By Vimal Bahuguna
A young biotechnology firm has a promising new research direction. Its approach is unique and surprising and, if successful, could produce a drug worth billions. The firm needs capital to proceed with its research. But what is this research worth? How can a pharmaceutical company price a company whose project may very well fail? Alternatively, a pharmaceutical company has a number of scientists who have research proposals that if successful, could produce valuable drugs. Which of these directions should the company fund from its capital budget?
Pharmaceutical companies, plagued with these decisions, have recently begun to turn to option pricing, a statistical probability technique which ostensibly helps companies price research assets (see "Quantifying the Financial Value of R&D," IN VIVO, March 1998). Innovative research, according to those who favor options pricing theory, fits this category. The practice of using options—securing the right to own an asset rather than buying the asset itself—is not new to the life sciences industry. After all, the industry does think about deals and opportunities in the context of options on possibilities. This thinking is reflected in transactions commonly structured, for example, with variable royalties, milestones, and participation depending on success. Therefore, the key question boils down to not whether the options are useful financial tools, but what models to use to price these instruments.
One pricing theory, the Black-Scholes option pricing model, allows companies to price assets to improve capital budgeting where grave uncertainty exists. Pharmaceutical executives are intrigued by the possibilities of this model. Will the biotech firm we described earlier succeed with its research? We really don't know. The Black-Scholes model will, according to its proponents, help us navigate our decision-making process.
But our analysis of how Black-Scholes is actually applied leads us to believe that this theory in the context of pricing speculative pharmaceutical research is misplaced. It will, in fact, overwhelm managers as they begin questioning the decision-making tools that have served them so well in the past.
To begin this analysis, let us clarify how Black-Scholes is currently viewed by the pharmaceutical industry. Rightly, drug companies don't focus on options to hedge against the downside but to capture up-side value. Net present value (NPV) methods simply estimate future cash flow, discounting them at a specific rate in accordance with an assumed risk, and then compute the expected value of the project. Option pricing, a continuous arbitraging of risk, essentially evaluates the asset in a risk-neutral environment—since the risk can be readily traded away. This feature uncovers the value of the asset that sits "hibernating" in the traditional models of capital asset pricing such as NPV models. This may sometimes make an otherwise apparently marginal project worth pursuing. Option pricing theory does not draw its elegance from its insurance attributes, its ability to protect the downside—a security for which everyone pays a premium but which no one hopes to use.
As an investment instrument, an option is always riskier than the underlying asset: it provides no free lunch. Instead, option pricing is based on arbitrage pricing, which assumes that the underlying asset is liquid and can be bought and sold readily, without any discrete jumps to different price levels; that is, the underlying asset won't jump immediately in value from, say $100 million to $150 million, but will reach all intervening valuations, from $101 million through $149 million.
An option on a research program does not reflect either a single underlying asset nor a highly liquid one, nor does it expire worthless even when the option is not exercised (a compound that fails for one indication might succeed brilliantly in another). Moreover, these projects are always subject to huge discrete jumps in value. If a project succeeds at phase II, its value leaps to another level—the reason that deals signed after phase II are more valuable than those signed while phase II testing is still going on. While one could argue that value streams emanating from transactions around research assets, like royalties and milestone payments, can be separately valued, it's not easy to do so nor can they be easily traded.
Moreover, Black-Scholes entails a complex manipulation of statistical tools that require a huge database of comparable situations and transactions as well as a good understanding of the volatility of returns from completing the transaction. In the capital markets, the volatility—i.e., the standard deviation of returns over time—is relatively definable. But in pharmaceutical research, there are no reliable data for measuring volatility of returns. Few drug companies claim the ability to estimate the likelihood of specific returns based on a simple, or even a complex, simulation. Biology isn't amenable to such modeling. True, people using the Black-Scholes model often violate the theoretical construct by simplifying the underlying assumptions. However, complexities of biology do not allow for even a reasonable approximation of Black-Scholes conditions.
This complexity, in turn, forces the drug-company manager to rely on convenient surrogates, such as the stock price volatility of the biotech company, which theoretically mirrors the research project under consideration. Such a measure is essentially irrelevant to the underlying asset. Suddenly, the theory does not sound as pristine as the originators had envisioned.
There are industries in which Black-Scholes has important applications. Theorists would argue that asset ownership decisions are like evaluations of investment opportunities and, as such, can be treated like financial options. One of the most significant concepts embedded in this theory is that options give you a risk-free loan. An option with a strike price of $100 is essentially a loan of $100 from the asset owner which is payable only when one exercises the right to own the asset. The interest remains constant throughout the term of the option. The higher the market rate of interest, and the longer the time to maturity of the option, the higher the value of this option. This attribute of options has tremendous consequences for the interest-rate sensitive utility industry, where companies like New England Electric and Enron embrace the option value constructs enthusiastically in their capital budgeting process.
