Real Options Analysis and Strategic Decision Making By: Edward H. Bowman and Gary T. Moskowitz Organization Science, 2001, Vol. 12, No.6, pp. 772 – 777 by: Awais Ahmed

Introduction Investment decisions are critical processes by which an organization commits resources to future growth. These decisions are subject to a variety of internal pressures. Organizational theories and financial theories of investment valuation are rarely considered in tandem. 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 ( Amram and Kulatilaka, 1999). Real option model assumes an underlying source of uncertainty, such as the price of a commodity or the outcome of a research project. Over time, the outcome is revealed. Despite the theoretical attractiveness of the real options approach, its use by managers appears to be limited.

Research Question Article examines some of the practical organizational issues associated with the use of real options analysis. In strategic decision making managers face practical difficulties in using real option techniques. Illustrate these difficulties using Merck’s recent options calculation as an example. This example shows how the results of strategic analysis can differ from the assumptions of a typical real options model.

Valuing a Strategic Real Option Strategic researcher have suggested that several corporate decisions can be viewed through a strategy option lens. These include, for example the termination of a JV ( Kogut, 1991), R&D programs (Brealy and Myers,1981). These decision use a two-stage process a) Small invemsent gives company right to participate in the project (Purchases the Option), and b) When there are choices to make larger investments (Exercise the option). For capital budgeting purposes, a real option must be analyzed multiple times. First, the company must decide if the real option should be purchased. Second, evaluation is based on the differences between value of the project and the exercise price.

The Project and Data Real option analysis appears to present formidable problems for implementation. To demonstrate its feasible use and its relevant problem this use example of an option analysis performed by Merck. The data are based on information provided through managerial conversation. In the early 90s, Merck used option analysis to evaluate a proposed business relationship with a small biotechnology company (Sender, 1994). It is called a Project Gamma. Merck wanted to enter in new technology. Gamma had patented the technology but had not developed it. Merck inked the agreement, which resembles a call option, and can be analyzed using option analysis.

Merck’s Analysis Merck used the Black – Scholes model, which requires values for five parameters. The stock price, the exercise price, the time to expiration, the vitality and the risk free interest rate. Based on these parameters, the analysis shows that the value of the option exceeds the cost of option( except for two cases), so Merck agreed to license the technology and begin working on its commercial development. Tables 1 shows this analysis.

Limitations of the quantitative approach to real options The user of a quantitative model to value strategic real options faces a number of implementations problems (Lander and Pinches, 1998). These are (1) Model assumptions (2) Determining the inputs, and (3) Solving the option pricing algorithm. This study examine the first two problems below. (1) Modeling assumptions: Real options approach to quantitative decision making depends on the characteristics of the investment proposal being evaluated match the assumptions of the option valuation model. The analogy between financial and real options is imperfect making the financial option valuation models problematic for real options. In the Merck example , the use of the Black – Scholes model is problematic because of its embedded assumptions about the distribution of future stock prices.

Limitations of the quantitative approach to real options (2) - Determining the inputs: The effective use a quantitative input model to value a potential strategic investment is limited by the need to calculate the model's input. In examining the Merck case, the authors note that the company faced a number of problems in determining the values of the model’s inputs. These are – Stock price, volatility, time to expiration, exercise to price. In the strategic options research projects don’t have a readily observable stock price. As the outcome of the research is unknown until the project is completed. Merck used the volatility of stocks with a similar risk profile as its inputs. Option valuation model that exercise price of the option is fixed – if this is the case, then the value of the option changes dramatically.

The role of option analysis in strategic planning The real option approach to strategic analysis presents planners with a dilemma. Options are theoretically attentive way to think about the flexibility inherent in many invemsent proposals; however, the methodology presents many difficulties which lead erroneous conclusion. Overlay ambitious assumptions used by optimistic project champion lead errors in analysis. Merck example shows that the benefits of the real option approach was not simply the improved estimation. There were several technical objections to their estimations, ranging from their application of Black – Scholes model without correcting cash flows to the random walk process to the cash flow.

Conclusion 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 (Myer, 1984). Multiple forms of analysis are advantageous because the different methods act as check on each other. within the quantitative analysis step, strategic planners choose an appropriate valuation tool (e.g., options ) that matches the investment proposal. Overall, an options approach encourages experimentation and the proactive exploration of uncertainty.