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Archive for May, 2013

Butler|Philbrick|Gordillo & Associates is out with a great new post “Triumph of the Ostriches” discussing the market’s current level of overvaluation. Here is the summary of Butler|Philbrick|Gordillo’s forecasts:

Table 1. Statistical Return Forecasts for U.S. Stocks Over Relevant Investment Horizons

Source: Shiller (2013), DShort.com (2013), Chris Turner (2013), World Exchange Forum (2013), Federal Reserve (2013), Butler|Philbrick|Gordillo & Associates (2013)

Butler|Philbrick|Gordillo comment:

We have yet to see any evidence-based argument for why the valuation based analysis presented above is not relevant. What do we mean by ‘evidence based’? Show us numbers to support an alternative hypothesis, and then show me how those numbers have served to forecast returns in other periods with statistical significance.

Other memes relate to the idea of a ‘permanently high plateau’ (incidentally, the great 20th century economist Irving Fisher coined that phrase in 1929, just three days before the crash that preceded the Great Depression). Purveyors of this delusion cite the current ‘pollyanna’ environment for global corporations as validation for stratospheric equity valuations. “Corporations have high record cash positions”, they crow, “get ready for the great buy back and merger wave that’s coming!” “Profit margins are high, corporate taxes are near all-time lows, wage pressures are non-existent – corporations have never had it better! Oh and financing is effectively free!”

Unfortunately the wailing equity zealots do not factor in Stein’s Law, which states, “If something cannot go on forever, it will stop.” In a period of record fiscal duress, what is the probability that corporations will continue to receive favourable tax status? According to GMO’s analysis, corporate profit margins are one of the most mean-reverting series in finance, so why would be value markets under the assumption that they will stay high forever? Further, how valuable is the cash on corporate balance sheets if there is an equally large debt balance on the other side of the ledger (there is)?

The Ostriches aren’t concerned with valuation metrics or Stein’s Law, and let’s face it, they’ve been right to stick their head in the sand – at least so far. The problem is that in markets we won’t know who is right until the bottom of the final cyclical bear in this ongoing secular bear market. Only then will we see just how far from fundamentals the authorities have managed to push prices, and only then will we see whether it really is different this time.

Until then, investors can choose facts or faith. The facts say that investors are unlikely to be compensated at current valuations for the risks of owning stocks over the next few years. The church of equities says, ‘don’t worry about it’. So far the Ostriches have it, but all meaningful evidence suggests that over the next few years the Ostriches are going to feel like turkeys – at Thanksgiving.

Read Triumph of the Ostriches.

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Warren Buffett’s favored market valuation metric, market capitalization-to-gross national product, has passed an unwelcome milestone: the 2007 valuation peak, according to GuruFocus:

TMTGNP 2007

The index topped out at 110.7 percent in 2007, and presently stands at 111.7 percent. From GuruFocus:

As of today, the Total Market Index is at $ 17624.4 billion, which is about 111.7% of the last reported GDP. The US stock market is positioned for an average annualized return of 2.2%, estimated from the historical valuations of the stock market. This includes the returns from the dividends, currently yielding at 2%.

I’ve seen several arguments for why this time is different, and why it’s not a bubble. I don’t buy it. When we see clear skies, that’s all we can imagine, and so we extrapolate it over the horizon. From Seth Klarman’s latest:

Investing, when it looks the easiest, is at its hardest. When just about everyone heavily invested is doing well, it is hard for others to resist jumping in. But a market relentlessly rising in the face of challenging fundamentals–recession in Europe and Japan, slowdown in China, fiscal stalemate and high unemployment in the U.S.– is the riskiest environment of all.

[O]nly a small number of investors maintain the fortitude and client confidence to pursue long-term investment success even at the price of short-term underperformance. Most investors feel the hefty weight of short-term performance expectations, forcing them to take up marginal or highly speculative investments that we shun. When markets are rising, such investments may perform well, which means that our unwavering patience and discipline sometimes impairs our results and makes us appear overly cautious. The payoff from a risk-averse, long-term orientation is–just that–long term. It is measurable only over the span of many years, over one or more market cycles.