Oil companies are likewise partial to the technique, since the volatility of outcomes can be readily modeled, another prerequisite of option pricing. The volatility of finding oil can be reliably modeled using seismic survey information.
But in the drug and medical device industries, the cost of abandoning a program in the development phase, a key determinant of option value, is insignificant in comparison to expected cash flowing in from a successfully developed product. No wonder one is hard pressed in the pharma industry to show any significant differences between an option pricing model and a traditional NPV approach: there is no clear statistical reliability in the drug business.
NPV analysis certainly has flaws. Take two projects, one with an NPV of $100 million, the other with an NPV of $200 million. The first might return anywhere from $90 to $110 million; the second might yield returns from $10 million to $500 million. But the NPV analysis doesn't capture these spreads of possible outcomes: it simply gives a single expected value and therefore doesn't truly communicate the risks. Therefore alternatives to NPV methods must aim at addressing uncertainties and oversimplifications like projecting expected cash flow net of cost, and discounting the resultant cash flow using a weighted average cost of capital.
The biggest criticism of NPV has to be that positive NPV tells managers that it is OK to undertake a project immediately when it meets the hurdle rate criteria. The NPV has no way of informing the managers what will be gained or lost if the project is delayed by a day, a month, or a year. In fact, very few managers actively pursue the question: what are the returns on decisions taken after generating more information?
Just-in-time decision making does precisely that—calculates within an NPV analysis the value of additional information. Application of simple decision trees coupled with NPV of time-specific cash flow, utilizing information-adjusted discount rates, has yielded elegant results.
In this example, the adjusted project NPVs after procuring the additional information are $100 million for a major success and $30 million for a moderate success versus $110 million and $35 million without the additional information. By summing the NPVs of major and moderate successes with and without additional information and then subtracting the smaller sum (the NPV without additional information, or $65 million) from the larger (the NPV with additional information, or $74.8 million), one can determine the NPV of the additional information—in this case $9.8 million.
As compelling as the financial logic of just-in-time decision making is its perceived fairness and transparency in discriminating between projects with differing incremental information needs. Just-in-time decision making relies on compelling logic and informed intuition of the managers who ought to know rather than flawed default indicators, like comparable biotech stock prices masquerading as decision variables. It is this lack of logic and intuition in traditional NPV approaches that has prompted academics like Stephen Ross of Yale to lament that "whenever a company called a particular decision ‘strategic,' it was because it was a negative NPV decision that they wanted to undertake."
Just-in-time decision making does not imply "just wait." To the contrary, properly utilized it provides a vehicle for action by clearly highlighting what critical pieces of information are needed, as well as the steps necessary to secure that information as quickly as possible. It requires the willingness to follow a predetermined course of action the moment the critical information is in hand. It is not about waiting for that last piece of information. The value of incremental information must be constantly evaluated for new insights. Simply postponing a decision in order to buy time will do nothing except dissipate the ultimate value of the asset being considered. Delays in decision making will create opportunities for competitors to usurp competitive advantage.
In the right environment, just-in-time decision making is a formidable tool for creating competitive advantage. It transcends the obvious: of course one should get as much information as possible before making firm decisions; and therefore, of course one should take decisions at the last minute. The key is to build competencies that will enable assigning a more accurate value to an asset with less incremental information than the competition can. It is all right to wait until the last minute to exercise asset ownership decisions, as long as your last minute is a minute sooner than your competitor's.
Managers will do well to realize that thinking smart does not necessarily entail adopting exotic theoretical constructs, like Black-Scholes, that may have utility in other industries, but not in drugs and devices. In fact, managers have a tremendous opportunity to create value by driving just-in-time decision-making processes. Besides, they will be able to satisfy their option theorist friends by, rightly, suggesting that a just-in-time decision is a way to exercise an option after more information is available—a very valuable option considering that, in a connected world, the cost of procuring additional information may be far less than what was once imagined. But the just-in-time decision maker, in contrast to the user of option pricing models, will be using a tool set that is far more comprehensible and familiar.
So what's a manager to do? For starters, he or she has to learn strategic patience—waiting for strategic pieces of information that will make an option to own an asset valuable. But in drug companies, managers must be seen to manage—not indulge in strategic patience. Such pressure draws managers away from this important managerial trait. Managers have to learn to make investment decisions just in time—never too soon. Our advice: accelerate the information needed to own an asset, not the decision to own it.
Option pricing is a seductive, but ultimately, inappropriate tool for life sciences industries. Companies should rely on the techniques that have served them well and focus on accelerating information gathering and postponing decision making until the time is right. Black-Scholes gives pharmaceutical executives false expectations and the belief that uncertainly can be banished. It cannot.
Vimal Bahuguna is Managing Principal at the New Jersey-based health care consulting firm Bogart Delafield Ferrier (www.bdf.com)