HUSSMAN FUNDS

For anyone who works to infer information from a broad range of evidence, one of the important aspects of the job is to think carefully about the structure of the data – what is sometimes called the “data-generating process.” Data doesn’t just drop from the sky or out of a computer. It is generated by some process, and for any sort of data, it is critical to understand how that process works.

For example, one of the moments of market excitement last week was the reported jump in new housing starts for September. But later in the week, investors learned that there was a slump in existing home sales as well. If we just take those two data points at face value, it’s not clear exactly what we should conclude about housing. But the story is clearer once we consider the process that generates that data.

One part of the process is purely statistical. The housing data that is reported each month actually uses monthly data at an annual rate, so the jump from 758,000 to 852,000 housing starts at an annual rate actually works out to a statement that “During September, in an economy of about 130 million homes, about 100 million which are single detached units, a total of 9,500 more homes were started than in August – a fluctuation that is actually in the range of month-to-month statistical noise, but does bring recent activity to a recovery high.” Now, in prior recessions, the absolute low was about 900,000 starts on an annual basis, rising toward 2 million annual starts over the course of the recovery. The historical peak occurred in 1972 near 2.5 million starts, but the period leading up to 2006 was the longest sustained increase without a major drop. In the recent instance, housing starts bottomed at 478,000 in early 2009, so we’ve clearly seen a recovery in starts. But the present level is still so low that it has previously been observed only briefly at the troughs of prior recessions. 

The second part of the process is important to the question of what is sustainable. Here the question to ask is how and why does a decision to “start” a house occur? According to CoreLogic, about 22% of mortgages are underwater, with mortgage debt that exceeds the market value of the home. Likewise, banks have taken millions of homes into their own “real-estate owned” or REO portfolios, and have dribbled that inventory into the market at a very gradual rate. All of that means that the availability of existing homes for sale is far smaller than the actual inventory of homes that would be available if underwater homeowners were able, or banks were willing, to sell. Accordingly, much of the volume in “existing home sales” represents foreclosure sales, REO and short-sales (sales allowed by banks for less than the value of the outstanding mortgage). That constrained supply of homes available for sale is one reason why home prices have held up. At the same time, constrained supply means that new home buyers face higher prices and fewer choices for existing homes than they would if the market was actually clearing properly. Given those facts, buyers who are able to secure financing (or pay cash) often find it more desirable to build to their preference instead of buying an existing home. It’s not clear how many of these starts represent “spec” building by developers, but it’s interesting to note that the average time to sell a newly completed home has been rising, not falling, over the past year.

In the end, the data-generating process features millions of underwater homes, huge REO inventories, and yet constrained supply. The result is more stable home prices, but a misallocation of capital into new homes despite a glut of existing homes that cannot or will not be brought to market. So starts are up even though existing home sales are down. Inefficient or not, there’s no indication that inventory will be abruptly spilled into the market, so if the slow dribble continues, we’ll probably continue to see gradual growth in housing starts that competes with a gradual release of inventory. This isn’t an indication of economic resilience or housing “liftoff,” but is instead an indication of market distortion and misallocation of scarce capital. Housing starts increased off of a low base during the 1969-70 and 1981-82 recessions as well. A similar increase is unlikely to materially affect the course of what we continue to believe is a new recession today.

In the financial markets, the data-generating process is often very misunderstood. Investors often seem to believe that prices go higher because money comes “into” the market like air filling a balloon. But the actual process is that every dollar that comes into the market in the hands of a buyer is a dollar that leaves a moment later in the hands of a seller. So the data-generating process is dominated not by money-flow but by subjective eagerness to own stocks. Regardless of whether stocks are highly desired or utterly loathed, every share of stock, once issued, has to be held by some investor until that share is somehow retired. Stocks can never be over-owned or under-owned.

On that subject, a number of Wall Street analysts have argued that the increasing amount of investor assets in bond funds, relative to stock funds, is an indication that stocks are under-owned and that investors are overly bearish about equities. If one believes that the data is generated by money just “flowing” into stocks versus bonds, that’s a tempting conclusion. But again, the actual data-generating process is that every share of stock has to be held by someone, as does every bond certificate. The large amount of money being held in the form of bonds says simply that a huge amount of debt has been issued by both corporations and governments, and somebody has to hold it. With yields at record lows, those bonds are being held at very high valuations with little prospective return for the risk involved. The enormous volume of debt being held at high valuations should be a red flag for bond market investors, but is it a green flag for stocks?

According to Ned Davis Research, stock market capitalization as a share of GDP is presently about 105%, versus a historical average of about 60% (and only about 50% if you exclude the bubble period since the mid-1990’s). Market cap as a fraction of GDP was about 80% before the 73-74 market plunge, and about 86% before the 1929 crash. 105% is not depressed. Presently, market cap is elevated because stocks seem reasonable as a multiple of recent earnings, but earnings themselves are at the highest share of GDP in history. Valuations are wicked once you normalize for profit margins. Given that stocks are very, very long-lived assets, it is the long-term stream of cash flows that matters most – not just next year’s earnings. Stock valuations are not depressed as a share of the economy. Rather, they are elevated because they assume that the highest profit margins in history will be sustained indefinitely (despite those profit margins being dependent on massive budget deficits – see Too Little to Lock In for the accounting relationships on this). In my view, there are red flags all around.

Ultimately, what benefits stocks is a movement from being utterly loathed to being highly desired. Once that has occurred – once stocks are overvalued, overbought, and investors are overbullish, one has to rely on the idea that even more eager investors will enter the fray, and take those shares off the hands of already speculative holders. In general, the data-generating process produces the extreme of an advance exactly at the same time that it produces the highest confidence about the continuation of the advance. It produces the extreme of a decline exactly at the same time that it produces the greatest fear about the continuation of the decline. As a result, the point that investors are most inclined to think about the market in terms of the “trend” is exactly when they should be thinking about the market in terms of the “cycle.”

