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Balance sheet simulation – Strategy @ Risk

Strategy @ Risk

Category: Balance sheet simulation

Simulation of balance sheet risk
This entry is part 6 of 6 in the series Balance simulation

As I wrote in the article about balance sheet risk, a company with covenants in its loan agreements may have to hedge balance sheet risk even though it is not optimal from a market risk perspective.

But how can the company know which covenant to hedge? Often a company will have more than one covenant, and hedging one of them may adversely impact the other. To answer the question it is necessary to calculate the effect of a hedge strategy, and the best way to do that is by using a simulation model. Such a model can give the answer by estimating the probability of breech of a covenant.

Which hedging strategy the company is to choose demands knowledge about what covenant is the most at risk. How likely is it that the company will face a breech? Like I described in the previous article:

Which hedging strategy the company chooses depends on which covenant is most at risk. There are inherent conflicts between the different hedging strategies, and therefore it is necessary to make a thorough assessment before implementing any such hedging strategy.

In addition:

If the company hedges gearing, the size of the equity will be more at risk [..], And in addition, drawing a larger proportion of debt in the home (or functional) currency may imply an increase in economic risk. [..] Hence, if the company does not have to hedge gearing it should hedge its equity.

To analyse the impact of different strategies and to answer the questions above I have included simulation of currency rates in the example from the previous article:

The result of strategy choice given a +/- 10% change in currency rates was shown in the previous article. But that model cannot give the answer to how likely it is that the company will face a breech situation. How large changes in currency rates can the company take?

To look at this issue I have used the following modeling of currency rates:
- Rates at the last day of every quarter from 31/12/02 to 30/06/2013. The reason for choosing these dates is of course that they are the dates when the balance sheet is measured. It doesn’t matter if the currency rates are unproblematic March 1^st if they are problematic March 31^st. Because that is the date when books are closed for Q1 and the date when the balance sheet is measured.
- I have analysed the rated using Excel @Risk, which can fit a probability curve on historical rates. There are, of course, many methods for estimating currency rates and I will get back to that later. But this method has advantages; the basis is actual rates which have actually occurred.
The closest fit to the data was a LapLace-curve ((RiskLaplace (μ,σ) specifies a laplace distribution with the entered μ location and σ scale parameters. The laplace distribution is sometimes called a “double exponential distribution” because it resembles two exponential distributions placed back to back, positioned with the entered location parameter.)) for EUR and a Uniform-curve ((RiskUniform(minimum,maximum) specifies a uniform probability distribution with the entered minimum and maximum values. Every value across the range of the uniform distribution has an equal likelihood of occurrence)) for USD against NOK.

It is always a good idea to ask yourself if the fitted result has a good story behind it. Is it logical? What we want is to find a good estimate for future currency rates. If the logic is hard to see, we should go back and analyze more. But there seems to be a good logic/story behind these estimates in my opinion:
- EUR against NOK is so called mean reverting, meaning that it normally will revert back to a level of around 8 NOK +/- for 1 EUR. Hence, the curve is pointed and has long tails. We most likely will have to pay 8 NOK for 1 EUR, but it can move quite a bit away from the expected mean, both up and down.
- USD is more unpredictable against NOK and a uniform curve, with any level of USD/NOK being as likely, sound like a good estimate.
In addition to the probability curves for USD and EUR an estimate for the correlation between them is needed. I used the same historical data to calculate historical correlation. On the end quarter rates it has been 0,39. A positive correlation means that the rates move the same way – if one goes up, so does the other. The reason is that it was the NOK that moved against both currencies. That’s also a good assessment, I believe. History has shown it to be the case.

Now we have all the information needed to simulate how much at risk our (simple) balance sheet is to adverse currency movements. And based on the simulation, the answer is: Quite a bit.

I have modeled the following covenants:
- Gearing < 1,5
- Equity > 3 000
This is the result of the simulation (click on the image to zoom):

Gearing is the covenant most at risk, as the tables/graphs show. Both in the original mix (all debt in NOK) and if the company is hedging equity there is a high likelihood of breaching the gearing covenant.

