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Archive for the ‘Behavioral economics’ Category

Cliff Asness, founder of AQR, seems to be doing the rounds lately. Forbes has a great interview with him called Efficient Market? Baloney, Says Famed Value And Momentum Strategist Cliff Asness.

Here’s Asness talking about the outperformance of value:

Forbes: Isn’t it all about probabilities? You can’t predict the future, but do you feel you can find patterns that generally hold up?

Asness: It’s all about probabilities. And I love that you put it that way. I don’t think it’s different necessarily for non-quantitative firms. We just might acknowledge it a little more explicitly. But I’m in a business where if 52% of the day I’m right, I’m doing pretty well over the long term. That’s not so easy to live with on a daily basis. I like to say, when I say a strategy works, I kind of mean six or seven out of 10 years. A little more than half the days. If your car worked like this you’d fire your mechanic. But we are playing the odds. Some famous findings, cheap stocks, defined simply, price-to-book, cash flow, sales.

You can try to do better, but define it simply. Beat expensive stocks. They beat them on average with a small margin and you want to own a ton of them and be underweight or short a ton of the expensive ones because something like this for one stock means almost nothing.

On active versus passive investment, and the central paradox of efficient markets:

Forbes: How do you defend your approach? Not in the specifics, but the whole thing on indexing. We all know Malkiel and others, Charlie Ellis, will tell you that if you have the discipline to stick to an index, low fees. Your fees are relatively high. Wouldn’t you just be better off saving all that brain power and just riding the wave?

Asness: Sure. Off the bat I’ll tell you my two investing heroes, and there are a lot of good ones to choose from, are Jack Bogle and my dissertation advisor, Gene Fama. So I can’t sit here and put down indexing too much. And in fact when I’m asked, “What advice would you give individuals who are not going to dedicate themselves to this and what not,” I tell them, “The market might not be perfectly efficient, but for most people acting as if it is,” and this is not the only way to get to an index fund, but it’s one route to get to it, it’s certainly one route that implies an index fund. I tell them to do that.

Having said that, there are a lot of ways to get here, but there’s a central paradox to efficient markets. Efficient markets says you can’t beat an index, the price contains all the information. For a long time we’ve known, academics have known, that somebody has to be gathering that information. The old conundrum: What if everyone indexed? Prices would be wildly inefficient.

So I do take what ends up being an arrogant view, and I admit it, that on average people don’t beat the index. Here are the mistakes they make. Here are the risk premiums you can pick up. I do think it’s somewhat profitable, and we want to be some of the people helping make the markets efficient and we think you get paid for that. So that is both how I reconcile it and how I sleep at night.

But if an individual came to me and said, “What should I do?” I don’t say, “Pour your money into my fund,” because, for one thing, they don’t know that much and if we have a bad year they won’t stick with it. I tell them, “Study the history a little bit, just a little bit, and put it in the most aggressive mix of stocks and bonds that you wouldn’t have thrown up and left in the past. And go to Jack Bogle to get it.”

On quantitative value as practiced by AQR:

Forbes: Quickly go over what you call the four styles of investing, starting with value.

Asness: …

And that’s kind of the holy grail of investing. To find various investments, of course, and I know you know this, that go up over time — there’s no substitute for going up — but that go up at different times.

To us, the academic literature has produced a ton. If you want to be a cynic, they’ve produced too many. A lot of smart people with even smarter computers will turn out a lot of past results and we have hundreds of different effects. The blank effect. The silliest ones are things like the Super Bowl, the sunspot effect.

But when someone searches every piece of data and it’s not that silly, it involves an accounting variable, even if ultimately it’s silly when you drill down, it’s harder to dismiss. What we did was kind of almost a self examination of going through and saying, “Of all these things we’ve been reading about for years, many of which we’ve been implementing, if we had to bet the ranch,” now we’re quant, so we never bet the single ranch on anything but over the long-term, if we had to really say, “What are the biggies that we would be most confident in?” They have to have worked for the long term. Data still counts. They need to have great intuition. They have to have worked out of sample, that thing I was talking about before, after they’ve been discovered. If they were discovered in 1970, how’d they do after that? A telltale sign of something that was dredged out of the data is discovered in 1970. Wonderful for the first 70 years of the century and terrible for the last 30 years.

