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The lesson to be learned was not that QE or zero interest rates are omnipotent in supporting stock prices. The lesson was not that valuations are irrelevant, or that “this time is different” in ways that investors cannot comprehend. The lesson was not that low interest rates make stocks “cheap” at any price. Rather, the lesson was that in the presence of zero interest rates, yield-seeking speculation can persist even in the face of obscene valuations and recklessly overextended conditions. So while one can become neutral, one has to defer a hard-negative market outlook until the uniformity of market internals explicitly deteriorates (signalling a shift toward increasing risk-aversion among investors).

Based on a century of market evidence, I concluded that the distinction is the psychological preference of investors toward speculation or toward risk aversion. Moreover, I found that the most reliable measure of those preferences was the uniformity or divergence of market action across a broad range of internals, including individual stocks, industry groups, sectors, and asset classes, including debt securities of varying creditworthiness. That distinction proved to be extraordinarily valuable. The combination of extreme valuations and deteriorating market internals is precisely what allowed us to anticipate the 2000-2002 and 2007-2009 market collapses.

A few more assertions about the financial markets may be useful to discuss. One with appeal to many investors is the idea that valuations may be high on an absolute basis, but that stocks are still “cheap relative to interest rates.” This too is wrong, but wrong in an interesting way.

As I’ve detailed previously (see The Most Broadly Overvalued Moment in Market History), investors often misinterpret the form, reliability, and magnitude of the relationship between valuations and interest rates, and become confused about when interest rate information is needed and when it is not. Specifically, given a set of expected future cash flows and the current price of the security, one does not need any information about interest rates at all to estimate the long-term return on that security. The price of the security and the cash flows are sufficient statistics to calculate that expected return. For example, if a security that promises to deliver a $100 cash flow in 10 years is priced at $82 today, we immediately know that the expected 10-year return is (100/82)^(1/10)-1 = 2%. Having estimated that 2% return, we can now compare it with competing returns on bonds, to judge whether we think it’s adequate, but no knowledge of interest rates is required to “adjust” the arithmetic.

One intuitive way to evaluate the impact of interest rates is to consider the effect of a given departure of interest rates from normal levels. For example, consider again a $100 cash flow that will be received 10 years from today. If the typical return on such an investment is 6%, the current price will be $55.84. But suppose we expect returns to be held down to just 4% for the first 5 years, then 6% after that. In that case, the current price will be $100/[(1.04)^5 x (1.06^5)] = $61.42. That’s 10% higher than our previous calculation. Why? Because in order to reduce the return from 6% to 4% for the initial 5 year period, the price has to increase by 2% x 5 years = 10%.

Accordingly, if you believe that market valuations should be tightly related to the level of interest rates (the correlation actually goes the wrong way outside of the 1970-1998 period, but let’s assume otherwise), it follows that if interest rates are expected to be 3% below average for the entire decade ahead, market valuations ought to be 30% higher than historical norms. The problem is that the most reliable valuation measures (those most tightly correlated with actual subsequent market returns in cycles across history) are currently between 130-160% above their respective historical norms.

An additional theory crossed my desk in recent weeks, which is that corporate profits are enjoying a “winner take all” phenomenon, which will allow large, dominant companies to retain monopoly-like profit margins indefinitely. Now, there’s no question that many internet-related companies have benefited from network effects that have substantially contributed to their size, as well as their market capitalizations. The question is whether this effect now dominates the profit margin behavior of U.S. corporations more generally. One anonymous analyst, who we like quite a bit for his (or her) analytical approach even when we wholly disagree, recently proposed that profit margins might be more broadly affected by this sort of systematic “winner take all” dynamic.

To that end, Patrick O’Shaughnessy compiled some data by separating companies into five bins based on their profit margins, and then charted the aggregate profit margins of each bin (chart below). The analyst proposed, “If our explanation is correct, then the aggregate profit margins of the higher bins should have increased more over the last few decades than the aggregated profit margins of the lower bins. Lo and behold, that’s exactly what the data shows.”

My response to this is straightforward. The conclusion is wrong, but it’s wrong in an interesting way. That’s not a criticism of either analyst, just an issue with the conclusion being drawn, and it provides an opportunity to learn something valuable. The problem here is that the analysis is an artifact of selection bias.

To illustrate, I generated 100 geometric random walks, and then sorted them into quintiles based on their ending values. It should be clear that the members of the top bin are, by definition, the ones that have benefited the most from randomness, and the members of the bottom bin are, by definition, the ones that have suffered the most from randomness. Even though the underlying paths are random going forward, grouping them by their ending values and then looking backward gives the impression that there is some systematic “winner-take-all” process at play.

That’s not to say that we can reject the possibility of a “winner-take-all” dynamic, but what’s actually required to demonstrate it is to sort the series at some point T, and then show that subsequent outcomes are systematically biased in favor of the early winners. Again, there’s no question that many internet companies benefit from this kind of dynamic (though their market capitalizations already vastly extrapolate the continued expansion of those network effects). For the market as a whole, however, I remain convinced that the main story behind profit margin expansion in recent years has been weak growth in real unit labor costs, and that this is likely to change in the years ahead, as the combined result of weak demographic growth in the labor force, substantially less slack in the U.S. labor market, and limited benefits from labor outsourcing on unit labor costs, given that lower wages often go hand-in-hand with lower productivity.

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