There is a probability of 22% in the first case (all debt in NOK) and a probability of 23% in the second (equity-hedge). This is a rather high probability, considering that the NOK may move quite a bit, quit quickly.

The equity is less at risk and the covenant has more headroom. There is a 13% probability for breech with all debt in NOK, but 0% should the company choose either of the two hedging strategies. This is due to the fact that currency loans will reduce risk, regardless of whether debt fully hedges assets, or only partially.

Hence, based on this example it is easy to give advice to the company. The company should hedge gearing by drawing debt in a mix of currencies reflecting its assets. Reality is of course more complex than this example, but the mechanism will be the same. And the need for accurate decision criteria – likelihood of breech – is more important the more complex the business is.

One thing that complicates the picture is the impact different strategies have on the company’s debt. Debt levels may vary substantially, depending on choice of strategy.

If the company has to refinance some of its debt, and at the same time there is a negative impact on the value of the debt (weaker home currency), the refinancing need will be substantially higher than what would have been the case with local debt. This is also answers you can get from the simulation modeling.

The answer to the questions: “How likely is it that the company to breech its covenants and what are the consequences of strategic choices on key figures, debt and equity?” is something really only a good simulation model can give.

Originally published in Norwegian.
20/09/2013
Working Capital Strategy Revisited
This entry is part 3 of 3 in the series Working Capital

Introduction

To link the posts on working capital and inventory management, we will look at a company with a complicated market structure, having sales and production in a large number of countries and with a wide variety of product lines. Added to this is a marked seasonality with high sales in the years two first quarters and much lower sales in the years two last quarters ((All data is from public records)).

All this puts a strain on the organizations production and distribution systems and of course on working capital.

Looking at the development of net working capital ((Net working capital = Total current assets – Total current liabilities)) relative to net sales it seems as the company in the later years have curbed the initial net working capital growth:

Just by inspecting the graph however it is difficult to determine if the company’s working capital management is good or lacking in performance. We therefore need to look in more detail at the working capital elements and compare them with industry ‘averages’ ((By their Standard Industrial Classification (SIC) )).

The industry averages can be found from the annual “REL Consultancy /CFO Working Capital Survey” that made its debut in 1997 in the CFO Magazine. We can thus use the survey’s findings to assess the company’s working capital performance ((Katz, M.K. (2010). Working it out: The 2010 Working Capital Scorecard. CFO Magazine, June, Retrieved from http://www.cfo.com/article.cfm/14499542
Also see: https://www.strategy-at-risk.com/2010/10/18/working-capital-strategy-2/)).

The company’s working capital management

Looking at the different elements of the company’s working capital, we find that:

I.   Day’s sales outstanding (DSO) is on average 70 days compared with REL’s reported industry median of 56 days.

II.   Day’s payables outstanding (DPO) is the difference small and in the right direction, 25 days against the industry median of 23 days.

III.   Day’s inventory outstanding (DIO) on average 138 days compared with the industry median of 23 days, and this is where the problem lies.

IV.   The company’s days of working capital (DWC = DSO+DIO-DPO) (( Days of working capital (DWC) is essentially the same as the Cash Conversion Cycle (CCC). Se endnote for more.)) have on average according to the above, been 183 days over the last five years compared to REL’s median DWC of 72 days in for comparable companies.

This company thus has more than 2.5 times ‘larger’ working capital than its industry average.

As levers of financial performance, none is more important than working capital. The viability of every business activity rests on daily changes in receivables, inventory, and payables.

The goal of the company is to minimize its ‘Days of Working Capital’ (DWC) or which is equivalent the ‘Cash Conversion Cycle’ (CCC), and thereby reduce the amount of outstanding working capital. This requires examining each component of DWC discussed above and taking actions to improve each element. To the extent this can be achieved without increasing costs or depressing sales, they should be carried out:

1.   A decrease in ‘Day’s sales outstanding’ (DSO) or in ‘Day’s inventory outstanding’ (DIO) will represent an improvement, and an increase will indicate deterioration,

2.   An increase in ‘Day’s payables outstanding’ (DPO) will represent an improvement and an decrease will indicate deterioration,

3.   Reducing ‘Days of Working Capital’ (DWC or CCC) will represent an improvement, whereas an increasing (DWC or CCC) will represent deterioration.

