2016 Silver Bullet Awards Part Two

Each week I try to give special attention to those who do important work, even though it is probably unpopular. These contributors are so important, and their work is so helpful, that we recommend taking another look at the end of the year. (Part One is here).



In a WTWA first, CNBC anchor Sara Eisen earned a Silver Bullet Award for her excellent interview with Fed Vice-Chairman Stanley Fischer (Transcript and video via CNBC). As we wrote at the time:

One-by-one she asked all of the key questions in the current debate over Fed policy – potential for negative rates, Brexit impact, does the Fed make decisions based the economic impact abroad, the state of the economy, recession potential, employment, George Soros, and the strong bond market. Whether or not you agree with Vice-Chairman Fischer, it is important to know what he thinks.

Sara Eisen displayed first-rate journalism, as expected from a Medill School graduate. Unlike so many other financial interviewers she did not argue with her subject nor push her own agenda. She did raise all of the current Fed misperceptions common in the trading community. Her preparation and poise helped us all learn important information. It was well worth turning off my mute button and dialing back the TIVO.


We gave the Silver Bullet to Justin Fox for his writing on one of the most persistent myths – the manipulation of government statistics. His whole post is available here, but we particularly liked this bit:

First, because I know a little bit about the people who put together our nation’s economic statistics. The Bureau of Labor Statistics, Bureau of Economic Analysis and Census Bureau are run on a day-to-day basis by career employees, not political appointees. Even the appointees are often career staffers who get promoted, and many have served under multiple administrations. When top statistics-agency officials do leave government, it’s often for jobs in academia. Credibility with peers is generally of far more value (economic and otherwise) to these people than anything a politician could do for them.

To those with even basic experience in civil service, the political manipulation theory makes little sense.


Ben Carlson won a Silver Bullet for investigating the apparent link between Fed meetings and stock performance. While many (including at least one WSJ writer) took the rumor at face value, Ben asked a clever question: What happens if you change the starting date of the analysis?

As it turns out, any relationship between the two is likely a result of 2008.


Menzie Chinn was a big winner this year. Professor Chinn, a Wisconsin economist, debunked many annoying data conspiracies in one fell swoop. In so doing, he also illustrated how an inappropriate use of log scales can mislead readers.

We called his piece the most profitable thing for investors to read that week – if you missed it, be sure and catch up!


By late in the year, it was increasingly apparent that individual investors were misreading the VIX as a “fear indicator” rather than a measure of expected volatility. Chris Ciovacco did an excellent job in making that distinction. His image here is particularly persuasive.

Runner up awards to Jeff Macke and Adam H. Grimes for their similar conclusions on the same subject.


Shiller’s CAPE method has often caused some eyebrow-raising on A Dash, most notably since he doesn’t use it himselfJustin Lahart of the Wall Street Journal thought to analyze just how this method (and others like it) would work in practice:

For New York University finance professor Aswath Damodaran, this is the real sticking point. He set up a spreadsheet to see if there was a way that using the CAPE could boost returns. When the CAPE was high, it put more money into Treasuries and cash, and when it was low it put more into stocks.

He fiddled with it, allowing for different overvaluation and undervaluation thresholds, changing target allocations. And over the past 50-odd years, he couldn’t find a single way he could make CAPE beat a simple buy-and-hold strategy. In the end, he doesn’t think it represents an improvement over using conventional PEs to value stocks.

“This is one of the most oversold, overhyped metrics I’ve ever seen,” says Mr. Damodaran.

Mr. Shiller agrees that the CAPE can’t be used as a market-timing tool, per se. Rather, he thinks that investors should tilt their portfolios away from individual stocks that have high CAPEs. But he says he isn’t ready to modify his CAPE for judging the overall market.


With the blogosphere in full election season fever, some started to worry that the 2016 stock market gains were a precursor to something much worse. We gave the Silver Bullet to Ryan Detrick of LPL Research for discrediting this argument with two easy charts:


We make a special effort to recognize writers trying to debunk the endless onslaught of recession predictions. Bill McBride of Calculated Risk did this very effectively, with a few key points:

Note: I’ve made one recession call since starting this blog.  One of my predictions for 2007 was a recession would start as a result of the housing bust (made it by one month – the recession started in December 2007).  That prediction was out of the consensus for 2007 and, at the time, ECRI was saying a “recession is no longer a serious concern”.  Ouch.

For the last 6+ years [now 7+ years], there have been an endless parade of incorrect recession calls. The most reported was probably the multiple recession calls from ECRI in 2011 and 2012.

In May of [2015], ECRI finally acknowledged their incorrect call, and here is their admission : The Greater Moderation

In line with the adage, “never say never,” [ECRI’s] September 2011 U.S. recession forecast did turn out to be a false alarm.

