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Decision – Strategy @ Risk

Strategy @ Risk

Tag: Decision

Uncertainty modeling
This entry is part 2 of 3 in the series What We Do

Prediction is very difficult, especially about the future.
Niels Bohr. Danish physicist (1885 – 1962)

Strategy @ Risks models provide the possibility to study risk and uncertainties related to operational activities; cost, prices, suppliers, markets, sales channels etc. financial issues like; interest rates risk, exchange rates risks, translation risk , taxes etc., strategic issues like investments in new or existing activities, valuation and M&As’ etc and for a wide range of budgeting purposes.

All economic activities have an inherent volatility that is an integrated part of its operations. This means that whatever you do some uncertainty will always remain.

The aim is to estimate the economic impact that such critical uncertainty may have on corporate earnings at risk. This will add a third dimension – probability – to all forecasts, give new insight: the ability to deal with uncertainties in an informed way and thus benefits above ordinary spread-sheet exercises.

The results from these analyzes can be presented in form of B/S and P&L looking at the coming one to five (short term) or five to fifteen years (long term); showing the impacts to e.g. equity value, company value, operating income etc. With the purpose of:
- Improve predictability in operating earnings and its’ expected volatility
- Improve budgeting processes, predicting budget deviations and its’ probabilities
- Evaluate alternative strategic investment options at risk
- Identify and benchmark investment portfolios and their uncertainty
- Identify and benchmark individual business units’ risk profiles
- Evaluate equity values and enterprise values and their uncertainty in M&A processes, etc.
Methods

To be able to add uncertainty to financial models, we also have to add more complexity. This complexity is inevitable, but in our case, it is desirable and it will be well managed inside our models.

People say they want models that are simple, but what they really want is models with the necessary features – that are easy to use. If something is complex but well designed, it will be easy to use – and this holds for our models.

Most companies have some sort of model describing the company’s operations. They are mostly used for budgeting, but in some cases also for forecasting cash flow and other important performance measures. Almost all are deterministic models based on expected or average values of input data; sales, cost, interest and currency rates etc.

We know however that forecasts based on average values are on average wrong. In addition will deterministic models miss the important uncertainty dimension that gives both the different risks facing the company and the opportunities they bring forth.

S@R has set out to create models that can give answers to both deterministic and stochastic questions, by linking dedicated Ebitda models to holistic balance simulation taking into account all important factors describing the company. The basis is a real balance simulation model – not a simple cash flow forecast model.

Both the deterministic and stochastic balance simulation can be set about in two different alternatives:
1. by a using a EBITDA model to describe the companies operations or
2. by using coefficients of fabrications (e.g. kg flour pr 1000 bread etc.) as direct input to the balance model – the ‘short cut’ method.
The first approach implies setting up a dedicated Ebitda subroutine to the balance model. This will give detailed answers to a broad range of questions about markets, capacity driven investments, operational performance and uncertainty, but entails a higher degree of effort from both the company and S@R. This is a tool for long term planning and strategy development.

The second (‘the short cut’) uses coefficients of fabrications and their variations, and is a low effort (cost) alternative, usually using the internal accounting as basis. This will in many cases give a ‘good enough’ description of the company – its risks and opportunities. It can be based on existing investment and market plans. The data needed for the company’s economic environment (taxes, interest rates etc) will be the same in both alternatives:

The ‘short cut’ approach is especially suited for quick appraisals of M&A cases where time and data is limited and where one wishes to limit efforts in an initial stage. Later the data and assumptions can be augmented to much more sophisticated analysis within the same ‘short cut’ framework. In this way analysis can be successively built in the direction the previous studies suggested.

This also makes it a good tool for short-term (3-5 years) analysis and even for budget assessment. Since it will use a limited number of variables – usually less than twenty – describing the operations, it is easy to maintain and operate. The variables describing financial strategy and the economic environment come in addition, but will be easy to obtain.

Used in budgeting it will give the opportunity to evaluate budget targets, their probable deviation from expected result and the probable upside or down side given the budget target (Upside/downside ratio).

Done this way analysis can be run for subsidiaries across countries translating the P&L and Balance to any currency for benchmarking, investment appraisals, risk and opportunity assessments etc. The final uncertainty distributions can then be “aggregated’ to show global risk for the mother company.