And it has to be implementable. Meaning, there are some things that academics and others look at that when you try with real world transactions cost in a real world portfolio, you find, “Gee, gross, I made a lot of money but net Wall Street made a lot of money. I didn’t make a lot of money.”

Came down to four. Value. Cheap beats expensive. Famous is in U.S. stocks. For me it’s very related to Graham and Dodd value. It’s the same intuition. But where most Graham and Dodd investors will use it to pick a handful of stocks we’ll say the thousand cheapest on our favorite measures will beat the thousand most expensive. We’re betting on a concept more than a specific firm, but looking for the same ideas.

I still think of value as the hero of the story. You’re a manager long cheap and short expensive. I’m the momentum heretic. I’m long good momentum, short bad momentum. A good year for you is usually not my best year. Think about it. It works in the math but also in spirit. When value’s being rewarded you would not think it’s a particularly good time for momentum.

If there’s any magic to the finding, and I’m still amazed by it, is while we hedge each other a bit, more than a bit, both of us make money if we follow it with discipline over time.

Read Forbes’ Efficient Market? Baloney, Says Famed Value And Momentum Strategist Cliff Asness.

Click here to read earlier articles on Asness, AQR’s Value Strategies In Practice or On The Great Shiller PE Controversy: Are Cyclically-Adjusted Earnings Below The Long-Term Trend?.

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

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Institutional Investor has a great piece from Clifford Asness and John Liew called The Great Divide over Market Efficiency on the efficient markets debate. Most interesting for me was their discussion on the launch of AQR and the “value” strategies it employs:

Starting in the mid-1980s, researchers began investigating simple value strategies. That’s not to say value investing was invented at that time. We fear the ghosts of Benjamin Graham and David Dodd too much to ever imply that. This was when researchers began formal, modern academic studies of these ideas. What they found was that Graham and Dodd had been on to something. Stocks with lower price multiples tended to produce higher average returns than stocks with higher price multiples. As a result, the simplest diversified value strategies seemed to work. Importantly, they worked after accounting for the effects of CAPM (that is, for the same beta, cheaper stocks still seemed to have higher expected returns than more expensive stocks). The statistical evidence was strong and clearly rejected the joint hypothesis of market efficiency and CAPM.

We started our careers in the early 1990s, when as a young team in the asset management group at Goldman, Sachs & Co. we were asked to develop a set of quantitative trading models. Why they let a small group of 20-somethings trade these things we’ll never know, but we’re thankful that they did. Being newly minted University of Chicago Ph.D.s and students of Gene Fama and Ken French, the natural thing for us to do was develop models in which one of the key inputs was value. …

Asness Long Short Value v2

Above is a graph of the cumulative returns to something called HML (a creation of Fama and French’s). HML stands for “high minus low.” It’s a trading strategy that goes long a diversified portfolio of cheap U.S. stocks (as measured by their high book-to-price ratios) and goes short a portfolio of expensive U.S. stocks (measured by their low book-to-price ratios). The work of Fama and French shows that cheap stocks tend to outperform expensive stocks and therefore that HML produces positive returns over time (again, completely unexplained by the venerable CAPM). The graph above shows this over about 85 years.

If you notice the circled part, that’s when we started our careers. Standing at that time (before the big dip you see rather prominently), we found both the intuition and the 65 years of data behind this strategy pretty convincing. Obviously, it wasn’t perfect, but if you were a long-term investor, here was a simple strategy that produced positive average returns that weren’t correlated to the stock market. Who wouldn’t want some of this in their portfolio?

Fortunately for us, the first few years of our live experience with HML’s performance were decent, and that helped us establish a nice track record managing both Goldman’s proprietary capital, which we began with, and the capital of some of our early outside investors. This start also laid the groundwork for us to team up with a fellow Goldman colleague, David Kabiller, and set up our firm, AQR Capital Management.

As fate would have it, we launched our first AQR fund in August 1998. You may remember that as an uneventful little month containing the Russian debt crisis, a huge stock market drop and the beginning of the rapid end of hedge fund firm Long-Term Capital Management. It turned out that those really weren’t problems for us (that month we did fine; we truly were fully hedged long-short, which saved our bacon), but when this scary episode was over, the tech bubble began to inflate.

