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Corporate Strategy – Page 5 – Strategy @ Risk

Strategy @ Risk

Category: Corporate Strategy

  • Decisions – Criteria for selection

    Decisions – Criteria for selection

    This entry is part 5 of 6 in the series Monte Carlo Simulation

    The risk is best expressed by using a graph illustrating the probability curve. The slope tells us about the uncertainty involved, the steeper the curve the less uncertainty involved.

    Having alternatives a study of the probability curve will ease the decision process. Since we can calculate the probability curve for any relevant item or metric like NOPLAT, EBIT, profit etc. a comparison between the alternatives makes the priority process more objective. Any board member or decisions maker can by the look at the probability curve understand the risk involved.

    The argumentation is also logical and follows the principle that it can be audited and tested. The discussion can rather debate the premises and their defined uncertainties since they give the consequences.

    Ordinary budgets not taken uncertainty into account is based on a deterministic and unrealistic assumtion and tells nothing about the uncertainty and risk involved.

  • The advantages of simulation modelling

    The advantages of simulation modelling

    This entry is part 6 of 6 in the series Monte Carlo Simulation

     

    All businesses need the ability to, if not predict the future; assess what its future economic performance can be. In most organizations this is done using a deterministic model, which is a model which does not consider the uncertainty inherent in all the inputs to the model. The exercise can best described as pinning jelly to a wall; it is that easy to find the one number which correctly describes the future.

    The apparent weakness of the one number which is to describe the future is usually paired with so called sensitivity analysis. Such analysis usually means changing the value of one variable, and observe what the result then is. Then another variable is changed, and again the result is observed. Usually it is the very extreme cases which are analyzed, and some times these sensitivities are even summed up to show extreme values and improbable downsides.

    Such a sensitivity analysis is as much pinning jelly to the wall as is the deterministic model itself. The relationship between variables is not considered, and rarely is the probability of each scenario stated.

    What the simulation model does is to model the relationship between variables, the probability of different scenarios, and to analyze the business as a complex whole. Each uncertain variable is assessed by key decision makers giving their estimates for

    • The expected value of the variable
    • The low value at a given probability
    • The high value at a corresponding probability level
    • The shape of the probability curve

    The relationship between variables is either modeled by its correlation coefficient or a regression.

    Then a simulation tool is needed to do the simulation itself. The tool uses the assigned probability curves to draw values from each of the curves. After a sufficient number of simulations, it will give a probability curve for the desired goal function(s) of the model, in addition to the variables themselves.

    As decision support this is an approach which will give you answers to questions like:

    • The probability of a project NPV being at a given, required level
    • The probability of a project or a business generating enough cash to run a successful business
    • The probability of default
    • What the risk inherent in the business is, in monetary terms
    • And a large number of other very useful questions

    The simulation model gives you a total view of risk where the sensitivity analysis or the deterministic analysis gives you only one number, without any known probability. And it also reveals the potential upside which is in every project. It is this upside which must be weighted against the potential downside and the risk level which is appropriate for each entity.

    The S@R-model

    The S@R-model is simulation tool which is built on proven financial and statistical technology. It is written in a language especially made for modelling financial decision problems, called Pilot Lightship. The model output is in the form of both probabilities for different aspects of a financial or business decision, and in the form of a fully fledged balance sheet and P&L. Hence, it gives you what you normally expect as output from at deterministic model, and in addition it gives you simulated results given defines probability curves and relationships between variables.

    The operational part of the business can be modeled either in a separate subroutine, or directly into the main part of the simulation tool, called the main model. For complex goal functions with numerous variables and relationships, it is recommended to use the subroutine, as it gives greater insight into the complexity of the business. Data from the operational subroutine is later transferred to the main model as a compiled file.

  • Risk and Monte Carlo simulation

    Risk and Monte Carlo simulation

    This entry is part 1 of 6 in the series Monte Carlo Simulation

     

    Risk, when does it occur? Whenever the outcome of a situation is not perfectly certain you have uncertainty or risk. Investment decisions taken under these circumstances involve a probability for an outcome that will differ from your estimated target. Decisions taken under uncertainty are a reality and a constraint manager’s face. In order to reduce the risk (probability of gain/loss) you have basically two ways of doing it, reduce the exposure or try to reduce the uncertainty by gathering more information.

    Risk – randomness with knowable probabilities.

    Uncertainty – randomness with unknowable probabilities.

    The problem with information is very often the lack of it due to cost and time factors. A major point in this context is that uncertainty can be reduced but risk can be calculated.

