Brian Gaudet's  Instablog

Although – with the possible exception of a rum and Coke – Diageo (DEO) and The Coca-Cola Company (KO) do not seem to have much in common, I believe both are excellent companies with many of the same sources of competitive advantage. 

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This article is part III of a series on evaluating a company's future prospects.

Having found a company with a track record indicative of a strong competitive advantage, we need to convince ourselves that the competitive advantage is sustainable well into the future.  To do so, we must look deeper into the source of a company’s competitive advantage using the five forces that determine industry profitability: supplier bargaining power, buyer bargaining power, the threat of new entrants, the threat of substitutes, and the intensity of rivalry between existing firms. This framework for analyzing industry profitability comes from Michael Porter’s book “Competitive Advantage”, and this article draws heavily from what I learned from this book, which I recommend reading.  What we are looking for is an industry where the incumbents are likely to enjoy a very long period of stable and highly profitable operations. If our company is one of the leaders in such an industry, we gain confidence that the company’s demonstrated competitive advantage will persist far into the future.

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A common approach to retirement planning is to determine an amount that can be initially withdrawn from a retirement portfolio, and increased each year by the rate of inflation. For example, Monte Carlo simulation might show that someone withdrawing $35,000 a year (in today’s dollars) from a $1,000,000 portfolio with some given asset class allocation would result in a terminal portfolio value after forty years of at least $250,000 in 85% of the simulations. 

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Tactical asset class allocation is an investment strategy where a portfolio’s long-term allocation is adjusted based off of the expected return of each asset class.  One approach to estimating an asset class’s expected return is to apply a valuation model to an index fund representative of that asset class. Here the primary difficulty is obtaining enough years of historical earnings data for each of the index’s constituents to allow creating a time series of the index’s historical earnings that is representative of the index’s collective demonstrated earnings power. The reason why one (or even several) year’s of earnings are not enough is that a single year’s earnings can be impacted by cyclic factors – for more on this topic, see this article:  seekingalpha.com/article/139168-how-to-determine-the-demonstrated-earnings-power-of-a-cyclical-company-like-caterpillar

Once we have an estimate of the index’s collective earnings power, we can use a simple valuation model (see seekingalpha.com/article/140593-a-simple-valuation-model-for-large-cap-stocks) to estimate the index’s intrinsic value, and use this and the current market price to estimate the index’s expected rate of return. If this expected return is higher than the asset class’s long-term expected return, then that asset class’s allocation could be bumped up, and vice versa.  Two approaches to this adjustment algorithm will be discussed later; first let’s see how to estimate an index’s collective earnings power and intrinsic value by looking at three examples.

                Small Cap Domestic Stocks:

To estimate this asset class’s intrinsic value and expected return, I used Standard and Poor’s Small Cap 600 index as a proxy for the small cap domestic stock (SCDS) asset class, as they have this index’s constituent list (in ticker format) conveniently downloadable as a spreadsheet.  To implement this asset class in a portfolio, I would probably use Vanguard’s NAESX index fund.

My first step in analyzing this index is to bring up the Investing Toolbox’s Portfolio Tool, put the tool in Asset Class Modeling Mode, and then import the tickers of the Small Cap 600 index by copying them from the spreadsheet and using Menu->Import->Ticker List function. I then have the tool automatically download (from Morningstar) each constituent’s industry, ten-years of earnings data, and current market capitalization.  This information is used to generate each constituent’s real (inflation-adjusted) expected return, and also generates a covariance matrix (600 X 600 in this case) which is used to estimate the index’s standard deviation of returns.  The index’s real arithmetic return is calculated as a market-cap weighted average of each constituent’s expected return, as estimated using the valuation model discussed in “The Patient Investor”.  A small sample of this portfolio as shown entered into the tool is shown below:

 

 

As you can see, the tool estimates the index’s real expected arithmetic average return to be 8.5%, with a standard deviation of 32.5%. Although the standard deviation of returns is close to the historical long-term average, the expected return is a lower than the long-term historical average of 10.6%.