I disagreed with that call in 2011; I wasn’t even on recession watch!

And here is another call [last December] via CNBC: US economy recession odds ’65 percent’: Investor

Raoul Pal, the publisher of The Global Macro Investor, reiterated his bearishness … “The economic situation is deteriorating fast.” … [The ISM report] “is showing that the U.S. economy is almost at stall speed now,” Pal said. “It gives us a 65 percent chance of a recession in the U.S.

The manufacturing sector has been weak, and contracted in the US in November due to a combination of weakness in the oil sector, the strong dollar and some global weakness.  But this doesn’t mean the US will enter a recession.

The last time the index contracted was in 2012 (no recession), and has shown contraction several times outside of a recession.

We strongly recommend reading the original post in its entirety.


Jon Krinsky of MKM and Downtown Josh Brown both earned the Silver Bullet award in late 2016, for taking on myths about currency strength and stock performance. In sum: there is zero evidence of a long-term correlation between stocks and the dollar.


Our final Silver Bullet award of the year, given on New Year’s Eve, went to Robert Huebscher of Advisor Perspectives. His full article is definitely worth a read, but choice excerpts follow below. Good financial products are bought, not sold!

But I caution anyone against buying precious metals from Lear Capital. It is not an SEC-registered investment advisor and its web site states that there is no fiduciary relationship between it and its customers.

And also…

For example, Lear will sell you a $10 circulated Liberty gold coin (1/2 ounce) for $753.00 (plus $24 shipping). I did a quick search on eBay and found a circulated Liberty coin selling for as low as $666 (with free shipping).

Buying silver is no different. Lear will sell you a pre-1921 circulated Morgan silver dollar for $30 (plus $10 shipping). On eBay, I quickly found one of these for $22.00 (plus $2.62 shipping).


As always, you can feel free to contact us with recommendations for future Silver Bullet prize winners at any time. Whenever someone takes interest in defending a thankless but essential cause, we hope you’ll find them here.  Have a Happy New Year and a profitable 2017.

Is Forecasting Always A Folly?

Forecasting season is upon us. Anyone who gets to be quoted in print or speak to a reporter is asked an opinion. Expertise not required! It is paradise for pundits.

Many have decried the “folly of forecasting.” People love to laugh at supposed experts, looking back at old forecasts. Since most forecasts are based upon a model, modelers are thrown under the bus as well.

Barry Ritholtz wrote on this topic in his excellent Apprenticed Investor series. There are now over 200,000 blog hits on this phrase.


But please consider this: Most models and forecasts are bad – very bad – but not all. The trick is to figure out which is which. Barry notes the possible exceptions:

There are only two kinds of predictions that have some value to investors: One is probability-based, and the other is risk-based. If you apply the same rules — no one knows the future, they are subject to revision and should not be taken as gospel — then these are sometimes worth considering.

Here are a few examples.

  • Millions of people attempt to paint, but only a handful are successful. Could you pick the winners?
  • Millions attempt to write, but there are few best-sellers. Could you guess them in advance?
  • Worldwide wine consumption is over 30 billion bottles. How many are really good?
  • In the U.S., 11 million people are playing baseball in a given year. Fewer than 900 are in the major leagues. There is so little difference that only an expert could identify the best players by watching them bat.

Finding the best in any large field is a real challenge!

The issue is especially important for financial analysis. I have been pondering this question for weeks. How can I best explain an important but unpopular viewpoint? I recently began this theme with by citing a bogus analysis in the New York Times. In simple fashion, I showed that if you only had the long-term average – that the market returned a positive result 2/3 of the time – you would do much better to predict “Up” every year rather than guessing 2/3 up and 1/3 down. This counter-intuitive result should be cause for thought, since it is an expensive and common investor mistake.

Ben Carlson inspired another approach. He publishes consistently strong work at his blog, A Wealth of Common Sense, which I always read and frequently cite. He discusses the difficulty of selecting the best stocks and sectors. This is the updated sector asset quilt he created, followed by his principal conclusion.

Like any asset quilt, there’s no rhyme or reason from one year to the next. I’m sure you could torture the data here using a momentum or value-based strategy to improve upon the results of the S&P 500, but unless you’re using a rules-based approach, you’re really just guessing when attempting to figure out which sectors will perform best over any given time frame.

That conclusion seems persuasive to a very intelligent observer using annual ranking changes. Can a first-rate forecaster add any value?