We were long cheap stocks and short expensive stocks, right in front of the worst period for value strategies since the Great Depression. Imagine a brand-new business getting that kind of result right from the get-go. Not long cheap stocks alone, which simply languished, but long cheap and short expensive! We remember a lot of long-only value managers whining at the time that they weren’t making money while all the crazy stocks soared. They didn’t know how easy they had it. At the nadir of our performance, a typical comment from our clients after hearing our case was something along the lines of “I hear what you guys are saying, and I agree: These prices seem crazy. But you guys have to understand, I report to a board, and if this keeps going on, it doesn’t matter what I think, I’m going to have to fire you.” Fortunately for us, value strategies turned around, but few know the limits of arbitrage like we do (there are some who are probably tied with us).

On the question of market efficiency, years as practitioners have put Asness and Liew somewhere between Fama and Shiller:

We usually end up thinking the market is more efficient than do Shiller and most practitioners — especially, active stock pickers, whose livelihoods depend on a strong belief in inefficiency. As novelist Upton Sinclair, presumably not a fan of efficient markets, said, “It is difficult to get a man to understand something, when his salary depends upon his not understanding it!” However, we also likely think the market is less efficient than does Fama.

After backtesting countless “value” and fundamental strategies for our book Quantitative Value I found myself in the same boat. There exist some strategies that, over the long term, lead to a consistent, small margin over market, but fewer work than most believe, and our own efforts to cherry pick the model inevitably lead to underperformance.

Click here to read The Great Divide over Market Efficiency and here if you’d like to read more on Quantitative Value, or connect with me on LinkedIn.

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Jason Zweig has a great blog post about Dean LeBaron, founder of Batterymarch Financial Management, and pioneer of quantitative investing: the use of statistical analyses rather than human judgment to pick stocks. Batterymarch’s Dean Williams delivered the incredible “Trying Too Hard” speech from 1981, which is required reading if you’re interested in behavioral investment:

I had just completed what I thought was some fancy footwork involving buying and selling a long list of stocks. The oldest member of Morgan’s trust committee looked down the list and said, “Do you think you might be trying too hard?” A the time I thought, “Who ever heard of trying too hard?” Well, over the years I’ve changed my mind about that. Tonight I’m going to ask you to entertain some ideas whose theme is this: We probably are trying too hard at what we do. More than that, no matter how hard we try, we may not be as important to the results as we’d like to think we are.

LeBaron, 80 years old, spoke to Zweig from his home near Sarasota, Fla. He believes that the name of the game for investors has been to make as much money as possible, but from now on, the prime directive will be to “lose as little money as possible.” 

If we are in a transition period, then the person who is in the most danger is the one who has recently done well, because he’s done well on things that are about to change.

In complex systems, the dynamics are predictable but the timing isn’t. It’s like adding a grain of sand one at a time to a pile: You can’t tell when it will collapse, but you know it will.

The highlight for me is this story about one of Mr. LeBaron’s most successful techniques at Batterymarch. Every year he ran a contest to see who could pick the stocks that would perform worst–not best–over the next year. Mr. LeBaron then went out and bought them all–more than 100 at a time–believing that if you can hold on for several years:

You should make enough on the ones that don’t go bankrupt to make up for the ones that do.

It’s harder than it sounds.

Read Jason Zweig’s blog post about Dean LeBaron.

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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Here’s the updated St Louis Fed’s FRED on Warren Buffett’s favored market measure, total market capitalization-to-GNP:

FRED Graph

The Q1 2013 ratio – the most recent point – is 110 percent.

According to the FRED data, the Q1 2000 TTM/GNP peak ratio was 158 percent, and the Q3 2007 TTM/GNP peak was 114 percent. The average for the full period – Q3 1949 to Q3 2012 – is 69 percent. The last time the market traded at a below-average ratio was Q1 2009.

Here’s the log version:

FRED Graph

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 Is the AAII Sentiment Survey a Contrarian Indicator? Charles Rotblut, CFA asks if the AAII Sentiment Survey results signal future market direction.

Each week from Thursday 12:01 a.m. until Wednesday at 11:59 p.m. the AAII asks its members a simple question:

Do you feel the direction of the stock market over the next six months will be up (bullish), no change (neutral) or down (bearish)?

AAII members participate by visiting the Sentiment Survey page (www.aaii.com/sentimentsurvey) on AAII.com and voting.