An interesting feature is the models ability to start simulations with an empty opening balance. This can be used to assess divisions that do not have an independent balance since the model will call for equity/debt etc. based on a target ratio, according to the simulated production and sales and the necessary investments. Questions about further investment in divisions or product lines can be studied this way.

Since all runs (500 to 1000) in the simulation produces a complete P&L and Balance the uncertainty curve (distribution) for any financial metric like ‘Yearly result’, ‘free cash flow’, economic profit’, ‘equity value’, ‘IRR’ or’ translation gain/loss’ etc. can be produced.

In some cases we have used both approaches for the same client, using the last approach for smaller daughter companies with production structures differing from the main companies.
The second approach can also be considered as an introduction and stepping stone to a more holistic Ebitda model.

Time and effort

The work load for the client is usually limited to a small team of people ( 1 to 3 persons) acting as project leaders and principal contacts, assuring that all necessary information, describing value and risks for the clients’ operations can be collected as basis for modeling and calculations. However the type of data will have to be agreed upon depending on the scope of analysis.

Very often will key people from the controller group be adequate for this work and if they don’t have the direct knowledge they usually know who to ask. The work for this team, depending on the scope and choice of method (see above) can vary in effective time from a few days to a couple of weeks, but this can be stretched from three to four weeks to the same number of months.

For S&R the time frame will depend on the availability of key personnel from the client and the availability of data. For the second alternative it can take from one to three weeks of normal work to three to six months for the first alternative for more complex models. The total time will also depend on the number of analysis that needs to be run and the type of reports that has to be delivered.

Selecting strategy

Models like this are excellent for selection and assessment of strategies. Since we can find the probability distribution for equity value, changes in this brought by different strategies will form a basis for selection or adjustment of current strategy. Models including real option strategies are a natural extension of these simulation models:

If there is a strategy with a curve to the right and under all other feasible strategies this will be the stochastic dominant one. If the curves crosses further calculations needs to be done before a stochastic dominant or preferable strategy can be found:

Types of problems we aim to address:

The effects of uncertainties on the P&L and Balance and the effects of the Boards strategies (market, hedging etc.) on future P&L and Balance sheets evaluating:
- Market position and potential for growth
- Effects of tax and capital cost
- Strategies
- Business units, country units or product lines – capital allocation – compare risk, opportunity and expected profitability
- Valuations, capital cost and debt requirements, individually and effect on company
- The future cash-flow volatility of company and the individual BU’s
- Investments, M&A actions, their individual value, necessary commitments and impact on company
- Etc.
The aim regardless of approach is to quantify not only the company’s single and aggregated risks, but also the potential, thus making the company capable to perform detailed planning and of executing earlier and more apt actions against uncertain factors.

Used in budgeting, this will improve budget stability through higher insight in cost side risks and income-side potentials. This is achieved by an active budget-forecast process; the control-adjustment cycle will teach the company to better target realistic budgets – with better stability and increased company value as a result.

This is most clearly seen when effort is put into correctly evaluating strategies-projects and investments effects on the enterprise. The best way to do this is by comparing and Choosing strategies by analyzing the individual strategies risks and potential – and select the alternative that is dominant (stochastic) given the company’s chosen risk-profile.

A severe depression like that of 1920-1921 is outside the range of probability. –The Harvard Economic Society, 16 November 1929
24/11/2010
When in doubt, develop the situation

Developing the situation is the common-sense approach to dealing with complexity. Both as a method and a mind-set, it uses time and our minds to actively build context, so that we can recognize patterns, discover options, and master the future as it unfolds in front of us (Blaber, 2008)

In our setting ‘developing the situation’ is the process of numerically describing (modelling) the company’s operations taking into account input from all parts of the company; sales, procurement, production, finance etc. This again has to be put into the company’s environment; tax regimes, interest and currency rates, investors expected return and all other stake holders expectations.

This is a context building process ending up with a map of the company’s operations giving clear roles and responsibilities to all departments and owners to each set of input data (assumptions).