Finding the Real Experts

Ben is quite correct in noting that most contrived explanations will torture the data. Is this true of every approach? Here are some things I look for in evaluating a model:

  • It does not use too many variables compared to the available data
  • It has a good record using “out of sample” data as well as in real time
  • The underlying method is logical, proceeding from a theory
  • The modeler has both experience and expertise

Two sharply contrasting success stories reflect the two most common model types, trend following and mean reversion.

Dr. Robert Shiller is a leading economist at a top university, a Nobel Laureate, author of many papers and books of value to investors, and a popular media guest. Among investors, he is probably best known for his Cyclically Adjusted Price to Earnings ratio (CAPE) method. One of the methods that he endorses is the Barclays ETN, CAPE. Barclays implements CAPE in a mean reversion method. They look at the historical CAPE for each sector choosing the sectors most under-valued by this comparison (throwing out the bottom one). This is a mean-reversion method based upon fundamental data. At the introduction over four years ago, Barclays had promising backtest data. This did not attract many investors, most of whom cite CAPE as a method for timing the overall market – which Dr. Shiller himself does not do. After four years, the fund remains very small (under $34 million).

How has it done in real time?

Probably riskier than buy and hold the market, but much stronger returns. Those choosing to use CAPE as a reason to exit the market (not Dr. Shiller’s recommendation) would have done better to buy the ETN.

Dr. Vincent Castelli is not a professor at a top university, but he could have been. He will not win a Nobel prize, because his best work was top secret. He spent a career making U.S. armed forces safer and more effective, heading a group of other scientists from various disciplines. His modeling is known in quant circles, where he demonstrates, advises, and coaches. He is probably not going to become famous on CNBC.

His approach to sector analysis begins with the time-tested method of trend following. The tricks are in separating signal from noise, recognizing trends in a timely fashion, and exiting while you can protect profits. As an expert in modeling, Vince touches all the bases for sound work — ruthlessly pruning variables, a generous out-of-sample test, and real-time comparisons.

These two brilliant men took quite different approaches to life and later to analyzing stock sectors. Each found a profitable approach where most of us would see nothing.


There are many paths to successful investing. Remain open to profit opportunities by giving open-minded consideration to other approaches. Finding the best experts is just as important as finding the best stocks or sectors.

The Evolution of the “Hussman Chart”

Hardly a week goes by without an article like this one by the influential Henry Blodget — One smart stock market analyst thinks this is where we’re headed…(gulp). Mr. Blodget writes as follows:

But anyone who’s feeling comfortable after a strong week in the markets should at least understand that: 1. The macro environment most conducive to crashes is still in place (overvaluation + increasing risk aversion) and 2. The way the market is behaving now is exactly the way it behaved before the biggest crashes in history.

So, neither Hussman, nor I, nor you should be surprised if the market keeps on dropping and doesn’t bottom until it’s down 50% or more from the peak.

As Hussman noted last week in his usual depressing note, a 50% crash would not even be the worst-case scenario. It would just be a normal correction from valuations we reached in 2015.

Featured in the valuation articles is a chart purporting to show very low expected annualized returns for a multiple-year period. The implication for stock investors is clear: Little upside combined with huge risk. It has had a big impact both with individual investors and also my investment advisor colleagues.


Last week, among several other illustrations of popular investment misconceptions, I included a version of what I will call the “Hussman Chart.” I suggested that if you did not understand the chart, you shouldn’t be using it for your investment decisions. My main point was that people blindly accept conclusions from intelligent sources who use sophisticated methods. Of the are dozens of possible illustrations, I included the Hussman Chart. I know that many people sold their stocks some time ago when their investment advisors warned them, producing one of the charts I discuss below.

My worst fears were confirmed! Out of the thousands reading the post, only two or three explained anything about the chart – how it was constructed, what it implied, how to think about it. Quite a few people repeated the author’s conclusion. Wow! They understood and accepted the conclusion without any evaluation of the reasoning. Others did not want to be challenged. They wanted me to explain what I thought was wrong with the chart.

Readers promptly ignored everything else in the article. Some even concluded (amazingly) that I was stating that I personally did not understand the chart. Jesse Felder, a fellow investment advisor and blogger stated this viewpoint explicitly. His conclusion (without any explanation of the chart):

Furthermore, this negative correlation between valuations and forward returns is statistically very high (greater than -90%) and backed by 65 years worth of data. The Buffett Yardstick, as Hussman demonstrates, has been nearly as good as his own version at forecasting forward returns and is backed by roughly 90 years worth of data. Both charts, and the data and reasoning behind them, clearly demonstrate and validate the concept that, “the price you pay determines your rate of return.”


Apparently I need to elaborate on the original theme. I will do so by providing examples of “the chart” over the years. The variables, adjustments and time periods change, but the conclusions are generally the same. Each chart has a documented method and stands on its own. Together one gets a different picture. While the method is continually “improved” and the time period changed, there is never a date with destiny. We do not know whether the early versions worked or not. There is no distinction between the time period used to create the method and the “out of sample” period that follows.