Day’s sales- and payables outstanding

Many companies think in terms of “collecting as fast as possible, and paying as slowly as permissible.” This strategy, however, may not be the wisest.
At the same time the company is attempting to integrate with its customers – and realize the related benefits – so are its suppliers. A “pay slow” approach may not optimize either the accounts or inventory, and it is likely to interfere with good supplier relationships.

Supply-chain finance

One way around this might be ‘Supply Chain Finance ‘(SCF) or reverse factoring ((“The reverse factoring method, still rare, is similar to the factoring insofar as it involves three actors: the ordering party, the supplier and the factor. Just as basic factoring, the aim of the process is to finance the supplier’s receivables by a financier (the factor), so the supplier can cash in the money for what he sold immediately (minus an interest the factor deducts to finance the advance of money).” http://en.wikipedia.org/wiki/Reverse_factoring)). Properly done, it can enable a company to leverage credit to increase the efficiency of its working capital and at the same time enhance its relationships with suppliers. The company can extend payment terms and the supplier receives advance payments discounted at rates considerably lower than their normal funding margins. The lender (factor), in turn, gets the benefit of a margin higher than the risk profile commands.

This is thus a form of receivables financing using solutions that provide working capital to suppliers and/or buyers within any part of a supply chain and that is typically arranged on the credit risk of a large corporate within that supply chain.

Day’s inventory outstanding (DIO)

DIO is a financial and operational measure, which expresses the value of inventory in days of cost of goods sold. It represents how much inventory an organization has tied up across its supply chain or more simply – how long it takes to convert inventory into sales. This measure can be aggregated for all inventories or broken down into days of raw material, work in progress and finished goods. This measure should normally be produced monthly.

By using the industry typical ‘days inventory outstanding’ (DIO) we can calculate the potential reduction in the company’s inventory – if the company should succeed in being as good in inventory management as its peers.

If the industry’s typical DIO value is applicable, then there should be a potential for a 60 % reduction in the company’s inventory.

Even if this overstates the true potential it is obvious that a fairly large reduction is possible since 98% of the 1000 companies in the REL report have a value for DIO less than 138 days:

Adding to the company’s concern should also be the fact that the inventories seems to increase at a faster pace than net sales:

Inventory Management

Successfully addressing the challenge of reducing inventory requires an understanding of why inventory is held and where it builds in the system.
Achieving this goal requires a focus on inventory improvement efforts on four core areas:
1. demand management – information integration with both suppliers and customers,
2. inventory optimization – using statistical/finance tools to monitor and set inventory levels,
3. transportation and logistics – lead time length and variability and
4. supply chain planning and execution – coordinating planning throughout the chain from inbound to internal processing to outbound.
We believe that the best way of attacking this problems is to produce a simulation model that can ‘mimic’ the sales – distribution – production chain in necessary detail to study different strategies and the probabilities of stock-out and possible stock-out costs compared with the costs of doing the different products (items).

The costs of never experience a stock-out can be excessively high – the global average of retail out-of-stocks is 8.3% ((Gruen, Thomas W. and Daniel Corsten (2008), A Comprehensive Guide to Retail Out-of-Stock Reduction in the Fast-Moving Consumer Goods Industry, Grocery Manufacturers of America, Washington, DC, ISBN: 978-3-905613-04-9)) .

By basing the model on activity-based costing, it can estimate the cost and revenue elements of the product lines thus either identify and/or eliminate those products and services that are unprofitable or ineffective. The scope is to release more working capital by lowering values of inventories and streamlining the end to end value chain

To do this we have to make improved forecasts of sales and a breakdown of risk and economic values both geographically and for product groups to find out were capital should be employed coming years (product – geography) both for M&A and organic growth investments.

A model like the one we propose needs detailed monthly data usually found in the internal accounts. This data will be used to statistically determine the relationships between the cost variables describing the different value chains. In addition will overhead from different company levels (geographical) have to be distributed both on products and on the distribution chains.

