Real options

In real life both for investment decisions and in valuation of companies there are managerial flexibility in the sense that at future points of time there is flexibility in choosing among alternatives.

When investing, the simplest example is the choice between to invest after a feasibility study or walk away. In valuation the choice can be at a future point of time to continue operation or disinvest.

These alternatives are real options available for the decision maker. Recognizing these real options will usually increase (reduce loss) the value of the investment or the company under valuation.

It is well known that most standard valuation techniques of risk-adjusted discounted cash flow (DCF) analysis fails to capture all sources of value associated with this type of investment, in that it assumes that the decision to invest is irreversible and inflexible, i.e., the investment cash flows are committed and fixed for the life of the project.

A main contribution of real options analysis is to incorporate managerial flexibility inherent in the project in its valuation. Added flexibility value, overlooked in DCF analysis, comes from managerial decisions that can take advantage of price movements: operating flexibility and investment timing flexibility.

Strategy @ Risk has the ability to incorporate a client’s specific decision alternative in the simulation model. Thus combining Monte Carlo simulation with decision tree analysis. The four-step process of the real option decision analysis is shown below.


Production Plant Case

The board faces the following situation: The company has a choice between building a plant with production capacity of 150 000 metric tons at a most likely cost of $450 mill. or a smaller plant with a capacity of 85 000 metric tons at a most likely cost of $300 mill..

The demand for the product is over 100 000 metric tons and rising. The decision between a small and large plant will be taken in year 1 and full production starts in year 2.

If the decision has been to build the smaller plant (at a higher cost per unit produced) the capacity can be increased by 65 000 metric tons at most likely cost of $275 mill. (Normal distributed with variance of ±25%). The decision to increase capacity will be taken in year 2 if the demand exceeds 110 000 metric tons. It is assumed that the demand is normally distributed with a most likely demand of 100 000 metric tons, and demand varies ±20% (upper and lower 5% limit). The demand later periods is assumed to have an increasing variance and a 30% autocorrelation

In year 3 and 4 it is considered that there is a 40% chance that if sales has been good (over 110 000 metric tons) a competitor will have entered the market reducing sales by 30 000 metric tons. If the demand falls below 70 000 metric tons the company will disinvest.

The decisions will be made on the value of the discounted cash flows (20% discount rate).
The above problem can be presented as a decision tree.


The boxes represent the “decision point”. The circles represent chance events. The chance events may be continuous, as is the case with demand forecasts, or discrete, as is the case of a competitor entering the market or not.

Net Present Value of the Alternatives

The analysis using both the decision tree and Monte Carlo simulation gives us the net present value of the different alternatives. As shown in the figures to the right, the best alternative is to build a large plant immediately giving a net present value of $679 mill.
A small plant will give a lower net present value (NPV $626 mill.) even if we increase the capacity at a later stage (NPV $637 mill.).


In this case it will never be profitable to disinvest at any point of time. This will always give a lower value.In some cases it is difficult to distinguish the best strategy from its alternatives. We will in a later post come back to selection strategies using stochastic dominance.

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S@R develops models for support of decision making under uncertainty. Taking advantage of recognized financial and economic theory, we customize simulation models to fit specific industries, situations and needs.

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