Competitive Stock Valuation

November 04, 2007

Google vs. Microsoft: Blue/Red Ocean Stock Pricing -- A Reply to SA Comments

My blog is syndicated by Seeking Alpha. Sometimes the comments posted there cut right to the heart of the matter. For example, in response to "Google vs. Microsoft: Blue/Red Ocean Earnings Productivity" a reader posted the following comment:

I get the idea that marginal costs should equal marginal revenues to maximize earnings. But it's not clear to me why you think the models can be safely extrapolated out ... Particularly in the Microsoft case -- where the model shows actual REP at -36%.

The purpose of this post is to address the issue of extrapolating relative earnings productivity and risk-adjusted differentials for Google (NASDAQ: GOOG) and Microsoft (NASDAQ: MSFT) in the competitive stock valuation model. The details on how this model works are published in Chapter 9 of my book Competing for Customers and Capital.

Relative Earnings Productivity [REP] is based on a simple principle. As management comes closer to optimal spending the difference between its actual and maximum potential earnings will decrease. When this happens, other things equal, investors will bid up the company's stock price in recognition of that extraordinary achievement. Consider what Al Rappaport says in the second edition of his classic 1986 book Creating Shareholder Value:

It is … productivity that the stock market reacts to when pricing a company’s shares.  Embedded in all shares is an implied long-term forecast about a company’s productivity – that is, its ability to create value in excess of the cost of producing it.  When the stock market prices a company’s shares according to a belief that the company will be able to create value over the long term, it is attributing [this belief] to the company’s long-term productivity or, equivalently, a sustainable competitive advantage.  In this way, productivity is the hinge on which both competitive advantage and shareholder value hang (Rappaport 1998, 69).

It was Professor Rappaport's insight that led me to formulate relative earnings productivity as one of the metrics needed to combine the concept of long-term efficiency with sustainable competitive advantage. But there is a link between efficiency and competitive advantage that I did not discuss in the post that prompted the comment above.

Market share attraction [MSA] theory says that, on average, in a competitive market a company will attract revenues in direct proportion to the share of expenses incurred in serving that market. The popularity of this model is based on several characteristics: it's logically consistent; it derives from four simple axioms; its parameters can easily be estimated; and it seems to outperform both linear and multiplicative models.

An interesting investigation into the properties of the [MSA] model is available in the 2001 paper "Why is Five a Crowd in the Market Share Attraction Model: The Dynamic Stability of Competition" by Paul Farris and his coauthors. Their analysis assumes that firms maximize earnings by optimizing their own spending based on competitors' last-period budgets and the MSA model.

Since maximum earnings derived from the MSA model are nonlinear, so too is relative earnings productivity. This chart, reproduced from "Google vs. Microsoft: Blue Ocean vs. Red Ocean Earnings Productivity" shows relative earnings productivity for Google and Microsoft from March 2005 through June 2007.

They say a picture is worth a thousand words. Well, for investors and senior management of the companies, this chart is worth a million numbers. The quarterly time series reads across the top axis. The vertical axis reads off relative earnings productivity.


Relative earnings productivity equals the ratio of actual to maximum earnings added to -1 (see the equation at the bottom left of this chart).  Why scale REP in this odd way? In order to depict a range from zero, where actual and maximum earnings are equal, to a very large negative number where actual earnings are far less than maximum earnings. The last observation in Google's REP schedule sends a clear message to investors. In the ten quarters following its IPO, Google management systematically guided the company to the point of nearly optimizing operating expenses and costs by the close of the 2nd quarter of 2007.

Oddly enough the calculation of relative earnings productivity using market share attraction theory assumes a strong linear relationship between sales revenues and company expenses.  This assumption sometimes is violated in financial accounting data, but it is quite easily tested with a scatter diagram. The following chart shows the relationship between Google's share of operating expenses and costs [SOC] on the horizontal [x] axis and its share of revenues [SOR] on the vertical [y] axis.


Beginning with the quarter ending June 30, 2005 in the lower left-hand corner of the chart, Google incurred total operating expenses and costs of $909 million or 11.2% of both company's $8.081 billion total expenses and costs. Google generated $1.384 billion or 12.0% of $11.545 billion combined revenues. Now, 0.8% may not seem like much until you realize it represents an opportunity gain of over $65 million in expenses and costs of serving the market.

In the quarter ending September 30, 2007 in the upper right-hand corner the chart above, Google management captured 23.5% of the $17,993 billion in combined revenues, while incurring 27.1% of the $10.758 billion in combined expenses and costs. The difference between Google's SOR and SOC was -3.567 share points. The company incurred an opportunity loss of $383.8 million. There is a high (0.9) correlation between Google's share of revenues and share of costs. Only the two green markers in this chart are above the (implicit) 90 degree line of equal proportionality. The fitted trend line is unfavorable. As Google's share of revenues doubled from 12% to 24%, it became less efficient. Note the slope of the trend line is 0.8 (with an intercept of 3.0 share points). If revenues and costs were proportional the slope would be one.