Here is a summary of the charts below.

Date Independent variable Starting point Length of Forecast Adjustments
Nov, 2008 Terminal multiple 1950 7 years
Oct, 2010 Terminal yield 1944 7 years
Aug, 2010 Adjusted forward earnings 1963 10 years Reducing margins
Jan, 2011 Normalized earnings 1928 10 years “Normalizing” earnings
Dec, 2013 CAPE 1932 10 years Mean-reverting margins
Feb, 2016 Non-financial Gross Value Added 1950 12 years World effect, excludes financials

The rest of this report will show the evolution of the approach and raise some specific concerns and points that you might wish to consider.

[I have never met Dr. Hussman, but I have a generally favorable impression of him. He taught for a bit at one of my schools. (Someday I might learn if he considers himself a “Michigan man.”) He is respected as a philanthropist. His approach is intended to be in the best interest of his investors. Updating his methods and conclusions is a natural part of investment management. He reports his thinking frequently and takes on issues directly. Were this not the case, a review like this one would not be possible. He has built a very successful business and earned a strong reputation. His articles are always among the most popular, especially among investment advisors].

Analysis – the Evolution of “The Chart”

First example — How Low, How Bad, How Long? November, 2008



Second example — No Margin of Safety, No Room for Error October, 2010



Third example — Valuing the S&P 500 Using Forward Operating Earnings August, 2010


This quoted explanation illustrates why some might have trouble following the methodology:

The two main failures of standard FOE analysis are that 1) analysts assume a long-term norm for the P/E ratio that properly applies to trailing net, not forward operating earnings, and; 2) analysts fail to model the variation in prospective earnings growth induced by changes in the level of profit margins, and therefore wildly over- or underestimate long-term cash flows that are relevant to proper valuation. By dealing directly with those two issues, we can obtain useful implications about market valuation.

As I have frequently noted, it is not theory, but simple algebra, that the long-term annual total return for the S&P 500 over any horizon T can be written as:

Long term total return = (1+g)(future PE / current PE)^(1/T) – 1
+ dividend yield(current PE / future PE + 1) / 2

The first term is just the annualized capital gain, while the second term reasonably approximates the average dividend yield over the holding period. For the future P/E, one can apply a variety of historically observed P/E ratios in order to obtain a range of reasonable projections, but the most likely outcome turns out to be somewhere between the historical mean and median.

You have to get two things right: the “normal” future P/E and the prospective long-term earnings growth rate g. Standard FOE analysis misses on both counts. Very simply, looking out over a 7-10 year horizon, the proper historical norm for price-to-forward operating earnings is approximately 12.7. Moreover, one cannot simply apply the long-term operating earnings growth rate of 6.3% (0.063) as an unchanging measure of g. Rather, an accurate growth rate for the model has to reflect the level of profit margins at any point in time, since the current P/E multiple may reflect either depressed or elevated earnings. For a 10-year investment horizon, the proper value of g should take into account the gradual normalization of margins. Historically, the best estimate is approximately:

g = 1.063 x (0.072 / (FOE/S&P 500 Revenues))^(1/10) – 1

[Jeff]You should at least be able to understand that the earnings are “adjusted” by a method that is deemed to be appropriate.


Fourth example — Borrowing Returns from the Future January, 2011



Fifth example — Does the CAPE Still Work? December, 2013





[Jeff] If you look at this chart and the two above, you will see that the big divergence in the late 80’s has disappeared.

Comments on the multi-year growth projections

These are points that would be discussed extensively if the research had a peer review.

  • It is necessary to explain carefully both variables, especially making clear when one can evaluate the relationship
  • There should be a sharp distinction between the portion of a chart which is a back test, or an idea fitted to past data, and the “out of sample” data that follows.
  • A multi-year projection has an eventual “date with destiny.” If you are one year away, you can calculate the return that would be needed to make the forecast correct. Think of it as a runner going for a world record in the mile. If he is five seconds off the pace with 100 yards to go, you may safely conclude that he will not break the record.
  • The concept might be extended to more years. If a very negative forecast is in place and the first year or two is strong, it might take a market crash for the forecast to come true.

Research Tests

This very brief summary is a glimpse of what a solid research design should include.


There should be a hypothesis and a test of the hypothesis.

It should be possible to disprove the conclusion.

Stated results should not consume all of the data.


It is best to share data, especially when not proprietary. This allows others to replicate the work. (One of the top economics books of the past year included a serious spreadsheet error, discovered because data were shared. It is fairly common in academic circles. Dr. Shiller shares his data, despite the great difficult in develop the historical earnings).