Without including uncertainty and volatility in both assumptions and data, this is however only a two dimensional map. Adding the always present uncertainty gives us the third dimension and the option of innovation:

… discovering innovative options instead of being forced to default to the status quo. Developing the situation optimizes our potential to recognize patterns and discover innovative options because it’s synergistic with how the human mind thinks and makes decisions (Blaber, 2008)

Having calculated the cumulative probability distributions for key variable, new information is immediately available. Shape and localization tells us about underlying uncertainty and possible outcomes. Some distributions can be tweaked and some not. Characteristics of production like machine speed, error rates or the limit of air traffic movements are given and can only be changed over time with new investments. Other like sales, ebitda, profit etc. can be tweaked and in some cases even fine tuned by changing some of the exogenous variable or by introducing financial instruments or hedges etc.

Planning for an uncertain future is a hard task, but preparing for it by adapting to the uncertainties and risk uncovered is well within our abilities – giving us:

… freedom of choice and flexibility to adapt to uncertainties instead of avoiding them because they weren’t part of the plan. Happenstance, nature, and human behaviour all interact within an environment to constantly alter the situation. No environment is ever static. As the environment around us changes, developing the situation allows us to maintain our most prized freedom: the freedom of choice – to adapt our thinking and decision-making accordingly (Blaber, 2008)

Not all uncertainty represents risk of loss, but manifestations of opportunities given the right strategy, the means and will of implementation:

… having the audacity to seize opportunities, instead of neglecting them due to risk aversion and fear of the unknown. Risk aversion and fear of the unknown are direct symptoms of a lack of context, and are the polar opposites of audacity. The way to deal with a fear of the unknown isn’t to avoid it by doing nothing … (Blaber, 2008)

Pete Blaber’s book originally written on a totally different theme than ours can, as good books on strategy and hard earned experience from military planning, easily be adapted to our civilian purpose.

References

Blaber, P., (2008). The Mission, the Men, and Me. New York, Berkley Hardcover.

01/07/2009
Valuation as a strategic tool

This entry is part 1 of 2 in the series Valuation

Valuation is something usually done only when selling or buying a company (see: probability of gain and loss). However it is a versatile tool in assessing issues as risk and strategies both in operations and finance.

The risk and strategy element is often not evident unless the valuation is executed as a Monte Carlo simulation giving the probability distribution for equity value (or the value of entity). We will in a new series of posts take a look at how this distribution can be used.

By strategy we will in the following mean a plan of action designed to achieve a particular goal. The plan may involve issues across finance and operation of the company; debt, equity, taxes, currency, markets, sales, production etc. The goal usually is to move the value distribution to the right (increasing value), but it may well be to shorten the left tail – reducing risk – or increasing the upside by lengthening the right tail.

There are a variety of definitions of risk. In general, risk can be described as; “uncertainty of loss” (Denenberg, 1964); “uncertainty about loss” (Mehr &Cammack, 1961); or “uncertainty concerning loss” (Rabel, 1968). Greene defines financial risk as the “uncertainty as to the occurrence of an economic loss” (Greene, 1962).

Risk can also be described as “measurable uncertainty” when the probability of an outcome is possible to calculate (is knowable), and uncertainty, when the probability of an outcome is not possible to determine (is unknowable) (Knight, 1921). Thus risk can be calculated, but uncertainty only reduced.

In our context some uncertainty is objectively measurable like down time, error rates, operating rates, production time, seat factor, turnaround time etc. For others like sales, interest rates, inflation rates, etc. the uncertainty can only subjectively be measured.

“[Under uncertainty] there is no scientific basis on which to form any calculable probability whatever. We simply do not know. Nevertheless, the necessity for action and for decision compels us as practical men to do our best to overlook this awkward fact and to behave exactly as we should if we had behind us a good Benthamite calculation of a series of prospective advantages and disadvantages, each multiplied by its appropriate probability waiting to be summed.” (John Maynard Keynes, 1937)

On this basis we will proceed, using managers best guess about the range of possible values and most likely value for production related variables and market consensus etc. for possible outcomes for variables like inflation, interest etc. We will use this to generate appropriate distributions (log-normal) for sales, prices etc. For investments we will use triangular distributions to avoid long tails. Where, most likely values are hard to guesstimate or does not exist, we will use rectangular distributions.

