building a new company HQ–a sign of trouble ahead?

This is a long-standing Wall Street belief.  The basic idea is that as companies expand and mature, their leadership gradually turns from entrepreneurs into bureaucrats.  The ultimate warning bell that rough waters are ahead for corporate profits is the announcement that a firm will spend huge amounts of money on a grandiose new corporate headquarters.

An odd article in the Wall Street Journal reminded me of this a couple of days ago.  The company coming into question in it is Amazon, which has just initiated a search for the site of a second corporate HQ.

What’s odd:

–why no comment on Apple’s new over-the-top $5 billion HQ building?

–the headquarters idea was followed by a discussion of research results from a finance professor from Dartmouth, Kenneth French, which show that publicly traded firms with the highest levels of capital spending tend to have underperforming stocks.

I’ve looked on the internet for Prof. French’s work, much of which has been done in collaboration with Eugene Fama.  I couldn’t find the paper in question, although I did come across an interesting, and humorous, one that argues the lack of predictive value of the capital asset pricing model (CAPM)–despite it’s being the staple of the finance theory taught to MBAs.  (The business school idea is apparently that reality is too complicated for non-PhD students to understand so let’s teach them something that’s simple, even though it’s wrong.)

my thoughts

–money for creating/customizing computer software, which is one of the largest uses of corporate funds in the US, is typically written off as an expense.  From a financial accounting point of view, it doesn’t show up as capital spending.

–same thing with brand creation through advertising and public relations.  I’m not sure how Prof. French deals with this issue.

Over the past quarter-century, there’s been a tendency for companies to decrease their capital intensity.  In the semiconductor industry, this was the child of necessity, since each generation of fabs seems to be hugely more expensive than its predecessor.  Hence the rise of third-party fabs like TSMC.

For hotel companies, it has been a deliberate choice to divest their physical locations, while taking back management contracts.  For light manufacturing, it has been outsourcing to the developing world, but retaining marketing and distribution.


What’s left as capital-intensive, then?  Mining, oil and gas, ship transport, autos, steel, cement, public utilities…  Not exactly the cream of the capital appreciation crop.


At the very beginning of my investment career, the common belief was that high minimum effective plant size and correspondingly large spending requirements formed an anti-competitive “moat” for the industries in question.  But technological change, from the 1970s steel mini-mill that cost a tenth the price of a blast furnace onward, has shown capital spending to be more Maginot Line than effective defense.

So it may well be that the underperformance pointed to by Prof. French has less to do with profligate management, as the WSJ suggests, than simply the nature of today’s capital-intensive businesses–namely, the ones that have no other option.







more on discounting

In actual practice, judging what the market has already discounted in the price of an individual stock or the prices of stocks in general, is a tricky thing.  Even seasoned professionals are often wrong.

There are trends in overall market direction that are relatively easy to spot.  In a bull market, investors tend to ignore bad news and respond strongly to good.  In bear markets, the opposite happens.

Perhaps the main reason for professionals that technical analysis is more than a curious practice of a more primitive time is that watching for deviations from the usual daily price action of individual stocks can give clues to what other investors are thinking/doing.  Rises on unusually high volume, for example, can suggest that others are figuring out what you already know and have acted on.  On the other hand, failure of the stock to react positively to news that supports your positive thesis suggests that what you thought was a new, investable insight actually wasn’t.

The reality that investors only act piecemeal, or the idea that we act differently when infused with greed than when in the vise grip of fear are both much too untidy for the statisticians who formulated the Efficient Market Hypothesis/Capital Asset Pricing Model that arose in the 1970s (and which–mind-bogglingly–is still taught in business schools).

These theories have no place for observation/practical experience.  They assume that everyone has the same information and that the market factors new information into prices instantaneously.  What’s particularly ironic is that they were formed during the early 1970s.  How so?

–1972 was the peak of the “Nifty Fifty” or “One-Decision Stocks” speculation.  Investors believed that a small number of stocks–Kodak, Xerox, National Lead, for example–would grow rapidly forever.  Therefore, they should never be sold, and no price was to high to pay to acquire them.  The result was that this group of names traded at as high as 110x earnings–in an environment where the 10-year Treasury yielded 6% and the average stock traded at 11x.

–this high was immediately followed by a vicious bear market in 1973-74 that saw stocks trade in mid-1974 at discounts to net cash on the balance sheet–and still go down every day, on the theory that money in the hands of management scoundrels wasn’t worth 100 cents on the dollar.