                  Large Cap Foreign Stocks:

Modeling the expected return of the large cap foreign stock index (LCFS) presents the issue of where to find ten-years of earnings data.  Although most foreign companies do present English language financial reports, these reports are not to be found in a standardized format that lends itself to automatic download.  My solution is to use Standard and Poor’s ADR index, since a market cap weighted average of this index would be a good proxy for a large cap foreign stock index.  And since these companies all have shares listed as American Depository Receipts, Morningstar’s database has ten-years of financial data in standardized format.  To implement this asset class as an index fund, I would probably use Vanguard’s VGTSX index fund.

The next step is to import a ticker list of the ADR index’s constituents into the Portfolio Tool, and then following the same procedure as for the SCDS asset class.  Doing so, we find that the tool estimate’s the index’s real expected arithmetic average return to be 7.9%, with a standard deviation of 19.1%.  This can be compared to my estimate of this asset class’s long-term historical return of 6.7% with a standard deviation of 18%.

              Large Cap Domestic Stocks:

For our final example, let’s look at large cap domestic stocks (LCDS). Here an obvious proxy is the SP500 index, which has actual operating earnings data downloadable from Standard and Poor’s, which means we can plug an earnings time series directly into our valuation model.  This gives an estimated intrinsic value of $915, which can be compared to the current index value of $1027 implies an expected real geometric return of 4.9% (see this article  seekingalpha.com/article/142838-s-p-500-... for an example of this calculation). Here the expected return is approximated as the ratio of estimated intrinsic value to market value, multiplied by the 5.5% real discount rate (the long-term expected geometric return used for this asset class).  See my book “The Patient Investor” for the rationale for this calculation, which takes into account mean reversion.  Converting this geometric return to an arithmetic mean using this asset class’s long-term standard deviation of 17% gives an expected average return of 6.85%. 

To compare this to the approach used for the LCFS and SCDS asset classes, I imported the SP500 constituents into the portfolio tool, and analyzed the portfolio, which showed an expected average return equal to 6.6% and a 17.5% standard deviation, pretty close to the numbers found by directly analyzing operating earnings, which in my opinion give a more accurate picture of sustainable earnings.

Adjusting a Portfolio’s Allocation using Expected Returns:

The amount by which you adjust each asset class depends on the tactical asset class allocation strategy.  One approach is to adjust each asset class proportionally to the ratio of current (valuation based) expected return and long-term expected return.  Let’s use a simple example of a portfolio with a long-term allocation of 32% LCDS, 23% LCFS, 16% SCDS, and 29% STDB (short-term domestic bonds). 

Using the data collected from the portfolio tool, the second row of Table 1 shows the portfolio’s long-term allocation.  The third row shows the expected long-term real rate of return for each asset class, the fourth row the expected returns generated using the portfolio tool, and the fifth row the ratio of valuation model based expected returns and long-term historical expected returns.  In row 6, we adjust the allocation based off of this ratio, which sometimes results in the sum of the portfolio’s weights exceeding 1.0, so in the last row we scale the allocation using the reciprocal of the sum of weights found in the last column of row 5. Since the short-duration bond market is pretty efficient, in this example I set the STDB expected return equal to the long-term historical average.

Table 1:

Asset Class	LCDS	LCFS	SCDS	STDB	Sum Weights
LT Allocation	32%	23%	16%	29%	1.0
LT average ER	6.7%	6.7%	10.6%	2.2%
Valuation-Based average ER	6.8%	7.9%	8.5%	2.2%
Ratio	1.01	1.18	0.80	1.0
Adj. Allocation	32.3%	27.1%	12.8%	29%	1.012
Tactical Allocation	31.8%	26.7%	12.5%	29%	1.0

  

An alternative approach, assuming that you generated your original allocation using a mean variance optimizer (MVO), is to use the valuation model based expected returns and rate of return volatility as an input to the MVO and run a new optimization, after which you would run a sample of portfolios along the efficient frontier through the Monte Carlo simulator configured in optimization mode, and choose the allocation of the portfolio with maximal utility within the context of your financial goal as the tactical asset class allocation. Running these new returns through the MVO (using super-class mode) resulted in the SCDS asset class completely disappearing from the new portfolio.  This is because this MVO optimizes based off of geometric return, which fell to 3.8% (subtract the variance divided by two divided by (1 plus the arithmetic rate of return), which give it a very poor return / risk profile.