With only two companies in the analysis of market share attraction the result for Microsoft is a mirror image of that for Google. Beginning with the quarter ending June 30, 2005 in the upper right-hand corner of the following figure, Microsoft incurred $7.172 billion or 88.8% of the $8.081 billion combined operating expenses and costs of the two companies. At the same time Microsoft generated sales revenues of $10.161 billion, or 88.0% of combined revenues. As before, 0.8% may not seem line much until you realize it represents an opportunity loss of over $65 million.

Turn the clock forward ten quarters and Microsoft's share of revenues fell to 76.5% while incurring 72.9% of combined expenses. The difference between Microsoft's SOR and SOC in September 2007 was +3.567 share points which produced an opportunity gain of $383.8 million. Only the two red markers are below the (implicit) line of equal proportionality. The trend is favorable. As MSFT's share of revenues dropped it became more efficient. As before, the slope of the fitted trend is 0.8. If revenues and costs were proportional the slope would be one.

Over the ten quarters Microsoft became significantly more efficient: its marginal cost per (revenue share) basis point fell from $6.8 to $4.4 million. But management failed systematically to improve on its relative earnings productivity. Google on the other hand became significantly less efficient: its marginal cost per basis point increased from $0.86 to $1.62 million. At the same time Google's management improved is earnings productivity – the difference between its actual and maximum potential earnings systematically increased from a -36% to -3%.

Last year in his online discussion of my book Chris Kenton said "You can be very efficient at doing the wrong things!" The corollary is "You can be inefficient at doing the right things." Perhaps "efficiency vs. productivity" and doing the "right and wrong things" revolve around sailing in a blue ocean vs. a red ocean, at least in this case.

Relative earnings productivity [REP] and risk-adjusted differentials [RAD] are two inputs to the competitive stock valuation model. My post on "Google vs. Microsoft: Blue/Red Ocean Stock Pricing" published by Seeking Alpha promoted another visitor to make this comment:

I'm not buying your predictions. There are too many other factors in play here. I guess we'll see in six months but I would be surprised to see MSFT under 40 in April and GOOG over 750. Maybe your models predict it, but I just don't see it. How often does this type of model accurately predict future value?

I too would be surprised! There are indeed "other factors" in play here and they are not hidden in a black box. They are right up front:

First, the competitive stock valuation model assumes that management will maximize earnings by optimizing operating costs. Google management has satisfied this requirement. Microsoft management has not. But Google might stumble and Microsoft might get it right.

Second, the model assumes that investors believe the company will create value in excess of costs over the long run. As Professor Rappaport put it in the quote above, the company will "... maximize long run productivity" or equivalently achieve "a sustainable competitive advantage." Google investors believe management will continue to maximize long run productivity. Microsoft investors do not. But these beliefs are fragile.

Can the results of competitive stock pricing be "safely extrapolated?" In a word, yes: but only in those cases where a company's relative earnings productivity and risk-adjusted differentials are opposite ...  in the extreme. On the one hand, when REPs are nearly zero at the same time that RADs are less than -2.0 over many periods, you might take a long position in the stock. On the other hand, when you find a company's REPs are less than -100 and RADs are greater than +2.0 over many periods, you might short the stock. Both instances represent serious mispricing. In my experience these extremes are rare ... fewer than 2 in every hundred companies.

There are two take-aways from this reply to comments. If you have the time to find the outliers, combined with the money and risk propensity to invest in them, the competitive stock valuation model might make money for you. If you are Eric Schmidt or Steve Ballmer you might find the model identifies strategic challenges you have overlooked until now. And overcoming those challenges make make money for your shareholders.

Thanks for visiting.


October 28, 2007

Google vs. Microsoft: Blue Ocean vs. Red Ocean Stock Pricing

On Friday October 26, 2007 Google's stock price (NASDAQ: GOOG) closed at $674.60, up nearly seven-fold from $100.34 at the close of trading on the first day of its IPO on August 19, 2004.  By comparison Microsoft's price (NASDAQ: MSFT) closed at $35.04, up fractionally from $27.12 during the same time span.  There were no stock splits during this period to distort the comparison. Is this just another example of irrational exuberance? Or are there fundamental differences between this blue ocean superstar and its red ocean competitor?

But wait you say. Google and Microsoft are not really competitors. Google runs a search engine and Microsoft sells packaged software. Make no mistake: the overlap in their current offerings may be slender in the competition for customers, but millions of decisions are made every day by investors about whether to buy/sell their common stocks. And it is in capital markets where the low hanging fruits of customer satisfaction are to be found.

This is the 5th and final post in my series on the competition between a blue-ocean superstar (Google) and its red-ocean rival (Microsoft). This one, like the earlier posts in the series, was inspired by Blue Ocean Strategy, the book by Professors Kim and Mauborgne of the INSEAD business school in Fontainebleau, France.