Endnote

Days Sales Outstanding (DSO) = AR/(total revenue/365)

Year-end trade receivables net of allowance for doubtful accounts, plus financial receivables, divided by one day of average revenue.

Days Inventory Outstanding (DIO) = Inventory/(total revenue/365)

Year-end inventory plus LIFO reserve divided by one day of average revenue.

Days Payables Outstanding (DPO) = AP/(total revenue/365)

Year-end trade payables divided by one day of average revenue.

Days Working Capital (DWC): (AR + inventory – AP)/(total revenue/365)

Where:
AR = Average accounts receivable
AP = Average accounts payable
Inventory = Average inventory + Work in progress

Year-end net working capital (trade receivables plus inventory, minus AP) divided by one day of average revenue. (DWC = DSO+DIO-DPO).

For the comparable industry we find an average of: DWC=56+39-23=72 days

Days of working capital (DWC) is essentially the same as the Cash Conversion Cycle (CCC) except that the CCC uses the Cost of Goods Sold (COGS) when calculating both the Days Inventory Outstanding (DIO) and the Days Payables Outstanding (DPO) whereas DWC uses sales (Total Revenue) for all calculations:

CCC= Days in period x {(Average inventory/COGS) + (Average receivables / Revenue) – (Average payables/[COGS + Change in Inventory)]

Where:
COGS= Production Cost – Change in Inventory

Footnotes
27/11/2012
M&A: When two plus two is five or three or …

When two plus two is five (Orwell, 1949)

Introduction

Mergers & Acquisitions (M&A) is a way for companies to expand rapidly and much faster than organic growth – that is coming from existing businesses – would have allowed. M&A’s have for decades been a trillion-dollar business, but empirical studies reports that a significant proportion must be considered as failures.

The conventional wisdom – is that the majority of deals fail to add shareholder value to the acquiring company. According to this research, only 30-50% of deals are considered to be successful (See Bruner, 2002).

If most deals fail, why do companies keep doing them? Is it because they think the odds won’t apply to them, or are executives more concerned with extending its influence and company growth (empire building) and not with increasing their shareholder (s) value?

Many writers argue that these are the main reasons driving the M&A activities, with the implication that executives are basically greedy (because their compensation is often tied to the size of the company) – or incompetent.

To be able to create shareholder value the M&A must give rise to some forms of synergy. Synergy is the ability of the merged companies to generate higher shareholder value (wealth) than the standalone entities. That is; that the whole will be greater than the sum it’s of parts.

For many of the observed M&A’s however, the opposite have been the truth – value have been destroyed; the whole have turned out to be less than the sum of its parts (dysergy).

“When asked to name just one big merger that had lived up to expectations, Leon Cooperman, former co-chairman of Goldman Sachs’ Investment Policy Committee, answered: I’m sure there are success stories out there, but at this moment I draw a blank.” (Sirower, 1997)

The “apparent” M&A failures have also been attributed to both methodological and measurement problems, stating that evidence – as cost saving or revenue enhancement brought by the M&A is difficult to obtain after the fact. This might also apply to some of the success stories.

What is surprising in most (all?) of the studies of M&A success and failures is the lack understanding of the stochastic nature of business activities. For any company it is impossible to estimate with certainty its equity value, the best we can do is to estimate a range of values and the probability that the true value will fall inside this range. The merger two companies amplify this, and the discussion of possible synergies or dysergies can only be understood in the context of randomness (stochasticity) ((See: the IFA.com – Probability Machine, Galton Board, Randomness and Fair Price Simulator, Quincunx at http://www.youtube.com/watch?v=AUSKTk9ENzg)).

[tube] http://www.youtube.com/watch?v=AUSKTk9ENzg, 400,300 [/tube]

The M&A cases

Let’s assume that we have two companies A and B that are proposed merged. We have the distribution for each company’s equity value (shareholders value) for both companies and we can calculate the equity distribution for the merged company. Company A’s value is estimated to be in the range of 0 to 150M with expected value 90M. Company B’s value is estimated to be in the range of -40 to 200M with expected value 140M. (See figure below)

If we merge the two companies assuming no synergy or dysergy we get the value (shareholder) distribution shown by the green curve in the figure. The merged company will have a value in the range of 65 to 321M, with an expected value of 230M. Since there is no synergy/dysergy no value have been created or destroyed by the merger.