Bullish sentiment has averaged 38.8% over the life of the survey. Neutral sentiment has averaged 30.5% and bearish sentiment has averaged 30.6% over the life of the survey.

In order to determine whether there is a correlation between the AAII Sentiment Survey and the direction of the market, Rotblut looked at instances when bullish sentiment or bearish sentiment was one or more standard deviations away from the average. He then calculated the performance of the S&P 500 for the following 26-week (six-month) and 52-week (12-month) periods. The data for conducting this analysis is available on the Sentiment Survey spreadsheet, which not only lists the survey’s results, but also tracks weekly price data for the S&P 500 index.

Table 2 from the article has the results:

Table 2. Performance of Sentiment Survey as a Contrarian Indicator

Sentiment Level Number of
Observations
Average
S&P 500
Change
(%)
Median
S&P 500
Change
(%)
% of
Periods
Correctly
Contrarian
(%)
6-Month Performance
Bullish > +3 S.D. From Mean
2.0
7.4
7.4
0.0
Bullish > +2 S.D. From Mean
44.0
-0.7
0.3
48.0
Bullish > +1 S.D. From Mean
167.0
0.8
2.9
34.0
Bullish < –1 S.D. From Mean
212.0
6.9
6.2
80.0
Bullish < –2 S.D. From Mean
16.0
14.0
17.7
100.0
Bearish > +3 S.D. From Mean
3.0
25.8
23.0
100.0
Bearish > +2 S.D. From Mean
50.0
2.8
5.3
60.0
Bearish > +1 S.D. From Mean
162.0
4.7
6.0
71.0
Bearish < –1 S.D. From Mean
211.0
3.8
4.5
26.0
Bearish < –2 S.D. From Mean
9.0
-5.5
-1.7
67.0
All
1,319.0
4.0
4.7
12-Month Performance
Bullish > +3 S.D. From Mean
2.0
3.6
3.6
50.0
Bullish > +2 S.D. From Mean
44.0
-2.0
3.6
48.0
Bullish > +1 S.D. From Mean
167.0
2.4
6.3
31.0
Bullish < –1 S.D. From Mean
206.0
12.9
14.3
84.0
Bullish < –2 S.D. From Mean
16.0
20.7
21.7
100.0
Bearish > +3 S.D. From Mean
3.0
35.0
25.6
100.0
Bearish > +2 S.D. From Mean
50.0
3.1
14.3
60.0
Bearish > +1 S.D. From Mean
152.0
7.1
11.8
74.0
Bearish < –1 S.D. From Mean
211.0
7.7
9.9
24.0
Bearish < –2 S.D. From Mean
9.0
-4.3
4.8
44.0
All
1,293.0
8.4
10.2
Based on data from July 24, 1987, to May 2, 2013. Numbers are rounded.

Rotblut observes:

Neither unusual nor extraordinarily high levels of optimism are highly correlated with declining stock prices when the entire survey’s history is considered. The 44 periods with bullish sentiment readings more than two standard deviations above average were followed by a six-month fall in the S&P 500 only 48% of the time. The average six-month decline was 0.7%.

Extraordinarily high levels of pessimism have a mixed record of being correlated with higher stock prices. On a six-month basis, the S&P 500 rose 60% of the time following a bearish sentiment reading more than two standard deviations above the historical mean. The average and median gains were 2.8% and 5.3%, respectively. On a 12-month basis, the S&P 500 rose 60% of the time, with an average gain of 3.1% and a median gain of 14.3%. The average increases in prices are well below the typical increases realized throughout the entire history of the survey, though the median increases are greater than the typical gains.

Read Is the AAII Sentiment Survey a Contrarian Indicator?

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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Margin debt in the United States — money borrowed against securities in brokerage accounts — has risen to its highest level ever, at $384 billion, surpassing the previous peak of $381 billion set in July 2007 according to New York Times Business Day’s Off The Charts: Sign of Excess?. Margin debt as a proportion of GDP is not quite yet at the peak set in 2007, but it has exceeded 2.25% only twice previously in the last 50 years–2000 and 2007. The bottom panel shows that each of those instances was followed by a large drawdown:

NYT Margin Debt

Read Off The Charts: Sign of Excess?

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.

h/t SD.

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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.

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

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

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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