VideoThat's not to say, however, that real options is pervasive in those industries, says Gardner Walkup, a partner and expert on real-options valuation in the Menlo Park, California, office of management-consulting firm Strategic Decisions Group. "These are obviously huge organizations," says Walkup of the mainly Fortune 100 energy companies that are his clients. "There are pockets within each of the organizations that feel very comfortable with the paradigm."
Although Walkup says real options is not by any means "the silver bullet that's going to answer everything," the technique is "at the point where it's within the lexicon of many companies and industries." The latter, in addition to the ones cited above, include automotive, aerospace, consumer goods, industrial products, and high tech. Intel, for one, is training finance employees in real-options valuation, and has used the technique to analyze a number of capital projects.
Fertile Fields
Real-options analysis could gain currency via its application in a number of functions that aren't industry-specific — such as supply-chain management. Inventory, for example, can be regarded as a real option, says Triantis, albeit a costly one. Build-to- order models, flexible assembly, contract manufacturing, and procurement contracts all offer numerous options that can be exploited. In high tech, as computer components become more commoditylike, with futures, options, and spot markets developing for items like memory chips, supply-chain managers will need to become skilled financial engineers, predicts Triantis. Real options could become one of their most valuable tools.
Information technology is another fertile, cross-industry field for applying real options. IT now consumes the greater part of corporate capital budgets, and large applications are notoriously risky. But their deployment can be optimized, and the risk minimized, through real-options analysis, according to Mark Jeffery, assistant professor of technology at Northwestern University's Kellogg School of Management. In a recent paper, Jeffery and co-authors Sandeep Shah and Robert J. Sweeney demonstrated how real-option analysis can determine the optimal rollout of an enterprise data warehouse, via the phase-wise consolidation of data marts.
What's more, Jeffery argues that real options can better optimize a portfolio of IT investments. The classical application of real options, and the point of much research, is to show that a given investment with a negative NPV may in fact have substantial value, thanks to its embedded options. But in today's capital-rationed environment, all IT investments are presumed to have a positive NPV, and a substantial one at that. Jeffery therefore advocates calculating the real-options value of positive-NPV projects, to arrive at an "expanded" NPV for each — and an optimal ranking of IT investments.
The trouble is, although there is widespread interest in taking a portfolio approach to managing IT investments, few companies — 24 percent — actually optimize such portfolios, according to a recent survey of 130 senior IT executives conducted by the Kellogg School, DiamondCluster International, and the Society for Information Management. None of the executives surveyed used real options.
Real Concerns
In the end, it will take more than research papers and case studies to persuade companies to adopt real options. Numerous objections must be overcome; here are four big ones.
Real options is a "black box." The sophisticated mathematics (such as partial differential equations) of real options, and the consequent lack of transparency and simplicity, are real concerns. But thanks to more-powerful PCs and spreadsheets, one can model multiple options with little more than a knowledge of high school algebra and binomial lattices, say experts. Jeffery, who has a PhD in theoretical physics, and his research assistant and co-author Shah are devising "little Excel macros that do the binomial model, so you can calculate compound options in a very straightforward way," he says. Meanwhile, software publishers like Decisioneering now offer off-the-shelf applications for modeling complex real-options scenarios.
"We've missed something really important," comments Martha Amram, chief economist at PLX Systems, a Pasadena, California-based software company, and a prominent real-options author. "To communicate, [real-options analysis] has to be transparent and clear."
Real options is a new economy tool. It doesn't help the cause that Enron was considered an innovative user of real options. Some observers maintain, however, that the reputation was deserved, and that use of the tool had little to do with Enron's financial difficulties or downfall. Meanwhile, loose talk about growth options may have helped fuel the astronomical valuations of some Internet companies before the market bubble burst. Then again, rigorous application of real options might have told a different story. A few years ago, when Amazon.com's stock was selling for $76 or so, an analysis using real-options theory by UCLA professor Eduardo S. Schwartz pegged its worth at around $12 — a more realistic value, as it turned out.
Real options only works for tradable assets. A common objection to real options is that it doesn't work when the underlying asset isn't tradable — that is, when the asset price over time can't be observed in the financial markets. Jeffery, however, points out that the key parameter in a real-options valuation is volatility, and that in order to estimate volatility, you need appropriate and sufficient data — such as historical R&D data, actuarial information, and so on. If data doesn't exist, it can be created. How? In a nutshell, by identifying the assumptions driving the bottom line of a project, then identifying the risks associated with those assumptions, then creating a statistical distribution of risk using Monte Carlo simulations. "It is possible to vary the risk drivers in a project and simulate financial-market data," says Jeffery. "If you take the lognormal of that distribution, the standard deviation is the volatility."
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