Decision making under uncertainty--real options to the rescue?

First coined by Myers (91) in 1977, the real options framework views decision-makers with the option to invest, grow, or abandon a project contingent upon the arrival of new information. Benchmarking off of the much researched and practiced financial risk management derivative products, real options attempt to quantify uncertain environments in a world of competition and 'real-time' technology. Scholes (105) defines "any security as a derivative if its price (or value) dynamics depends on the dynamics of some other underlying asset or assets and time." Using this concept, the value of a project can be viewed as a derivative of input costs, output yield, time, and uncertainty.

The seminal work of Black and Scholes (10) and Merton (85) in 1973 provided a method to properly value options. Their work led to an explosion of research in pricing all derivative products (also known as contingent claims) and to the wide acceptance and use of the Chicago Board Options Exchange (CBOE). Using Black, Scholes, and Merton concepts, companies are able to utilize financial derivative products to hedge risks unique to their business operations. Today, it is estimated that a nominal value of $70 trillion (Merton (86)) in financial derivative products, including futures, forwards, and options, is traded on the marketplace. Due to the apparent success of financial derivatives, it only seemed natural to utilize the contingent claims valuation process to assess project selection at the firm level.

Academia usually identifies evaluation techniques that are in-line with theory, and it takes many years for practitioners to adopt such ideas. Take for example, standard discounted cash flow (DCF) tools. First identified in the 1950s, the use of net present value (NPV) and cost of capital techniques did not replace payback period until the 1980s. In fact, in a survey by Gitman and Vandenberg (39), they compared cost of capital techniques used by major U.S. firms between 1980 versus 1997. After surveying the Fortune 1000 companies, only 35% of firms used cost of capital techniques in 1980. It wasn't until 1997 that 70% of firms utilized it. Additionally, in 1997 many firms have begun to differentiate between project risks, with 77% adopting some form of varying hurdle rate in their NPV analysis. Slow adoption into practice is not unusual, and real options analysis (ROA) has begun its acceptance in a similar fashion (i.e. first identified more than 20 years ago, but just now entering into the firm's decision-ma king process).

However, there is one major difference between ROA vs. DCF and payback period vs. DCF. ROA should not be viewed as an entirely new decision framework that will supplant all existing techniques. Our view is that DCF and ROA should be viewed as complementary decision-making tools. DCF techniques should be used for certain decision environments, whereas, ROA should be utilized for others. DCF tools should be used for decisions involving a moderately straightforward business structure, unsophisticated projects, and a steady environment that allows for dependable forecasts. Whereas, ROA should be utilized for uncertain business decisions that rely on the value of additional information. Therefore, ROA may be more useful for actively managing existing projects by delaying further investment and expanding or abandoning commitments. In order to perform a ROA, standard DCF tools are needed to calculate inputs for the option valuation. Therefore, a DCF approach should be performed first anyhow; only to be followed-up w ith the more labor intensive ROA, as necessary. FIGURE 1 provides a schematic of the complementary nature of ROA and DCF.

Lint and Pennings (70) agree with this sentiment of ROA complementing DCF analysis. In analyzing a new product development, they recognized that projects fall into one of four quadrants:

Quadrant 1 - Projects with high-expected payoff and low volatility. These projects represent the ideal decision-making environment. Traditional DCF analysis should be performed and projects should be activated as soon as possible.

Quadrant 2 - Projects with low expected payoff and low volatility. Traditional DCF tools should be used and the project should be abandoned as soon as possible.

Quadrant 3 - Projects with high-expected payoff and high volatility. These projects are more representative of today's investment in technology and highly competitive markets. ROA should be utilized to quantify this risk and decisions should be made with the arrival of new information.

Quadrant 4 - Projects with low expected payoff and high volatility. Similar to Quadrant 3, ROA should be used and these projects should only be activated with the arrival of "good" information.

More specifically to engineering economic decisions, Park and Herath (96) divide the investment categories according to varying levels of uncertainty -- the higher the uncertainty; the more a ROA will impact these decisions. Both high/low uncertainty replacement and expansion decisions are discussed in this context, as well as, mergers and acquisitions, research & development, and abandonment options.

Despite the complementary nature of ROA and DCF, ROA does provide additional benefits. As noted in Kogut and Kulatilaka (62), due to the evolution of institutions, methods of organization, and rules developed over the last century, firms have developed evaluation tools that address short-term profitability. If firms start viewing platform investments (i.e. invest a little now and wait for information) as long-term profit opportunities, then ROA can be used to quantify these long-term ventures. Rausser and Small (99) view these platform investments as "information rents" or option premiums. In other words, companies should view the costs of laying the foundation for long-term investments as the price to pay for the option to enter some business segment in the future.

Additionally, the value of an option comes from both the uncertainty of the investment environment and from the decision-maker's ability to take action to make the most of the opportunities created by that uncertainty. In essence, ROA is a means to quantify risk and uncertainty of individual projects on a risk-return framework similar to financial markets; thus, ROA's goal is to increase shareholder value. ROA allows some subjectivity to be removed from the decision process by providing a means of applying an objective, market-based measure of value to uncertain situations. From a modeling perspective, ROA minimizes the need to identify the decision-maker's utility function and the firm's risk-adjusted discount rate. It also provides a method to evaluate a nonlinear, or asymmetric, payoff function due to kinked economies of scale and production being a function of demand.

How WIDELY PRACTICED?

A large portion of real options applications have been published within the last five years, however, ROA applications date back to the mid-1980s. Earlier work focused on natural resource applications because of the existence of a publicly traded futures market to proxy option parameters. For example, Brennan and Schwartz (16) in 1985 utilized ROA to evaluate a natural resource investment using stochastic control theory to determine optimal policies for developing, managing, and abandoning projects. Since then, real options application papers have addressed numerous areas of industry to include biotechnology, manufacturing and inventory, natural resources, research & development, stock valuation, strategy, and technology. TABLE 1 provides a grouping of some of the application papers in the standard areas. Similar tabulations can be found in Trigeorgis (120) and Lander and Pinches (65). For educational purposes, the papers are listed in each category from more straightforward applications to complex developmen ts. For those new to real options, it is recommended to read the starred "*" applications first.

UNUSUAL APPLICATIONS -- REAL OPTIONS EVERYWHERE

In addition to the strategic decision areas, it is also worth noting some "unique" applications of real options to a variety of economic decisions. Mahajan (73) develops a ROA for pricing expropriation risk of a foreign project and points to the fact that many multinational firms maybe acting sub-optimally in handling their foreign exchange risk and country risk. Brown and Davis (17) compare two mutually exclusive projects with unequal service lives. In a real option framework, they include the option to switch from one project to another, in addition to the standard annual equivalent analysis. Saphores (104) uses ROA to determine the optimal number of times to spray a pest population during a farming season. Viewing the pest density as the uncertainty, a decision framework for a risk-neutral farmer is developed. Cortazar, Schwartz, and Salinas (29) evaluate firms complying with national laws to keep pollutants to certain levels. The option to invest in environmental technologies is analyzed, which leads to s ome surprising results. Due to the expensive technology, many firms should either cut back production or pay the fines levied for producing too many pollutants instead of investing in environmental technologies. Finally, Panayi and Trigeorgis (97) develop a compound option model to evaluate the international bank expansion of the Bank of Cyprus growing into the U.S.

STRATEGIC ENGINEERING ECONOMIC DECISIONS