Our willingness to invest amidst failing markets is the best way we know to build positions at great prices, but this strategy, too, can cause short-term underperformance. Buying as prices are falling can look stupid until sellers are exhausted and buyers who held back cannot effectively deploy capital except at much higher prices. Our resolve in holding cash balances–sometimes very large ones–absent compelling opportunity is another potential performance drag.

For more on market value-to-GNP see my earlier posts Warren Buffett Talks… Total Market Value-To-Gross National ProductWarren Buffett and John Hussman On The Stock MarketFRED on Buffett’s favored market measure: Total Market Value-to-GNPThe Physics Of Investing In Expensive Markets: How to Apply Simple Statistical Models.

Order Quantitative Value from Wiley FinanceAmazon, or Barnes and Noble.

Click here if you’d like to read more on Quantitative Value, or connect with me on LinkedIn.

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In a great article The Wiki Man: If you want to diet, I’m afraid you really do need one weird rule Rory Sutherland argues that we require a “black-and-white, binary approach” to things we find psychologically difficult to follow. Sutherland says, “And as the world’s religions have known for thousands of years, abstinence is far easier than the continuous exercise of self-restraint. Or, as the neuroscientist V.S. Ramachandran suggests, “humans may not have free will but they do have free won’t.”

Absolute rules (if X, then Y) work with the grain of human nature. We feel far more guilt running a red light than breaking a speed limit. Notice that almost all religious laws are absolute: no food is half kosher; it is or it isn’t. No Old Testament prophet proposed something as daft as the French 35-hour ‘working-time directive’: they invented the Sabbath instead.

In a more complex world weighed down by Big Data, convoluted tax structures and impenetrable legislation, do we actually need more of what religion once gave us: simple, unambiguous, universal absolutes? In law such rules are known as Bright Line Rules: rather than 20 million words of tax law, you simply declare ‘any financial transaction whose only conceivable motivation is the avoidance of tax is by definition illegal’.

Does a complex world need simpler rules? And simpler metrics? The temptation is that because we have gigabytes of data, we feel the need to use all of it. Perhaps all you need is a few bits of the right information?

During the second world war, experts needed to decide whom to train as RAF fighter pilots. Today this would mean a battery of complex tests. Back then they used two simple questions: 1) Have you ever owned a motorcycle? 2) Do you own one now? The ideal recruits were those who answered 1) Yes and 2) No. They wanted people who had been brave enough to ride a motorbike but were sane enough to abandon the habit.

How many of the world’s problems could be solved if we abandoned this pretence of perfect rationality and fell back on simple, heuristic rules of thumb? According to the brilliant German decision-scientist Gerd Gigerenzer, quite a few.

The investing corollaries are easy to find. I’ll expand on that later this week.

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h/t @abnormalreturns and @farnamstreet

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David Tepper was on CNBC this morning arguing that stocks are historically cheap:

[Tepper] said the post showed “when the equity risk premium is high historically, you get better returns after that.” He continued, “So we’re at one of the highest all-time risk premiums in history.”

In making his argument Tepper referred to this article, Are Stocks Cheap? A Review of the Evidence, in which Fernando Duarte and Carlo Rosa argue that stocks are cheap because the “Fed model”—the equity risk premium measured as the difference between the forward operating earnings yield on the S&P500 and the 10-year Treasury bond yield—is at a historic high. Here’s the chart:

Here’s Duarte and Rosa in the article:

Let’s now take a look at the facts. The chart [above] shows the weighted average of the twenty-nine models for the one-month-ahead equity risk premium, with the weights selected so that this single measure explains as much of the variability across models as possible (for the geeks: it is the first principal component). The value of 5.4 percent for December 2012 is about as high as it’s ever been.The previous two peaks correspond to November 1974 and January 2009. Those were dicey times. By the end of 1974, we had just experienced the collapse of the Bretton Woods system and had a terrible case of stagflation. January 2009 is fresher in our memory. Following the collapse of Lehman Brothers and the upheaval in financial markets, the economy had just shed almost 600,000 jobs in one month and was in its deepest recession since the 1930s. It is difficult to argue that we’re living in rosy times, but we are surely in better shape now than then.