For company B no value would be added in the merger if A was bought at a price equal to or higher than the expected value of the company. If it was bought at a price less than expected value, then there is a probability that the wealth of the shareholders of company B will increase. But even then it is not with certainty. All increase of wealth to the shareholders of company B will be at the expenses of the shareholders of company A and vice versa.

Case 1

If we assume that there is a “connection” between the companies, such that an increase in one of the company’s revenues also will increase the revenues in the other, we will have a synergy that can be exploited.

This situation is depicted in the figure below. The green curve gives the case with no synergy and the blue the case described above. The difference between them is the synergies created by the merger. The synergy at the dotted line is the synergy we can expect, but it might turn out to be higher if revenues is high and even negative (dysergy) when revenues is low.

If we produce a frequency diagram of the sizes of the possible synergies it will look as the diagram below. Have in mind that the average synergy value is not the value we would expect to find, but the average of all possible synergy values.

Case 2

If we assume that the “connection” between the companies is such that a reduction in one of the company’s revenues streams will reduce the total production costs, we again have a synergy that can be exploited.
This situation is depicted in the figure below. The green curve gives the case with no synergy and the red the case described above. The difference between them is again the synergies created by the merger. The synergy at the dotted line is the synergy we can expect, but it might turn out to be higher if revenues is lower and even negative (dysergy) when revenues is high.

In this case, the merger acts as a hedge against revenue losses at the cost of parts of the upside created by the merger. This should not deter the participants from a merger since there is only a 30 % probability that this will happen.

The graph above again gives the frequency diagram for the sizes of the possible synergies. Have in mind that the average synergy value is not the value we would expect to find, but the average of all possible synergy values.

Conclusion

The elusiveness of synergies in many M&A cases can be explained by the natural randomness in business activities. The fact that a merger can give rise to large synergies does not guarantee that it will occur, only that there is a probability that it will occur. Spread sheet exercises in valuation can lead to disaster if the stochastic nature of the involved companies is not taken into account. AND basing the pricing of the M&A candidate on expected synergies is pure foolishness.

References

Bruner, Robert F. (2002), Does M&A Pay? A Survey of Evidence for the Decision-Maker. Journal of Applied Finance, Vol. 12, No. 1. Available at SSRN: http://ssrn.com/abstract=485884

Orwell, George (1949). Nineteen Eighty-Four. A novel. London: Secker & Warburg.

The whole is more than the sum of its parts. Aristotle, Metaphysica

Sirower, M. (1997) The Synergy Trap: How Companies Lose the Acquisition Game. New York. The Free Press.

14/12/2011
Working Capital and the Balance Sheet

This entry is part 2 of 3 in the series Working Capital

The conservation-of-value principle says that it doesn’t matter how you slice the financial pie with financial engineering, share repurchases, or acquisitions; only improving cash flows will create value. (Dobbs, Huyett & Koller, 2010).

The above, taken from “The CEO’s guide to corporate finance” will be our starting point and Occam’s razor the tool to simplify the balance sheet using the concept of working- and operating capital.

To get a better grasp of the firm’s real activities we will as well separate non-operating assets from operating assets – since it will be the last that defines the firm’s operations.

To find the amount of operating current assets we have to deduct the sum of minimum cash level, inventories and account receivables from total current assets. The difference between total- and operating current assets is assumed placed in excess marketable securities – and will not be included in the working capital.

Many firms have cash levels above and well beyond what is really needed as working capital, tying up capital that could have had better uses generating higher return than mere short-term placements.

The net working capital now found by deducting non-interest bearing current liabilities from operating current assets, will be the actual amount of working capital needed to safely run the firms operations – no more and no less.