    We will illustrate this by describing a typical investment decision and look into the decisions and how they can be enhanced by taking advantage of calculating the risk by using Monte Carlo Simulation. This is a method especially developed to handle situations with uncertainty and to calculate the risk involved. The logic is fairly simple and the applications are numerous.

    Most business concepts involve various proportions of income, costs and investments. We will in the following use the philosophy that every decisions shall be taken in order to maximize shareholder value, corporate competitiveness and customer satisfaction.

    We have here split the decision process into various steps in order illustrate actually how easy it is to do it. By clicking on each theme you will see how we have given a flavor on how the problem can be solved.

  • Risk, price and value

    Risk, price and value

    This entry is part 3 of 4 in the series A short presentation of S@R

     

    Having arrived at the probability distribution for the value of equity (see full story) we are able to calculate expected gain, loss and their probability when investing in a company where the capitalized value (price) is known. (see “The Probability of Gain and Loss”)

    In the figure below we have illustrated the investment and speculative area. The investment area comprice the part of the cumulative probability distribution below 50%.

     

    investment_figure.jpg

    The speculative area is the area above 50%. The expected value is given at the 50% probability point (stapled line). The literature advices, and successful investors insists, on having a safety margin (discount) of at least 20% between expected value (intrinsic value) and the market price, as shown by the yellow area in the figure below. Graham and Dodd in Security Analysis introduced the concept of a margin of safety in 1934.

    In a stochastic framework as ours it is better to set the safety margin at one of the percentiles or quartiles giving directly the value of the safety margin. A fixed percentage safety margin will always give a different probability for gain (loss), depending on the shape of the cumulative probability distribution.

    An investor having a portfolio of stocks should thus use percentiles as a margin – having the same probability for gain (loss) throughout the portfolio. In the case below a 20% safety margin coincide with the first quartile, – giving a 25% probability for loss and 75% probability for gain. The expected value of the company is 1.452 the first quartile is 1.160 giving an exepcted gain of 292 or more with 75% probability (dotted lines).

    We know that the total risk of any individual asset is the sum of the systematic and unsystematic risk. When computing the figure above we have used the company’s appropriate beta to account for the systematic risk (in calculating WACC). The unsystematic risk is given by the variance in the figure above.

    In a well-diversified portfolio the expected value of the unsystematic return is assumed to be zero. When investing in a single asset we should be looking for assets with a high unsystematic return. In our context companies with a capitalized value below the percentile set as limit of the safety margin.

    References

    1. Security Analysis: The Classic 1934 Edition by Benjamin Graham, David L. Dodd. October 1, 1996, McGraw-Hill Professional Publishing; ISBN: 0070244960
    2. and an interesting webiste The Graham-Buffett Teaching Endowment
  • Real options

    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.

    roaprocess

    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.

    real-options-web

    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.).

    plant-alternatives

    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.

  • What we do; Predictive and Prescriptive Analytics

    What we do; Predictive and Prescriptive Analytics

    This entry is part 1 of 3 in the series What We Do

     

    Analytics is the discovery and communication of meaningful patterns in data. It is especially valuable in areas rich with recorded information – as in all economic activities. Analytics relies on the simultaneous application of statistical methods, simulation modeling and operations research to quantify performance.

    Prescriptive analytics goes beyond descriptive, diagnostic and predictive analytics; by being able to recommend specific courses of action and show the likely outcome of each decision.

    Predictive analytics will tell what probably will happen, but will leave it up to the client to figure out what to do with it.

    Prescriptive analytics will also tell what probably will happen, but in addition:  when it probably will happen and why it likely will happen, thus how to take advantage of this predictive future. Since there are always more than one course of action prescriptive analytics have to include: predicted consequences of actions, assessment of the value of the consequences and suggestions of the actions giving highest equity value for the company.

    By employing simulation modeling (Monte Carlo methods) we can give answers – by probability statements – to the critical question at the top of the value staircase.

     

    Prescriptive-analytics

     

    This feature is a basic element of the S@R balance simulation model, where the Monte Carlo simulation can be stopped at any point on the probability distribution for company value  (i.e. very high or very low value of company) giving full set of reports: P&L and balance sheet etc. – enabling a full postmortem analysis: what it was that happened and why it did happen.

    Different courses of actions to repeat or avoid the result with high probability can then be researched and assessed. The EBITDA client specific model will capture relationships among many factors to allow simultaneous assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Even the language we use to write the models are specially developed for making decision support systems.

    Our methods will as well include data and information visualization to clearly and effectively communicate both information and acquired knowledge – to reinforce comprehension and cognition.

    Firms may thus fruitfully apply analytics to business data, to describe, predict, and improve its business performance.