The Fed model seems like an intuitive measure of market valuation, but how predictive has it been historically? John Hussman examined it in his August 20, 2007 piece Long-Term Evidence on the Fed Model and Forward Operating P/E Ratios. Hussman writes:

The assumed one-to-one correspondence between forward earnings yields and 10-year Treasury yields is a statistical artifact of the period from 1982 to the late 1990’s, during which U.S. stocks moved from profound undervaluation (high earnings yields) to extreme overvaluation (depressed earnings yields). The Fed Model implicitly assumes that stocks experienced only a small change in “fair valuation” during this period (despite the fact that stocks achieved average annual returns of nearly 20% for 18 years), and attributes the change in earnings yields to a similar decline in 10-year Treasury yields over this period.

Unfortunately, there is nothing even close to a one-to-one relationship between earnings yields and interest rates in long-term historical data. Why doesn’t Wall Street know this? Because data on forward operating earnings estimates has only been compiled since the early 1980’s. There is no long-term historical data, and for this reason, the “normal” level of forward operating P/E ratios, as well as the long-term validity of the Fed Model, has remained untested.

Ruh roh. The Fed model is not predictive? What is? Hussman continues:

… [T]he profile of actual market returns – especially over 7-10 year horizons – looks much like the simple, humble, raw earnings yield, unadjusted for 10-year Treasury yields (which are too short in duration and in persistence to drive the valuation of stocks having far longer “durations”).

On close inspection, the Fed Model has nearly insane implications. For example, the model implies that stocks were not even 20% undervalued at the generational 1982 lows, when the P/E on the S&P 500 was less than 7. Stocks followed with 20% annual returns, not just for one year, not just for 10 years, but for 18 years. Interestingly, the Fed Model also identifies the market as about 20% undervalued in 1972, just before the S&P 500 fellby half. And though it’s not depicted in the above chart, if you go back even further in history, you’ll find that the Fed Model implies that stocks were about as “undervalued” as it says stocks are today – right before the 1929 crash.

Yes, the low stock yields in 1987 and 2000 were unfavorable, but they were unfavorable without the misguided one-for-one “correction” for 10-year Treasury yields that is inherent in the Fed Model. It cannot be stressed enough that the Fed Model destroys the information that earnings yields provide about subsequent market returns.

The chart below presents the two versions of Hussman’s calculation of the equity risk premium along with the annual total return of the S&P 500 over the following decade.

Source: Hussman, Investment, Speculation, Valuation, and Tinker Bell (March 2013)

That’s not a great fit. The relationship is much less predictive than the other models I’ve considered on Greenbackd over the last month or so (see, for example, the Shiller PE, Buffett’s total market capitalization-to-gross national product, and the equity q ratio, all three examined together in The Physics Of Investing In Expensive Markets: How to Apply Simple Statistical Models). Hussman says in relation to the chart above:

… [T]he correlation of “Fed Model” valuations with actual subsequent 10-year S&P 500 total returns is only 47% in the post-war period, compared with 84% for the other models presented above [Shiller PE with mean reversion, dividend model with mean reversion, market capitalization-to-GDP]. In case one wishes to discard the record before 1980 from the analysis, it’s worth noting that since 1980, the correlation of the FedModel with subsequent S&P 500 total returns has been just 27%, compared with an average correlation of 90% for the other models since 1980. Ditto, by the way for the relationship of these models with the difference between realized S&P 500 total returns and realized 10-year Treasury returns.

Still, maybe the Fed Model is better at explaining shorter-term market returns. Maybe, but no. It turns out that the correlation of the Fed Model with subsequent one-year S&P 500 total returns is only 23% –  regardless of whether one looks at the period since 1948 (which requires imputed forward earnings since 1980), or the period since 1980 itself. All of the other models have better records. Two-year returns? Nope. 20% correlation for the Fed Model, versus an average correlation of 50% for the others.

Are stocks cheap on the basis of the Fed model? It seems so. Should we care? No. I’ll leave the final word to Hussman:

Over time, Fed Model adherents are likely to observe behavior in this indicator that is much more like its behavior prior to the 1980’s. Specifically, the Fed model will most probably creep to higher and higher levels of putative “undervaluation,” which will be completely uninformative and uncorrelated with actual subsequent returns.

The popularity of the Fed Model will end in tears. The Fed Model destroys useful information. It is a statistical artifact. It is bait for investors ignorant of history. It is a hook; a trap.

Hussman wrote that in August 2007 and he was dead right. He still is.