In this post I forecast each company's stock price following their March quarterly reports to be filed with the SEC in late April 2008. Unlike all other stock pricing models, my competitive stock valuation prices a stock from the top down, rather than the bottom up. Introduced in Chapter 9 of my book Competing for Customers and Capital, competitive valuation assumes all ships rise and fall with the tide. So the sales revenue and market value of competitors are first combined and then parsed out according to their relative earnings productivity [REP] and risk-adjusted (value sales) differentials [RAD].

The 1st post in this series was "Microsoft's $154 Billion Question: Optimizing Red Ocean Expenses." In it I mapped enterprise marketing expenses onto the sources of intangible market value and introduced a simple measure of how shareholders know if they're are getting their money's worth on "red ocean" spending. In the 2nd post on "Microsoft vs. Google: The Battle for Your Network" I argued that however appealing blue oceans may be, nearly every company ends up in a sea of red ocean expenses. At that point the most compelling question is how to manage expenses in this environment. Theoretically, the best way to do this is to "optimize" these costs. The 3rd post in the series was "Google vs. Microsoft: Blue vs. Red Ocean Earnings Productivity." That one addressed a larger question: are there significant differences between the earnings productivity of "Blue Ocean" compared with "Red Ocean" companies? The short answer is, yes at least in the case I am currently reviewing. In the 4th post on "Google vs. Microsoft: Crossing the Blue-Ocean, Red-Ocean Divide" I pulled back the curtain to reveal the often subtle and complex relationships between the sales revenue and market value of competitors.

Of the many points on an investor's compass only one directs a company on a blue ocean tack: it points to the northeast. This is the only direction in which both relative earnings productivity and risk-adjusted differentials increase simultaneously. Since its IPO Google has become a textbook case in northeast blue ocean tacking. This chart documents that classic pattern.


The blue markers in this chart reveal a pattern that is exactly like a sailing ship tacking toward a northeast destination against a strong, variable wind. The red and green dotted lines create an envelope defined by the limits of Google's westward and eastward tacks respectively. By projecting these coordinates into future quarters we define the company's worst (+3.0) and best (+5.5) case risk-adjusted differentials. The dotted blue line defines the expected case (+4.3) as the midpoint between the worst and best cases.

If Google management continues to maximize earnings in its competition with Microsoft it should capture 26.9% of revenues by the end of March 2008. In this event investor's would likely bid up its share of market value to 45%. Currently with a risk-adjusted differential of 4.3 and an enterprise risk of 4.3 Google's forecast market value is $254.7 billion. The same values of RAD and enterprise marketing risk in the expected (blue) case are entirely coincidental.

If the number of shares outstanding is unchanged Google's share price under the expected scenario should hit $821 in late April 2008. The worst (red) case and best (green) case prices, as defined by the risk-adjusted differentials in the chart above, are $723 and $919 respectively.

In two earlier posts I explained in detail how the competitive stock pricing model works. See "Exchange Wars: A Mexican Standoff? " and "Is Competition a New Risk Factor in Morgan Stanley's World?"

The way Microsoft management navigated its red ocean is more like an aimless drift than a purposeful tack. The next chart shows the pattern of coordinates in Microsoft's relative earnings productivity and risk-adjusted differentials over the same ten quarters.

This chart shows there is a random drift toward the northwest. Revealing that investors don't like what they see. Even though there has been a marked overall improvement in relative earnings productivity, there is no positive direction along the coordinates in this chart.

Faced with this pattern, the best way to forecast MSFT's risk-adjusted differentials is to set 95% confidence limits. Fortunately, this is easy to do. RAD is a standard normal variable (mean zero and standard deviation one). So taking the last observation as the expected value (-3.5), the lower and upper confidence limits become -5.5 and -1.5 respectively.

A fundamental assumption of my competitive stock pricing model is that the management of a company will maximize earnings over the intervening periods. Microsoft's quarterly sales revenues jumped by over 27% in September 2007 compared with 2006. Yet, over the same period its share of combined revenues fell 3.6 share points from 80.1% to 76.5%. Even so, the company is far from the ideal of maximizing earnings: the following table pegs maximum earnings market share at 63.1%.

There is no way management would entertain the significant loss in revenues associated with a 13.4 point drop in market share ... just to maximize earnings. That's why the forecast share price here is contained within the five year range of low and high market prices (about $24 to $34). And at a steady loss 3.6 share points per year it will take around three years for Microsoft to be driven to maximize earnings as a result of Google's revenue growth. Looks like shareholders will be reading more of the same old red ocean story well into the future.

No one knows. But it's a clear possibility that either Mr. Gates and Mr. Ballmer must come up with a blue ocean strategy or continue to watch their market share fall as Google adds new sources of revenue they cannot acquire. What do you think?

Thanks for viewing.