By summing net property, plant and equipment and other operating fixed assets we find the total amount of fixed assets involved in the firm’s operations. This together with net working capital forms the firms operating assets, assets that will generate the cash flow and return on equity that the owners are expecting.

The non-operating part – excess marketable securities and non-operating investments – should be kept as small as possible, since this at best only will give an average market return. The rest of the above calculations give us the firm’s total liability and equity, which we will use to set up the firm’s ordinary balance sheet:

However, by introducing operating-, non-operating- and working capital we can get a clearer picture of the firm’s activities ((Used in yearly reports by Stora Enso, a large international Pulp & Paper company, noted on NASDAQ OMX in Stockholm and Helsinki.)):

The balance sheet’s bottom line has been reduced by the smallest value of operating current assets and non-interest bearing debt and the difference between them – the working capital – will be an asset or a liability depending on which of them that have the largest value:

The above calculations is an integral part of our balance simulation model and the report that can be produced for planning, strategy- and risk assessment from the simulation can be viewed her; report for the most likely outcome (Pdf, pp 32). However this report can be produced for every run in the simulation giving the opportunity to look at tail events that might arise, distorting expectations.

Simplicity is the ultimate sophistication. — Leonardo da Vinci

References

Dobbs, D, Huyett, H, & Koller, T. (2010). The ceo’s guide to corporate finance. McKinsey Quarterly, 4. Retrieved from http://www.mckinseyquarterly.com/home.aspx

Endnotes

01/12/2010
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
Working Capital Strategy

This entry is part 1 of 3 in the series Working Capital

Passion is inversely proportional to the amount of real information available. See Benford’s law of controversy.

The annual “REL ((REL Consultancy. (2010). Wikipedia. Retrieved October 10, 2010, from http://en.wikipedia.org/wiki/REL_Consultancy)) /CFO Working Capital Survey” made its debut in 1997 in the CFO Magazine. The magazine identifies working capital management as one of the key issues facing financial executives in the 21st century (Filbeck, Krueger, & Preece, 2007).

The 2010 Working Capital scorecard (Katz, 2010) and its accompanying data ((http://www.cfo.com/media/201006/1006WCcompletev2.xls)) gives us an opportunity to look at working capital management ((Data from 1,000 of the largest U.S. public companies)); that is the effect of working capital management on the return on capital employed (ROCE):

or,

From the last formula we can see that – all else kept constant – a reduction in net operating working capital should imply an increased return on capital employed.

Gross and Net Operating Working Capital

A firm’s gross working capital comprises its total current assets. One part of it will consist of financial current assets held for various reasons other than operational, and the other part of receivables from operations and the inventory and cash necessary to run these operations. It is this last part that interests us.

The firm’s operations will have been long term financed by equity from owners and by loans from lenders. Firms usually also have short term financing from banks (short term credit + overdraft facilities/ credit lines) and most always from suppliers by trade credit. The rest of the current liabilities; current tax and dividends will not be considered as parts of operating current liabilities, since they comprises only non recurrent payments.

Net working capital is defined as the difference between current assets and current liabilities (see figure below). It can be both positive and negative depending on the firm’s strategic position in the market.

However usually a positive net working capital is required to ensure that the firm is able to continue its operations and that it has sufficient funds to satisfy both maturing short-term debt and upcoming operational expenses. In the following we assume that any positive net working capital is held as cash and that all excess cash is held as marketable securities.

By removing from both current assets and liabilities all items not directly related to and necessary for the operations, we arrive at net operating working capital as the difference between operating current assets and operating current liabilities:

Net operating working capital = Operating current assets – Operating current liabilities

Since the needed amount of working capital will differ between industries and be dependent on company size it will be easier to base comparisons on the cash conversion cycle.

Working Capital Management

Working capital management is the administration of current assets as well as current liabilities. It is the main part of a firm’s short-term financial planning since it involves the management of cash, inventory and accounts receivable. Therefore, working capital management will reflect the firm’s short-term financial performance.

Current assets often account for more than half of a company’s total assets and hence, represent a major investment for small firms as they can not be avoided in the same way as investments in fixed assets can – by renting or leasing. A large inventory will tie up capital but it prevents the company from lost sales or production stoppages due to stock-out. A high level of current assets hence means less risk to the company but also lower earnings due to higher capital tie-up – the risk-return trade-offs (Weston & Copeland, 1986).