The efficient frontiers generated using the long-term asset class returns and the valuation-based expected returns are shown below, where the original portfolio (with long-term historical asset class statistics input to the MVO) is shown in red, the new allocation (with valuation-based asset class statistics input to the MVO) in blue, and the slider set to display the original portfolio’s asset class weights and expected return statistics. The reason the new efficient frontier does not extend as far to the right is that the SCDS asset class is absent from any of the efficient frontier portfolios, and the reason the new efficient frontier extends farther to the northwest is due to the higher expected return on LCFS, which allowed a more “efficient” frontier to be generated.

 

 

Which approach you should use depends on how far you are willing to diverge from your long-term asset class allocation, obviously the first method diverges quite a bit less.

Regardless of the tactical asset allocation approach used, the current valuations of the index's analyzed indicates that it would make sense to underweight small cap domestic stocks (which using ETFs could be implemented with iShare's IJR), and overweight large cap foreign stocks (which could be implemented with iShare's ACWX).

If you would like to try out the software I used in writing this article, you can download it from my website for a free trial:

 web.me.com/briangaudet/InvestingToolBox/Investing_Tool_Box.html

Disclosure:  I have no position in any of the index funds mentioned in this article.

Tactical asset class allocation is an investment strategy where a portfolio’s long-term allocation is adjusted based off of the expected return of each asset class.  One approach to estimating an asset class’s expected return is to apply a valuation model to an index fund representative of that asset class. Here the primary difficulty is obtaining enough years of historical earnings data for each of the index’s constituents to allow creating a time series of the index’s historical earnings that is representative of the index’s collective demonstrated earnings power. The reason why one (or even several) year’s of earnings are not enough is that a single year’s earnings can be impacted by cyclic factors – for more on this topic, see this article:  seekingalpha.com/article/139168-how-to-determine-the-demonstrated-earnings-power-of-a-cyclical-company-like-caterpillar

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Estimating a company’s level of recurring capital expenditures is an important component of company analysis, being used to calculate both free cash flow and “owner earnings”, a concept discussed by Warren Buffet in some of his past annual reports.  Free cash flow is calculated as cash flow from operations adjusted for non-recurring items less recurring capital expenditures, whereas owner earnings is calculated as earnings adjusted for certain non-cash items, plus depreciation, less recurring capital expenditures (see Berkshire's 1986 AR).  Both free cash flow and owner earnings provide a useful check on a company’s reported earnings – if they are significantly different from reported earnings, then the reason for the difference should be investigated. 

 

Although a company’s total (as opposed to recurring) capital expenditures can easily be found in their 10-K filings, these can in different cases give either an optimistic or pessimistic picture of the level of capital expenditures required to maintain the company’s production volume of a competitive product.   Some companies do make an estimate of recurring capital expenditures, but this is the exception rather than the rule.

 

If a company does not break down capital expenditures into recurring and non-recurring components, the average ratio of recurring to total capital expenditures can be estimated by analyzing the company’s organic volume growth over a ten-year period. We use organic volume growth; otherwise acquisitions (particularly if late in the ten-year period) can distort the link between total capital expenditures over the period, production and manufacturing capacity. For Diageo (DEO), a company that sells wine, beer, and spirits, organic volume is given in the annual report as cases shipped adjusted for the effects of acquisitions.

 

We estimate the ratio of recurring to total capital expenditures over a ten-year period because the delay between a capital expenditure made with the purpose of expanding production and the resulting increase in volume can vary. Consequently, the relationship between capital expenditures and production measured over a single year may not give much insight. In our analysis, we assume that any percentage change in organic volume requires a corresponding percentage change in manufacturing capacity. Moreover, this percentage change in manufacturing capacity must be matched with a similar percentage change to property, plant, and equipment (PP&E).