It is important to provide a complete description of the methodology. This should include paths not taken and variables that were rejected.

It is helpful to show the link (ideally with an update) of past research theses as more evidence emerges.

My Own Concerns about the Conclusions

Many have asked me why I have not followed this approach in my own investment management. I do not write about it very much because of the work required. Dr. Hussman has a great research budget and team. I have a small staff who are already fully-employed on stock picking and managing our programs. Going back to replicate one of the old charts would be a fair amount of work. I will share my concerns here, but only in abbreviated form.

  • We never seem to reach the point of evaluation. How did these approaches work in the past?
  • The methodology seems to include many of the classic overfitting problems. I am certainly not the first to note this. Philosophical Economics in late 2013 wrote Valuation and Stock Market Returns: Adventures in Curve Fitting.
  • There are adjustments that are not well explained. The earnings are adjusted for expected changes in profit margins, for example. What if this assumption is not accurate? Profit margins are an intense (and separate) debate.
  • The method for adjustment keeps changing – different approaches, coefficients, etc.
  • Over the years, the time frame for the forecast keeps moving, from seven, to ten, and to 12. If you go back to the original Shiller papers, he was using five years. His disciples keep experimenting with different choices.
  • The independent variables change with each new iteration. The overall model always seems to fit. Past discrepancies disappear.
  • The attribution of “bad patches” in results to market overvaluation or undervaluation. This seems backwards. Why is the market wrong and the model right?

I am especially bothered by what I see as exaggeration and distortion. What does it add to this discussion to call valuations “obscene?” I find especially distasteful the statement, “The CAPE Ratio id doing exactly what it has always done, which is to help investors anticipate the investment returns they should expect over the next decade. Those returns will very likely be in the low, single digits”.

The CAPE ratio is not some wise old friend that has been around for centuries. It was invented only recently and has not worked very well. The claim of historical validation is also completely wrong. What if I told you that the Packers always won at home after a double-digit away loss in a dome? (I made this one up, but you get the idea). It is historically accurate, but does not have any value for predicting the future. Since Dr. Shiller and Dr. Hussman made a lot of specific choices about measuring earnings, past time frames, use of inflation information, and future time frames, their conclusions should be described as a model, not some definitive historical record. It is rather easy to create a view of history that provides a vastly different conclusion. (see The Single Greatest Predictor of Future Stock Market Returns). It includes this impressive chart.


[Jeff] Similar approach, vastly different result. This is not the only such example.

Implications for Investors

My most important point is a plea, repeated from last week’s post: Be careful about investing your money using analysis you do not really understand!

Whether you share my concerns or not, I recommend a deeper look into these issues, with one of three conclusions:

  1. If this leads you to agree with Dr. Hussman, his fund offerings that provide the best balance. I have written that his stock picking is excellent. Investing with him is better than going “all out” on your own because of fear.
  2. If the deeper look leads you to disagree, you might consider funds or advisors who take a different approach.
  3. If you are not sure, then hedge your investment “bets.”

Weighing the Week Ahead: Can Markets Finally Celebrate Good News?

The data calendar continues in something of an alternating mode. This week we have a concentration of the important economic releases. We also have daily appearances by Fed members. This provides a daily opportunity for pundits to interpret the news:

Can markets finally celebrate good news?

Prior Theme Recap

In my last WTWA I predicted special attention to housing sector issues in a week without much other data. Instead, the Brussels attacks quickly dominated the news. When there was not much additional information, the stories featured the reactions of one and all. Doug Short notes the three-day losing streak in his excellent weekly chart. (With the ever-increasing effects from foreign markets, you should also add Doug’s World Markets Weekend Update to your reading list).


Doug’s update also provides multi-year context. See his full post for more excellent charts and analysis.

We would all like to know the direction of the market in advance. Good luck with that! Second best is planning what to look for and how to react. That is the purpose of considering possible themes for the week ahead. You can make your own predictions in the comments.

This Week’s Theme

The economic calendar includes all of the most important reports. Fed participants will be out in forces. There will be plenty of fresh news to ponder.

In theory, the avalanche of news could lead to a dramatic market move. In practice, it usually works differently. The economic data are mixed. The Fed speakers disagree. Pundits are free to interpret the evidence through the prism of their predispositions. The difference in these viewpoints leads me to conclude that many will be asking:

Will good news be good for stocks?

And of course, the corollary – will bad economic news get a cushion from expectations of slower fed tightening?


The basic themes are familiar.

  • Good news about the economy is good for stocks;
  • The Fed will react to offset economic news either way – keeping the trading range; or
  • Nothing matters except oil prices.