Since the needed amount of working capital will differ between industries and also will be dependent on company size it will be easier to base comparisons of working capital management between companies and industries on their cash conversion cycle (CCC).

The Cash Conversion Cycle

The term “cash conversion cycle” (CCC) refers to the time span between a firm’s disbursing and collecting cash and will thus be ‘unrelated’ to the firm’s size, but be dependent on the firm’s type of business (see figure below).

Companies that have high inventory turnover and do business on a cash basis – usually have a low or negative CCC and hence needs very little working capital.

For companies that make investment products the situation is a completely different. As these types of businesses are selling expensive items on a long-term payment basis, they will tend to have a high CCC and must keep enough working capital on hand to get through any unforeseen difficulties.

The CCC cannot be directly observed in the cash flows, because these are also influenced by investment and financing activities and must be derived from the firm’s balance sheet:

+ Inventory conversion period (DSI)
+ Receivables conversion period (DSO)
– Payable conversion period (DPO)
= Cash Conversion Cycle (days)

Where:

DSI = Days sales of inventory, DSO = Days sales outstanding, DPO = Days payable outstanding, WIP = Work in progress, Period = Accounting period and COGS = Cost of goods sold ((COGS = Opening inventory + Purchase of goods – Closing inventory)) or:

+ Average inventory+WIP / [COGS/days in period]
+ Average Accounts Receivable / [Revenue / days in period]
+ Average Accounts Payable / [(Inventory increase + COGS)/ days in period]
= Cash Conversion Cycle (days)

The Observations

Even if not all of the working capital is determined by the cash conversion cycle, there should be a tendency for higher return on operating capital with lower CCC. However the data from the annual survey (Katz, 2010) does not support this ((Data used with permission from REL/CFO. Twenty of the one thousand observations have been removed as outliers, to give a better picture of the relation)):

The scatter graph shows no direct relation between return on operating capital and the cash conversion cycle. A closer inspection of the data for the surveys different industries confirms this.

Since the total amount of capital invested in the CCC is:

and is thus a function of sales. The company size will then certainly play a role when we only look at the yearly data. The survey however also gives the change from 2008 to 2009 for all the companies so we are able to remove the size effect by looking at the changes (%) in ROCE by a change in CCC:

The graph still shows no obvious relation between change (%) in CCC and change (%) in ROCE. Now, we know that the shorter this cycle, the fewer resources the company needs to lock-up; reduced debtor levels (DSO), decreased inventory levels (DSI) and/or increased creditor levels (DPO) must have an effect on the ROCE – but will it be lost in the clutter of all the other company operations effects on the ROCE?

Cash Management

Net operating working capital is the cash plus cash equivalents needed to pay for the day-to-day operation of the business. This will include; demand deposits, money market accounts, currency holdings and highly liquid short-term investments such as marketable securities ((Marketable securities with a maturity of less than three months are referred to as ‘cash equivalents’ on the balance sheet, those with a longer maturity as ‘short-term investments’)); portfolios of highly liquid, near-cash assets which serves as a backup to the cash account.

There are many reasons why holding cash is important; to act as a buffer when daily cash flows do not match cash out flows (Transaction motive), as a safety stock to face forecast errors and unforeseen expenses (Precautionary motive) or to be able to react immediately when opportunities can be taken (Speculative motive). If the cash level is too low and unexpected outflows occurs, the firm will have to either borrow funds or in the case of an investment – forgo the opportunity.

Such short-term borrowing of funds can be costly as can a lost opportunity by the lost returns of rejected investments. Holding cash however also induces opportunity costs due to loss of interest.

Cash management therefore aim at optimizing cash availability and interest income on any idle funds. Cash budgeting – as a part of the firm’s of short-term planning – constitutes the starting-point for all cash management activities as it represents the forecast of cash in- and outflows and therefore reflects the firm’s expected availability and need for cash.