How is it that these guys didn’t notice?

science and math vs. academic finance (pension consultants, too)

“Evaluating Trading Strategies”

The Buttonwood column in the February 21st issue of The Economist talks about a recent article published in the Journal of Portfolio Management, titled “Evaluating Trading Strategies,” authored by Profs. Campbell Harvey of Duke and Yan Liu of Texas A&M.

Long ago, I’d come to think of the difference between academic financial theorists and portfolio managers as somewhat like that between teachers of academic literary theory and actual authors.  That is to say, the two sets of people live in very different worlds, with little in common    …and without that much relevance for each other.

Three exceptions with finance:

–academics are often used as front men for various investment schemes, such as in the case of the ill-fated Long-Term Capital Management, which raised a huge amount of money to implement a strategy of buying illiquid bonds and collapsed shortly thereafter–destabilizing the world financial system in the process

–they often sit as window dressing on the boards of directors of financial companies, and

–their theories inform much of the methodology of the investment consultants on whose advice pension fund managers rely heavily.

its conclusion

The article has an emperor’s new clothes aspect to it.

Simply put, it says that academic finance researchers routinely use a standard for testing for the statistical significance of their findings that is much too weak and alrady discredited in mainstream scienticif research.  Because of this failing, in statistical work in finance large numbers of false.  This is through ignorance, not malice.

As the authors put it:

“So where does this leave us? …Most of the empirical research in finance, whether published in academic journals or put into production as an active trading strategy by an investment manager, is likely false.  …half the financial products (promising outperformance) that companies are selling to clients are false”

Who knows whether this article will have any long-term effects?

In the real world, very few people take academic finance theories seriously–except for pension funds, which rely heavily on consultants who use it to legitimize their advice.  The conclusion that the “advice” is little more than picking numbers out of a hat (arguably even less reliable than that method) has the potential to really shake up this chronically poor-performing sector.

information asymmetry

That’s the fancy name for the situation where either you know more than the other guy or vice versa.  Think:  buying a used car, or competing in a game/sport with someone who has only half your experience and skill.

In investing, cases like the first are ones that everyone not an auto mechanics wants to  avoid.  We should, however,be spending a lot of our research time seeking out the second type.

practice vs. academic theory

One of the odder things about financial theory taught in MBA programs is the professors’ insistence–despite overwhelming evidence to the contrary–that such situations don’t exist in investing.  Officially at least, they maintain that everyone possesses the same information.

There are several odd aspects to this state of affairs:

–professional investors are happy not to rock the boat, since, to the degree that students actually believe this stuff, business schools churn out large amounts of “dumb money” to be taken advantage of,

–if all market participants have precisely the same information, how is it an ethical enterprise to charge thousands of dollars a credit to inform students that they already know everything?

–in some deep sense, professors know they live in a Copernican world despite the fact they teach Ptolemy to get a paycheck.

When I was a student at NYU, the finance faculty had a number of eminent tenured theoreticians, as well as one semi-retired portfolio manager who was an adjunct teaching for fun.  One professor proposed a contest:  faculty members would each provide $10,000 of their own money into either a portfolio managed by the theoreticians or one managed by the adjunct “practitioner.”  Over, say, a year or two that would provide a practical illustration of the superiority of theory over vulgar practice.  Unfortunately, the test never got off the ground.  No one was willing to give real money to the professors; everyone wanted to back the working portfolio manager.

More tomorrow, on making information asymmetry work for you and me.

Capital Asset Pricing Model (CAPM)

The Capital Asset Pricing Model is the keystone of the academic Modern Portfolio Theory developed in the Fifties and Sixties.  Its leading lights, Harry Markowitz, William Sharpe and Merton MIller, received the Nobel Prize in Economics for their role in developing this theory.

Taking a very simple view, the main difference between the CAPM and what I described in my Alpha and Beta post is the explicit introduction of a “risk-free” asset, normally thought of as being treasury bills.

Here’s the Alpha and Beta equation:

stock return = α + β(index return) + ε,

where α is a constant, β is the multiplier that links stock return and market return, and ε is a random error term.  (Although the theory doesn’t require it, the “index” has typically been interpreted as a stock market index, like the S&P 500.)

If we argue that the stock return has two components, the risk-free return (rf)  + the return for taking risk, then the equation can be rewritten as:

stock return = rf + α + β(index return – rf) + ε,

where β (a slightly different β from the first equation, but the same general idea) is a measure of the volatility of a stock vs the market, and α (a different α, sometimes called Jensen’s alpha) is any return that remains, positive or negative. Continue reading