Keep in mind that the bear-market portion of the market cycle typically wipes out more than half of the gains achieved during the bull-market portion. During “secular” bear market periods, the cyclical bear markets wipe out closer to 80% of the prior bull market advances. Risk-management is very forgiving of missed gains in late-stage bull markets. The lack of risk-management is equally punishing to investors who overstay.

The most recent cycle has been unique, from my perspective, because the point where one normally might accept the opportunity for an aggressive stance in post-war data (reasonable valuations coupled with a firming in various trends and technical data) was also the point where the relevance of post-war data came into question. Depression-era data became very relevant, and posed the risk of deep drawdown losses and costly whipsaws under the very same conditions. My own response was to ensure that our methods could navigate extreme market fluctuations regardless of which environment we were facing. That “two data sets” uncertainty is behind us – but the most constructive portion of the past market cycle is also behind us.

As a side note, I should emphasize that trend-following measures are an important and valuable component of the analysis of market action, but it's important to consider a broad range of additional factors as well (breadth, leadership, overbought/oversold conditions, yield spreads, divergences, etc). It’s incorrect to believe that simple moving-average crossover methods have been wildly effective over the long-term; particularly since April 2010, which is the last time that our present methods would have indicated a favorable return/risk profile for the S&P 500. Notably, even the popular trend-following strategy of buying the S&P 500 when it is above its 200-day moving-average has had a net loss since April 2010, including dividends, and even ignoring transaction costs. Trading the 50-day moving-average broke even. The luckiest cross-over strategy turned out to be the 17-week moving-average, which would have gained about 14% since April 2010, ignoring transaction costs, but that same strategy would have historically lagged a buy-and-hold approach even before slippage, so there would have been no basis to prefer it in 2010.

At present, we have little reason to believe that the data-generating process we are observing here is anything “out-of-sample” from the standpoint of a century of historical evidence. We presently have an overvalued, overbought (intermediate-term), overbullish market featuring a variety of syndromes that have typically appeared in the “exhaustion” part of the market cycle: elevated valuation multiples on normalized earnings, emerging divergences in market internals, an increasingly tepid economic backdrop, market prices near the upper Bollinger bands at monthly and weekly resolutions, and other factors that – taken in aggregate – have historically been associated with very weak average market outcomes.

Yes, the Federal Reserve has continued its program of quantitative easing, but here, I am convinced that we understand the data-generating process by which QE has impacted stock prices – namely, by creating an ocean of zero-interest money that acts as a “hot potato,” encouraging investors to seek riskier securities until all assets are so overvalued that their prospective returns compete with zero-interest money. At that point, the zero-interest money (which has to be held by someone) is just passively held, and acts as no further stimulant to prices. QE has had its effects at points when prices have declined deeply over the prior 6-month period, and I suspect that any major future effort will work only until investors realize that this manipulation of their risk preferences is all that quantitative easing is capable of achieving. On this point, I fully agree with PIMCO’s Bill Gross, who tweeted last week “The crash on Oct 19 1987 showed that portfolio insurance puts were dangerous. R central bank ‘puts’ in the same category? Very likely.”

On the economic front, careful consideration of the data-generating process provides insight into how “surprises” can emerge in a very predictable way. For example, although short-term economic data isn’t particularly cyclical, the expectations of investors and economists typically swing too far in the direction of recent news, which in turn creates cycles in economic “surprises” because not many periods contain an utter preponderance of only-good or only-bad data. In modeling this process, the same behavior can be produced in random data. The length of the cycle appears to be proportional to the length of the “lookback” period used to determine whether the recent trend of the data is favorable or unfavorable.

Case in point, there’s a perception that the recent economic data has somehow changed the prospects for a U.S. recession. The idea is that while the data has remained generally weak, the latest reports have been better than expectations. However, it turns out that there is a fairly well-defined ebb-and-flow in “economic surprises” that typically runs over a cycle of roughly 44 weeks (which is by no means a magic number). The Citigroup Economic Surprises Index tracks the number of individual economic data points that come in above or below the consensus expectations of economists. I’ve updated a chart that I last presented in 2011, which brings that 44-week cycle up-to-date. Conspiracy theorists take note – the recent round of “surprises” follows the fairly regular pattern that we’ve observed in recent years. There’s no manipulation of the recent data that we can find – it just happens that the sine wave will reach its peak right about the week of the general election.

In short, it is not enough to examine data, even large volumes of it. In order to extract information and draw conclusions, it is crucial to think about the process that is involved in generating that data. In other words, it helps to think about the interactions between buyers and sellers, the effect of expectations and how they are formed, and – for physical and biological data – the actual systems that are operating to produce the facts and figures that are being analyzed.

If investors believe that the markets are simply balloons that increase as funds flow in and out, that stocks should be valued as a simple multiple of profits without concern for profit margins or the factors that drive those margins, that stocks are “under-owned” simply because an enormous volume of low-interest debt has been issued, that moderate growth in a distorted housing market representing a diminished fraction of economic activity will suddenly drive a robust recovery, and that central bank “puts” are a reliable defense against market losses – with no need to consider the mechanism by which those puts supposedly work – then the willingness to accept significant market risk is understandable. For my part, I am convinced that these beliefs are at odds with how the data are actually generated. The red flags are significant not only for the stock market, but for the bond market (particularly credit-sensitive debt) and the economy as well.