 

It follows that we can impute a value for PP&E at the beginning of our measurement period by dividing the company’s current net PP&E by one plus the percentage increase in organic volume growth over the period. The difference between the company’s current PP&E and the imputed start value is an estimate of the cumulative capital expenditures used to expand the company’s productive capacity over the period. Note that the actual PP&E at the start of the measurement period will often differ from the imputed value of PP&E at the start of the measurement period. One reason for this is that PP&E sometimes increases because the minimum capital expenditures required for maintaining production volume has consistently exceeded depreciation; this would make the imputed PP&E at the start of the period greater than the actual net PP&E at the start of the period.  Another is that productivity improvements can sometimes increase volume without additions or improvements to PP&E; this would make the imputed PP&E at the start of the period less than the actual net PP&E at that time.

 

Our next step is to allocate a portion of the total capital expenditures equal to the difference between current net PP&E and the imputed starting value as accruing towards expansion, with the remainder being recurring. The procedure for estimating the amount of capital expenditures used for expansion over the time period is shown in the following equation, where PP&E is current net PP&E, V_Current is the company’s current production volume, and V_Start is the company’s production volume at the start of the time period adjusted for acquisitions. 

 

 

Looking at this equation, we see that capital expenditures attributed to expansion increases with increasing volume over the period, and if volume is flat, the equation results in none of the capital expenditures being attributed to expansion. We can then estimate the company’s ratio of recurring capital expenditures to total capital expenditures over the period as:

 

 

I compared this estimate to two companies that disclosed their estimate of recurring capital expenditures, General Electric (industrial operations only) and Wal-Mart.  In the case of General Electric in 2008, the estimated ratio of recurring to total capital expenditures was 83% as compared to a ratio of 70% disclosed in there 2008 10-K.  For Wal-Mart, using data through FY 2009 resulted in an estimated ratio of 55% as compared to a disclosed ratio of 66%.  Part of the error for Wal-Mart is due to the abrupt slowdown in expansion; this made the historical relationship between recurring capital expenditures and total capital expenditures less indicative of the current situation.  Still, it is pretty close. Going back to FY 2008, our equation gives a ratio of recurring to total capital expenditures of 50% as compared to Wal-Mart’s stated ratio of 52%, and going back to FY 2007, our equation gives 46% as compared to Wal-Mart’s stated figure of 49%. 

 

Table 3‑1 illustrates our estimating of the ratio of recurring capital expenditures to total capital expenditures for Consolidated Edison (ED), Southern Company (SO, General Motors (GM), Toyota Motor Company (TM), The Coca-Cola Company (KO), and 3M (MMM).  For the two utilities, we estimated productive capacity by adjusting revenue by the urban electricity and gas index, a component of the consumer price index (CPI).  For example, using 10 years of data, if 2008 revenue was $17,127M and the ratio of the urban electricity and gas index in 2008 to the index in 1999 was 1.68, we assumed that productive capacity in 2008 was $10,194M, i.e., the rest of the change in revenue was due to price increase rather than and increase in productive capacity. Although both utilities had a different mix of electricity and gas revenues, the price of electricity and gas has similar increases over the period.

 

For Wal-Mart, we assumed that revenue growth is a good proxy for the growth in productive capacity, and consequently price increases were an insignificant contribution to revenue growth, which seems to be the case as the growth in the number of stores has only lagged revenue growth by 2%.  This is small enough difference that it could be attributed to an increase in Super Centers as a percentage of total stores, which would increase the revenue per store.  For General Motors, we could not find consistent manufacturing volume, so we used unit sales as a proxy. Note that the Southern Company has a ratio of recurring to total capital expenditures of greater than one; this can be interpreted as meaning that the capital expenditures have not been sufficient to maintain productive capacity.

 

 

The average ratio of recurring to total capital expenditures over the ten-year period can be used to estimate recurring capital expenditures in a given year, which in turn can be used to calculate free cash flow in that year. Free cash flow (FCF) in a given year i in a period of N years is calculated as the adjusted cash flow from operations in year i (AdjCashOps) less reported capital expenditures in year i (TotalCapex) multiplied by the ratio of the sum of recurring capital expenditures over the period to the sum of total capital expenditures over the period:

 

 

If you want a quick way to apply this technique of estimating a company’s recurring capital expenditures to a company you are analyzing, try out the software on my website:

 

web.me.com/briangaudet/ThePatientInvestor/Home.html

 

 

Disclosure:  I own shares in  DEO, KO & MMM.  I do not own shares of ED, SO, GM (which is now bankrupt), TM, or WMT.