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I’m back from the Value Investing Congress in Las Vegas. There were a number of outstanding presentations, but, for mine, the best was Vitaliy Katsenelson’s epic presentation based on his Little Book of Sideways Markets.

12 years into this sideways market, valuations are still 30% above the historical average, while in 1982 they were about 30% percent below average! Also, historically, stocks spent a good amount of time at below-average valuations before sideways market turned into a secular bull market.

Vitaliy shows that genuine 1930s-style bear markets are rare. Most of the time the market trades sideways or up.

slide-31

Since 2000, the market has traded sideways. Vitaliy expects this to continue for another decade:

slide-41

Read GDP growth has been consistent. There’s little relationship between earnings growth and stock returns.

slide-61

Real GDP growth is very similar in both sideways and bull markets…

slide-71

…the difference in returns is the change in valuation.

slide-121

Don’t chase stocks. In the absence of good stocks, hold cash.

slide-311

Sideways markets contain many cyclical bull and bear markets.

slide-321During a sideways market, asset allocation is not as important as stock selection.

slide-341

 

See the full presentation:

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Chris Turner has a guest post at Doug Short’s Advisor Perspectives called When Warren Buffett Talks … People Listen examining Warren Buffett’s favored market valuation metric: Market Value divided by Gross National Product. (I’ve also examined market value-to-GNP several times. See Warren Buffett and John Hussman On The Stock MarketFRED on Buffett’s favored market measure: Total Market Value-to-GNPThe Physics Of Investing In Expensive Markets: How to Apply Simple Statistical Models)

Here Chris looks at the metric using the CPI deflator on both the numerator — market value — and the denominator — Gross National Product.

Here Chris calculates two fair values for the S&P 500. The blue line shows the historical mean and the green line shows Buffett’s 80 percent value estimate:

Chris comments:

Readers can see from the chart that based on both Buffett’s rule and the historical mean, the S&P would be trading much lower from present levels. The S&P would be sub 1000 based on the historical mean and around 1150 based on the 80% Buffett rule.

Read When Warren Buffett Talks … People Listen.

Order Quantitative Value from Wiley FinanceAmazon, or Barnes and Noble.

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I’ve posted here regularly about the implications of mean reversion in elevated profit margins (see, for example, The Temptation To Abandon Proven Models In Speculative and Fearful Markets: Why This Time Isn’t Different, What Record Corporate Profit Margins Imply For Future Profitability and The Stock MarketWarren Buffett, Jeremy Grantham, and John Hussman on Profit, GDP and Competition). Those posts sparked some intense debate in the comments and offline about the increasing influence of foreign profits on corporate profit margins, and how this change may have permanently shifted up the mean for corporate profits as a proportion of GDP. The impact of such a structural change in the mean is twofold: First, it implies that the current cyclical extreme in the level of corporate profits as a proportion of GDP is less extreme than it appears on its face; and, second, that the ratio of corporate profits-to-GDP is less predictive as an indicator than it has been historically.

This is the chart, and the following comment, that sparked the debate:

Source: Hussman Weekly Comment “Taking Distortion at Face Value,” (April 8, 2013)

Hussman commented in relation to the chart (in Two Myths and a Legend, March 11, 2013):

In general, elevated profit margins are associated with weak profit growth over the following 4-year period. The historical norm for corporate profits is about 6% of GDP. The present level is about 70% above that, and can be expected to be followed by a contraction in corporate profits over the coming 4-year period, at a roughly 12% annual rate. This will be a surprise. It should not be a surprise.

Raj Yerasi, a money manager based in New York, has taken on the unenviable task in the following guest post of arguing the case that the increasing influence of foreign earnings on corporate profit margins means that the ratio in the chart overstates future mean reversion in earnings:

Profiting From Profits

Today more than ever the question of whether the stock market is overvalued or reasonably valued depends on whether corporate profit margins are abnormally elevated or sustainable. Some astute investors (such as Hussman and GMO) have argued in essence that the combination of record government deficit spending and unemployment levels has propped up corporate revenues while lowering labor costs, thereby boosting corporate profit margins by as much as 70 percent above historical averages. They contend that the withdrawal of fiscal stimulus as well as competitive dynamics will sooner or later cause profit margins to revert to the mean, unmasking substantial equity overvaluation.