Benoit Mandelbrot (Mandelbrot, 2004) and Taleb Nasim (Nasim, 2007) have rightly criticized the economic profession for “over use” of the normal distribution – the bell curve. The argument is that it has too thin and short tails. It will thus underestimate the possibility of far out extremes – that is, low probability events with high impact (Black Swan’s).

Since we use Monte Carlo simulation we can use any distribution to represent possible outcomes of a variable. So using the normal distribution for it’s statistically nicety is not necessary. We can even construct distributions that have the features we look for, without having to describe it mathematically.

However using normal distributions for some variables and log-normal for others etc. in a value simulation will not give you a normal or log-normal distributed equity value. A number of things can happen in the forecast period; adverse sales, interest or currency rates, incurred losses, new equity called etc. Together with tax, legal and IFRS rules etc. the system will not be linear and much more complex to calculate then mere additions, subtraction or multiplication of probability distributions.

We will in the following adhere to uncertainty and loss, where loss is an event where calculated equity value is less than book value of equity or in the case of M&A, less than the price paid.

Assume that we have calculated the value distribution (cumulative) for two different strategies. The distribution for current operations (blue curve) have a shape showing considerable downside risk (left tail) and a limited upside potential; give a mean equity value of $92M with a minimum of $-28M and a maximum of $150M. This, the span of possible outcomes and the fact that it can be negative compelled the board to look for new strategies reducing downside risk.

They come up with strategy #1 (green curve) which to a risk-averse board is a good proposition: reducing downward risk by substantially shortening the left tail, increasing expected value of equity by moving the distribution to the right and reducing the overall uncertainty by producing a more vertical curve. In numbers; the minimum value was reduced to $68M, the mean value of equity was increased to $112M and the coefficient of variation was reduced from 30% to 14%. The upside potential increased somewhat but not much.
To a risk-seeking board strategy#2 (red curve) would be a better proposition: the right tail has been stretched out giving a maximum value of $241M, however so have the left tail giving a minimum value to $-163M, increasing the event space and the coefficient of variation to 57%. The mean value of equity has been slightly reduced to $106M.

So how could the strategies have been brought about? Strategy #1 could involve introduction of long term energy contracts taking advantage of today’s low energy cost. Strategy #2 introduces a new product with high initial investments and considerable uncertainties about market acceptance.

As we now can see the shape of the value distribution gives a lot of information about the company’s risk and opportunities. And given the boards risk appetite it should be fairly simple to select between strategies just looking at the curves. But what if it is not obvious which the best is? We will return later in this series to answer that question and how the company’s risk and opportunities can be calculated.

References

Denenberg, H., et al. (1964). Risk and insurance. Englewood Cliffs, NJ: PrenticeHall,Inc.
Greene, M. R. (1962). Risk and insurance. Cincinnati, OH: South-Western Publishing Co.
Keynes, John Maynard. (1937). General Theory of Employment. Quarterly Journal of Economics.
Knight, F. H. (1921). Risk, uncertainty and profit. Boston, MA: Houghton Mifflin Co.
Mandelbrot, B., & Hudson, R. (2006). The (Mis) Behavior of Markets. Cambridge: Perseus Books Group.
Mehr, R. I. and Cammack, E. (1961). Principles of insurance, 3. Edition. Richard D. Irwin, Inc.
Rable, W. H. (1968). Further comment. Journal of Risk and Insurance, 35 (4): 611-612.
Taleb, N., (2007). The Black Swan. New York: Random House.

24/04/2009
Fish farming

When we in 2002 were asked to look into the risk of Cod fish farming, we had to start with the basics; how do cod feed and grow at different locations and what is the mortality at the same locations.

The first building block was Björn Björnsson’s paper; Björnsson, B., Steinarsson, A., Oddgeirsson, M. (2001). Optimal temperature for growth and feed conversion of immature cod. ICES Journal of Marine Science, 58: 29-38.

Together with: Björn Björnsson, Marine Research Institute, Iceland and Nils Henrik Risebro, University of Oslo, Norway we did the study presented in the attached paper – Growth, mortality, feed conversion and optimal temperature for maximum rate of increase in biomass and earnings in cod fish farming. (Growth, mortality, feed conversion and optimal temperature for maximum …..)

This formed the basis for a stochastic simulation model used to calculate the risk in investing in cod fish farming at different locations in Norway.