Working Capital Strategy

We will in the following look closer at working capital management using balance simulation ((In the Monte Carlo simulation we have used 200 runs, as that was sufficient to give a good enough approximation of the distributions)). The data is from a company with large fixed assets in infrastructure. The demand for its services is highly seasonal as schematic depicted in the figure below:

A company like this will need a flexible working capital strategy with a low level of working capital in the off-seasons and high levels in the high seasons. As the company wants to maximize its equity value it is looking for working capital strategies that can do just that.

The company has been working on its cash conversion cycle, and succeeded in that with on average of only 11,1 days 1M (standard deviation 0,2 days) (across seasons) for turning supplied goods and services into cash:

All the same, even then a substantial amount, on average €4,1M (standard deviation €1,8M) of the company’s resources, is invested in the cash conversion cycle:

In addition the company needs a fair amount of cash to meet its other obligations. Its first strategy was to keep cash instead of using short term financing in the high seasons. In the off-seasons this strategy gives a large portfolio of marketable securities – giving a low return and thereby a low contribution to the ROCE. This strategy can be described as being close to the red line in the seasonal graph above.

When we now plot the two hundred observed (simulated) values of working capital and the corresponding ROE (from now we use return on equity (ROE) since this of more interest to the owners), we get a picture as below:

This lax strategy shows little relation between the amount of working capital and the ROE and – from just looking at the graph it would be easy to conclude that working capital management is a waste of time and effort.

Now we turn to a stricter strategy: keeping a low level of cash through all seasons, using short term financing in the high seasons and always have cash closely connected to expected sales. Again plotting the two hundred observed values we get the graph below:

From this graph we can clearly see that if we can reduce the working capital we will increase the ROE – even if we live in a stochastic environment. By removing some of the randomness in the amount of working capital by keeping it close to what is absolutely needed – we get a much clearer picture of the effect. This strategy is best described as being close to the green line in the seasonal graph.

Since we use pseudo-random ((Pseudo random number generator (PRNG), also known as a deterministic random bit generator, is an algorithm for generating a sequence of numbers that approximates the properties of random numbers. The sequence is not truly random in that it is completely determined by a set of initial values, called the seed number)) simulation we have replicated the first simulation (blue line), for the stricter strategy (green line).

This means that the same events happened for both strategies; changes in sale, prices, costs, interest and exchange rates etc. The effects for the amount of working capital are shown in the graph below:

The lax strategy (blue line) will have an average working capital of €4,8M with a standard deviation of €3.0M, while the strict strategy (green line) will have an average working capital of €1,4M with a standard deviation of €3.3M.

Even if the stricter strategy seems to associate lower amounts of working capital with higher return to equity (se figure) and that the amount of working capital always is lower than under the laxer strategy, we have not yet established that it is a better strategy.

To do this we need to simulate the strategies over a number of years and compare the differences in equity value under the two strategies. Doing this we get the probability distribution for difference in equity value as shown below:

The expected value of the strict strategy over the lax strategy is €3,4 M width a standard deviation of €6,1 M. The distribution is skewed to the right, so there is also a possible additional upside. From this we can conclude that the stricter strategy is stochastic dominant to the laxer strategy. However there might be other strategies that can prove to be better.

This brings us to the question: does an optimal working capital strategy exist? What we do know that there will be strategies that are stochastic dominant, but proving one to be optimal might be difficult. Given the uncertainty in any firm’s future operations, you will probably first have to establish a set of strategies that can be applied depending on the set of events that can be experienced by the firm.

References

Filbeck, G, Krueger, T, & Preece, D. (2007). Cfo magazine’s “working capital survey”: do selected firms work for shareholders?. Quarterly Journal of Business and Economics , (March), Retrieved from http://www.allbusiness.com/company-activities-management/financial/5846250-1.html

Katz, D.M.K. (2010). Working it out: The 2010 Working Capital Scorecard. CFO Magazine, June, Retrieved from http://www.cfo.com/article.cfm/14499542

Weston, J. & Copeland, T. (1986). Managerial finance, Eighth Edition, Hinsdale, The Dryden Press

Footnotes

18/10/2010