. It is this value that has the variation, or the probability distribution, associated with it over the multi-year period. (Please note that stock price does not refer to Merck’s stock price; instead, it refers to the value of the project.) The calculation of the stock price excludes both (a) the cash flows for building the plant and the associated start-up costs (these costs are considered in the exercise price portion of the calculation) and (b) the upfront licensing and development costs (these costs are considered in the cost of the option). The stock price calculation is based on Merck’s best estimate as to the cash flows which would be generated by the project. This discounted cash flow valuation was made using traditional-net present-value techniques. In addition to the base case scenario, a number of sensitivity cases were run based on different assumptions about the success of the project. The base-case stock price was $28.5 million; the four sensitivity case stock prices were $22.5, $18, $15.8, and $15 million, because of the use of a variety of less-favorable assumptions.
The exercise price is the cost of building the plant and the associated start-up costs that would be incurred if the decision is made to commercialize the technology. Merck estimated that these costs would total $25.4 million. The time to expiration is based on the expected time to develop the product and build the factory. This was varied over two, three, and four years; after four years, it was felt that competing products would enter the market, making entry by Merck unfeasible.
The volatility is based on the annual standard deviation of returns for biotechnology stocks. These stocks appear to have a similar level of risk as Project Gamma. A volatility of 0.5 was used in the analysis and was provided by Merck’s investment banker. The risk-free interest rate is based on the then- prevailing yield on two- to four-year Treasury bonds. An interest rate of 4.5% was used in the calculation of the option value.
The Role of Option Analysis in Strategic Planning:
The real options approach to strategic analysis presents planners with a dilemma. Options are a theoretically attractive way to think about the flexibility inherent in many investment proposals; however, the use of the methodology presents many practical difficulties, which can lead all but the most careful users to make erroneous conclusions. The complexity of the options approach can also make it difficult to find errors in the analysis, or overly ambitious assumptions used by optimistic project champions. These practical difficulties may explain the limited use of real options analysis in strategic planning.
One approach to solving the problem of misspecified option valuation models is to create a more advanced, customized option valuation algorithm that better matches the characteristics of the investment proposal. The design, development, and computational solution of these advanced option models is often beyond the capabilities of corporate managers. Given the inherent difficulties in creating and solving these models, it is not surprising that advanced real options models are seldom used in strategic decision making.
The Merck example shows that the benefit of the real options approach was not simply the improved estimation. In fact, there are several technical objections to their estimation, ranging from their application of the BlackScholes model without correcting for the shortfall in the equilibrium cash flows (see Kogut and Kulatilaka 1994 for an applied analysis of this model) to the reasonableness of a random walk process to the cash flows. It is important to remember that numerical results are unlikely to be very sensitive to reasonable mathematical specifications of the cash flows’ dynamics. In unpublished estimations, we found that linear specifications of the option generate values similar to those found by Merck’s analysis using the Black-Scholes formula (available upon request). Whereas small deviations are worth fortunes in financial markets, they are fairly inconsequential in product markets.
This point echoes Bowman’s (1963) finding that the value of many decision rules lies more in the requirement for consistency than in the perennial search for optimality. In fact, the value of real options analysis is often found in the implications for project design rather than in the actual planning evaluation. For Merck, the key insight was to see the license as granting the right to exercise a future investment. Ordinarily, one might want to assign probabilities to good and bad investment scenarios and then take their mean values to estimate cash flows to understand whether to buy the license. The option perspective says that because the investment decision is contingent on buying the option, it is not reasonable to evaluate the license decision as if one must then make the future investment. Similarly, options analysis suggests the value of experimentation by breaking up the investment into a series of smaller sequential projects.
A formal quantitative valuation model is just one part of the overall strategic planning and capital allocation process. When making these decisions, companies need to perform both financial and strategic analysis (Myers 1984). Multiple forms of analysis are advantageous because the different methods act as a check on each other. For example, forecasting cash flows is notoriously difficult, making it problematic to use a capital allocation system that relies solely on financial analysis. Similarly, strategic analysis does not indicate whether the project offers a return that justifies its inherent risk. Within the quantitative analysis step, strategic planners choose an appropriate valuation tool (e.g., options or DCF) that matches the investment proposal.
Finally, we note that one potential advantage of using the real options analytic approach is that it might change the type of investment proposals that are reviewed. As Kogut and Kulatilaka (2001) note, an option perspective inverts the usual thinking about uncertainty absorption found in the organizational literature. If options are seen as a legitimate approach to analyzing proposals, then more option-like proposals may be considered. This increase can come from both a change in the types of new proposals that are generated (i.e., managers look for options to invest in), and from rethinking nonoption proposals to convert them into options. For example, one company we spoke with indicated that its planners were asked to consider a major capital expenditure to purchase new tooling for some of the firm’s factories. The planners converted this proposal into an option by suggesting that a pilot program be undertaken; if successful, then all of the factories would receive the new tooling. Thus, an options approach encourages experimentation and the proactive exploration of uncertainty. As more recent traditional organizational theories argue, this engagement in exploration is indeed a revolution in thinking.