This analysis purports to show that profits are indeed elevated above historical levels, but not nearly as much as some investors think, due to issues in the BEA’s NIPA data series they are using. Furthermore, the impact of any mean reversion in profit margins on overall equity market profits may be lower than people think.

To understand this, it is important to note that current analyses do not directly measure profit margins per se (meaning, profits divided by revenues). Rather, they measure corporate profits as a percentage of GDP, which captures not total revenues but the total value addition of corporations (along with other components). While there are multiple potential data issues in comparing profits to GDP, it nonetheless stands to reason that profits as a percentage of GDP should generally correlate with profit margins.

However, one big source of error is that the most widely known NIPA corporate profits data series, which the analyses referenced above appear to be using, represents profits generated by corporations that are considered US residents. As such, this data series includes profits generated by US companies’ international operations (e.g. Coca-Cola India, Coca-Cola China) and excludes profits generated by foreign companies’ US operations (e.g. Toyota USA). GDP, meanwhile, captures all economic activity within US borders, whether undertaken by US companies or foreign companies, and it excludes any economic activity abroad. It should be clear that one cannot compare these two metrics, since the corporate profits data series introduces profits generated by other economies and excludes profits generated by the US economy.

Since we are interested in how profit levels have changed over time, this mismatch might not matter, except that US companies’ profits from abroad have grown tremendously over the last 10 years, much more so than foreign companies’ profits from US operations:



This skews the calculated profits level upwards and by an increasing amount over time, making profit levels today look exceedingly elevated.

To do the analysis correctly, we need to use data that are more apples-to-apples. Fortunately, the NIPAs do include a data series of corporate profits that simultaneously excludes US companies’ profits from abroad and includes foreign companies´ profits from US operations, called “domestic industries” profits. Comparing these profits to GDP, profit levels still appear elevated but now not as much as when using the prior “national” profits data series:



Profits now appear to be at levels matching previous highs rather than at levels far exceeding previous highs. Comparing these levels to the average level since 1948, current profit levels are 40 percent above the average, with this average including an extended contractionary period in the 70s and 80s.

It is worth noting that these percentages match very closely with those cited by Warren Buffett in his 1999 article “Mr. Buffett On The Stock Market“. If we use the 4.0 percent to 6.5 percent range that Mr. Buffett observed as a “normalcy” band, then profit levels today are about 30 percent above the midpoint of that band. That is high, no doubt, but not as terrifying as 70 percent above the average.

It is also worth noting that effective corporate tax rates are lower today than in the past. Per the NIPA data, tax rates have decreased from about 45 percent in the 80s to 40 percent in the 90s to 30 percent in recent years. Using pre-tax “domestic industries” profits as a percentage of GDP, profit levels today may be closer to 20 percent elevated relative to historical norms. One may wish to focus solely on after-tax profit levels, since in theory companies target minimum after-tax returns on capital, but on the other hand, a consumer deciding whether it’s worth paying a premium for a company’s product or service may not be affected by that company’s tax burden. The right approach could be somewhere in between.

So what are the implications of all this? The actual extent and pace of mean reversion in profit margins will depend on other factors besides fiscal consolidation and unemployment: trade deficits, credit creation, tax policy, antitrust enforcement, etc. Setting all that aside, if we assume that profit margins of domestic businesses are, say, 30 percent higher than where they should be and will be, then we also need to figure out what percentage of equity market index earnings come from domestic operations. If we assume that, say, 1/3 of index earnings are from international operations that will not be affected by mean reversion in US profits, then the total drop in index earnings might only be 15 percent (since mean reversion from a 30 percent higher level implies a 23 percent drop, but only on 2/3 of earnings).

This is not to say, of course, that the consequences of mean reversion would be evenly distributed by sector. Perhaps investors are better off taking into account mean reversion on a sector by sector basis, given that we do not seem to be looking at a scenario of plummeting earnings that will sink all boats.

Special thanks to Andrew Hodge of BEA for clarifying certain NIPA data. Any remaining misunderstandings are the author´s responsibility.

Many thanks to Raj for a well-written argument.

Hussman anticipated Raj’s argument earlier. See The Temptation To Abandon Proven Models In Speculative and Fearful Markets: Why This Time Isn’t Different for Hussman’s rejoinder.

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