Your conclusion about how stocks will react is a function of what you believe is driving current market action. We do not get paid for knowing yesterday’s news, but it is important to understand the sources of market reaction.

Suppose at the start of last week, people could go “back to the future” and know about the Brussels attacks. What do you suppose would have been their market forecast? In actuality, when everyone knew the answer, we heard many explanations that events like this were now accepted as normal risks. I do not like the very idea that such events are “normal.” I understand the theoretical concept that the market significance is small. With that in mind, my point is how much easier it is to make statements like this after the fact.

Let’s try next week instead. Suppose the market has a significant rally. Many will say that it was end-of-quarter window dressing. But we all know the quarter is ending. If you expect a window-dressing rally, say it now – not as some know-it-all explanation next weekend. If the market declines, I suppose it will be called “profit taking.”

As always, I have my own opinion in the conclusion. But first, let us do our regular update of the last week’s news and data. Readers, especially those new to this series, will benefit from reading the background information.

Last Week’s Data

Each week I break down events into good and bad. Often there is an “ugly” and on rare occasion something really good. My working definition of “good” has two components:

  1. The news is market-friendly. Our personal policy preferences are not relevant for this test. And especially – no politics.
  2. It is better than expectations.

The Good

There was some good news in a light week for data.

dshort GDP

  • Market liquidity is much better than people think. (Matt Turner at BI).
  • Initial jobless claims of 265K remained very low. (Scott Grannis)

Weekly Claims 4-wk avg

  • Sentiment is still not bullish, despite the recent rally. This is a positive on a contrarian basis. Bespoke has the story.


  • Trucking tonnage is strong. Dr. Ed reviews the rebound in several recent economic indicators, including trucking.

Yardeni Trucking

  • New home sales increased at a seasonally adjusted annual rate of 512K. This was slightly better than expectations, and has more economic significance than existing sales. Calculated Risk once again notes the “distressing gap” between existing and new sales. The two series tracked closely until the housing bubble and bust. Bill observes that the gap is narrowing and expects the trend to continue.


The Bad

Some of the news was negative.

  • Durable goods orders declined by 2.8%,
  • Existing home sales declined 7.1% month-over-month with a seasonally adjusted annual rate of 5.08 million. Calculated Risk cites low inventory and stress in oil patch regions as contributing factors. The chart below shows the changes in months of supply.


Truth or Fake?

We know that truth can be stranger than fiction. Many probably know the real answers to these questions, but please play along.

  1. A major company unleashes a tweeting robot. It swiftly becomes offensive and bigoted. (Does that mean that it passed or failed the Turing test?) The FT’s Izabella Kaminska has an imaginative and interesting take – a Trading Places-style bet?
  2. Petitioners demand open carry of firearms at the Republican National Convention. (Akron Beacon Journal) The fact of the petition is known. The source and motive is not – at least in theory. Readers and clients who are Second Amendment fans please not that I am raising a point about media coverage. You may decide for yourself on the merits of the petition!
  3. The research team at a major mutual fund is on a mission to create self-serving results. This is one of my occasional attempts at humor. Those who read it joined Mrs. OldProf in a laugh. Maybe you will, too. She also liked “The Rookie” who showed both knowledge and integrity. Maybe I’ll bring the character back.

The Silver Bullet

I occasionally give the Silver Bullet award to someone who takes up an unpopular or thankless cause, doing the real work to demonstrate the facts.  Think of The Lone Ranger. Often the winner has done a single refutation of a specific post. Sometimes that is not enough to make the point. No single statement has enough substance to disprove! To appreciate Jacob Wolinsky’s effort you really need to read the entire article. The subject is Harry Dent, who provides the chart below — and a product to save you from the result!


Quant Corner

Whether a trader or an investor, you need to understand risk. I monitor many quantitative reports and highlight the best methods in this weekly update. Beginning last week I made some changes in our regular table, separating three different ways of considering risk. For valuation I report the equity risk premium. This is the difference between what we expect stocks to earn in the next twelve months and the return from the ten-year Treasury note. I have found this approach to be an effective method for measuring market perception of stock risk. This is now easier to monitor because of the excellent work of Brian Gilmartin, whose analysis of the Thomson-Reuters data is our principal source for forward earnings.

Our economic risk indicators have not changed.

In our monitoring of market technical risk, I am now using our new model, “Holmes”. Holmes is a friendly watchdog in the same tradition as Oscar and Felix, but with a stronger emphasis on asset protection. We have found that the overall market indication is very helpful for those investing or trading individual stocks. The score ranges from 1 to 5, with 5 representing a high warning level. The 2-4 range is acceptable for stock trading, with various levels of caution.