The stochastic part was taken from the “estimation errors” for the relations between growth, feed conversion, mortality etc. as function of deviation from optimal temperature.

As optimal temperature varies with cod size, temperature at a fixed location will during the year and over the production cycle deviate from optimal temperature. Locations with temperature profiles close to optimal temperature profile for growth in biomass will, other parameters held constant, are more favorable.

The results that came out favorably for certain locations were subsequently used as basis for an IPO to finance the investment.

The use of the model was presented as an article in Norsk Fiskeoppdrett 2002, #4 and 5. It can be downloaded here (See: Cod fish farming), even if it is in Norwegian some of the graphs might be of interest.

The following graph sums up the project. It is based on local yield in biomass relative to yield at optimal temperature profile for growth in biomass. Farming operation is simulated on different locations along the coast of Norway and local yield and its coefficient of variation (standard deviation divided by mean) is in the graph plotted against the locations position north. As we can see is not only the yield increasing as the location moves north, but also the coefficient of variation, indicating less risk in an investment.

The temperature profile for the locations was taken from the Institute of Marine Research publication: Hydrographic normals and long – term variations at fixed surface layer stations along the Norwegian coast from 1936 to 2000, Jan Aure and Øyvin Strand, Fisken og Havet, #13, 2001.

Locations of fixed termografic stations along the coast of Norway.

The study gives the monthly mean and standard deviation of temperature (and salinity) in the surface layer at the coastal stations between Sognesjøen and Vardø, for the period 1936 – 1989.

Monthly mean of temperature in the surface layer at all stations

By employing a specific temperature profile in the simulation model we were able to estimate the probability distribution for one cycle biomass at that location as given in the figure below.

Having the probability distribution for production we added forecasts for cost and prices as well as for their variance. The probability distributions for production also give the probability distribution for the necessary investment, so that we in the end were able to calculate the probability distribution for value of the entity (equity).

21/02/2009
The Probability of Gain and Loss

Every item written into a firm’s profit and loss account and its balance sheet is a stochastic variable with a probability distribution derived from probability distributions for each factor of production. Using this approach we are able to derive a probability distribution for any measure used in valuing companies and in evaluating strategic investment decisions. Indeed, using this evaluation approach we are able to calculate expected gain, loss and their probability when investing in a company where the capitalized value (price) is known.

For a closer study, please download Corporate-risk-analysis.

The Probability Distribution for the Value of Equity

The simulation creates frequency and cumulative probability distributions as shown in the figure below.

We can use the information contained in the figure to calculate the risk of investing in the company for different levels of the company’s market capitalization. The expected value of the company is 10.35 read from the intersection between probability curve and a line drawn from the 50% probability point on the left Y-axis.

The Probability Distribution for Gain and Loss

The shape of the probability curve provides concise information concerning uncertainty in calculating expected values of equity. Uncertainty is reduced the steeper the probability curve, whereas the flatter the curve so uncertainty is more evident. The figures below depicts the value of this type of information enabling calculation of expected gains or losses from investments in a company for differing levels of market capitalization.

We have calculated expected Gain or Loss as the difference between expected values of equity and the market capitalization; the ‘S’ curve in the graph shows this. The X-axis gives different levels of market capitalization; the right Y-axis gives the expected gain (loss) and the left y-axis the probability. Drawing a line from the 50% probability point to the probability curve and further to the right Y-axis point to the position where the expected gain (loss) is zero. At this point there is a 50/50 chance of realising or loosing money through investing in the company capitalized at 10.35, which is exactly the expected value of the company’s equity.

To the left of this point is the investment area. The green lines indicate a situation where the company is capitalized at 5.00 indicating an expected gain of 5.35 or more with a probability of 59% (100%-41%).

The figure to the right describes a situation where a company is capitalized above the expected value.

To the right is the speculative area where an industrial investor, with perhaps synergistic possibilities, could reasonably argue a valid case when paying a price higher than expected value. The red line in the figure indicates a situation where the company is capitalized at 25.00 – giving a loss of 14.65 or more with 78% probability.

To a financial investor it is obviously the left part – the investment area – that is of interest. It is this area that expected gain is higher than expected loss.

11/02/2009