The new approach improves trading results by taking some profits during good times and getting out of the market when technical risk is high. This is not market timing as we normally think of it. It is not an effort to pick tops and bottoms and it does not go short.

Interested readers can get the program description as part of our new package of free reports, including information on risk control and value investing. (Send requests to info at newarc dot com).

In my continuing effort to provide an effective investor summary of the most important economic data I have added Georg Vrba’s Business Cycle Index, which we have frequently cited in this space. In contrast to the ECRI “black box” approach, Georg provides a full description of the model and the components.

For more information on each source, check here.

Recent Expert Commentary on Recession Odds and Market Trends

Bob Dieli does a monthly update (subscription required) after the employment report and also a monthly overview analysis. He follows many concurrent indicators to supplement our featured “C Score.”

Georg Vrba: provides an array of interesting systems. Check out his site for the full story. We especially like his unemployment rate recession indicator, confirming that there is no recession signal. He gets a similar result with the twenty-week forward look from the Business Cycle Indicator, updated weekly and now part of our featured indicators.

Doug Short: Provides an array of important economic updates including the best charts around. One of these is monitoring the ECRI’s business cycle analysis, as his associate Jill Mislinski does in this week’s update. His Big Four update is the single best visual update of the indicators used in official recession dating. You can see each element and the aggregate, along with a table of the data. The full article is loaded with charts and analysis.

RecessionAlert: A variety of strong quantitative indicators for both economic and market analysis. While we feature the recession analysis, Dwaine also has a number of interesting systems. These include approaches helpful in both economic and market timing. He has been very accurate in helping people to stay on the right side of the market.

The Week Ahead

We have a huge week for economic data. While I highlight the most important items, you can get an excellent comprehensive listing at Investing.com. You can filter for country, type of report, and other factors.

The “A List” includes the following:

  • Employment report (F). Despite wide error band and revisions, still most important.
  • ISM index (F). Good for overall economy as well – some leading quality.
  • Consumer confidence (T). Conference Board version good for employment and spending.
  • Michigan sentiment (F). Same as Conference Board, but uses the “panel” approach.
  • Auto sales (F). One of the most important elements of the recovery – private data.
  • ADP private employment (W). Strong independent measure of employment.
  • Personal income and spending (M). One of the most important indicators.
  • Initial Claims (Th). The best concurrent news on employment trends.

The “B List” includes the following:

  • PCE price (M). The Fed’s favorite inflation indicator deserves attention.
  • Construction spending (F). Volatile February data, but important.
  • Pending home sales (M). Less important than new construction, but worth watching.
  • Chicago PMI (Th). One of two regional measures worth watching.
  • Crude oil inventories (W). Attracting a lot more attention these days.

There is an abundance of FedSpeak! And just when so many think that so much transparency and multiple voices are a problem. Personally, I find it helpful to look at individual positions, just as we would with other democratic institutions that vote, but many seem to prefer less information.

How to Use the Weekly Data Updates

In the WTWA series I try to share what I am thinking as I prepare for the coming week. I write each post as if I were speaking directly to one of my clients. Each client is different, so I have six different programs ranging from very conservative bond ladders to very aggressive trading programs. It is not a “one size fits all” approach.

To get the maximum benefit from my updates you need to have a self-assessment of your objectives. Are you most interested in preserving wealth? Or like most of us, do you still need to create wealth? How much risk is right for your temperament and circumstances?

WTWA often suggests a different course of action depending upon your objectives and time frames.

Insight for Traders

We continue both the neutral market forecast, and the bearish lean. Felix is still 100% invested, catching much of the rebound. The more cautious Holmes avoided the downdraft, and has increased overall positions to 25% invested. One of these was a lucky (?) call in Pepco Holdings (POM) two days before the surprise closure of the merger. For more information about Felix, I have posted a further description — Meet Felix and Oscar. You can sign up for Felix and Oscar’s weekly ratings updates via email to etf at newarc dot com. They appear almost every day at Scutify (follow here). I am trying to figure out a method to share some additional updates from Holmes, our new portfolio watchdog. (You learn more about Holmes by writing to info at newarc dot com.

Not using Fibonacci ratios? Really?? Adam H. Grimes explains his conclusion and invites traders to join the debate.

Doug Short occasionally highlights the “best stock market indicator” from John Carlucci. The current conclusion is an untradeable market. Holmes nodded and barked when he heard this.

Insight for Investors

I review the themes here each week and refresh when needed. For investors, as we would expect, the key ideas may stay on the list longer than the updates for traders. Major market declines occur after business cycle peaks, sparked by severely declining earnings. Our methods are focused on limiting this risk. Start with our Tips for Individual Investors and follow the links.

We also have a page (recently updated) summarizing many of the current investor fears. If you read something scary, this is a good place to do some fact checking. Pick a topic and give it a try.

Many individual investors will also appreciate our two new free reports on Managing Risk and Value Investing. (Write to info at newarc dot com).

Other Advice

Here is our collection of great investor advice for this week.

If I had to pick a single most important source for investors to read, it would be this thoughtfully-researched piece from Urban Carmel. He begins with this quotation and comment:

The US economy is stuck in one of the most sluggish recoveries in history. Growth is just 2% and it will remain slow as consumers and companies work off vast amounts of debt. The country has gotten off track and neither political party has any answers.

These sentiments were written in Time in 1992, the year one of the biggest growth eras in American history began. But these same words are often used to describe the current economic environment.

The rest of the article is a delightful compilation of past quotes that seem to fit the current era. It is worth a careful read, and you will find it amusing.

Stock Ideas

Under Armour (UA) illustrates the power of celebrity endorsements. (Jeff Reeves) Upside potential?

The newest academic studies show that dividend growth is predictable. It takes a combination of factors – not just one.

Chuck Carnevale explains why these dividends are important. As he always does, he combines theory, data, and specific ideas.


Energy Prices

Oil rebound? Dan Dicker at Oil & Energy Insider (subscription required) has ideas about how best to play a rebound. He likes Exxon-Mobil (XOM) as a buyer of a shale player and Blackstone (BX) because of their independent power to buy key assets.

Here is an interesting chart from their free edition:



Watch out for….

Bonds. The Personal Finance Engineer analyzes different asset allocations, testing the value of bonds as a way of reducing portfolio volatility. The answer depends a lot on the Fed’s actions.

My sense is that investors can do better.


Personal Finance

Professional investors and traders have been making Abnormal Returns a daily stop for over ten years. The average investor should make time (even if not able to read AR every day as I do) for a weekly trip on Wednesday. Tadas always has first-rate links for investors in this special edition. There are several great posts, but I especially liked this WSJ article on business development companies (BDC’s). A few years ago this was a popular method for getting additional yield by lending to businesses that did not qualify for regular bank loans. Problems are starting to emerge, and people are bailing out of these funds. There is probably a lesson there.

Market Outlook

Tom Lee, one of the most successful strategists in recent years, notes signs that value stocks and small caps are showing good relative strength since the market lows in February. He believes that the prevailing conventional wisdom of dollar strengthening may be incorrect.


Brian Gilmartin has similar comments about the dollar, suggesting that Q116 may be the trough in the earnings decline.

BlackRock has also turned bullish on U.S. equities.

(Those wishing to explore this idea further can get my free report on why 2016 can be the year of the value investor. Request via info at newarc dot com. We never use your email address for any other purpose).

Final Thoughts

There is continuing tension among the various market viewpoints. It is both too simple and also unhelpful to turn it all into a game of labels.

Investors must have a fundamental method and stick with it. I track the economy (and especially recession potential) because economic growth drives stronger earnings and higher stock prices. Much of the daily news flow is simply noise, distorted further by the simple mental models used by most participants. The three biggest current mistakes are the following:

  1. Making it all about oil. This viewpoint is sufficiently prevalent that it has created excessive skepticism about economic growth and recession potential.
  2. Making it all about the Fed. It is fun for most to criticize Fed policy, but not very useful. Most of the actual predictions (hyperinflation, market collapse after the end of QE) have not occurred.
  3. Making it all about valuation. The most popular methods of market valuation help to keep the average investor scared witless (TM OldProf Euphemism).

Traders have a more difficult challenge. They must guess which of the prevailing, if erroneous, mindsets will dominate on a given day. Good luck with that!

You Do Not Get Paid for Knowing Yesterday’s News!

You do not get paid for knowing yesterday’s news… unless you work as a pundit!  In that case you just need to go on TV and repeat what you read that morning in the Wall Street Journal or the FT.  Like the “B” student in a class, you learn the conventional wisdom and repeat it.  You can sound very confident — even smug — and seem right because you are describing the past.

For traders and investors, yesterday’s news is history — already reflected in market prices.  Unlike other aspects of life, being well-informed provides you no edge. It might even be a disadvantage.  The post-hoc explanations for market moves twist theory to fit perceptions.  As humans, we crave to make sense of everything; we are very creative in finding explanations.  This may build a view of the world that is quite wrong.

Finding an investing or trading edge requires an accurate view of the future, not the past.  You can do this in several ways:

  • Better information — possession of facts not widely known;
  • Speed — getting news faster and drawing the right conclusions;
  • Interpretation of data — understanding and using an indicator or technique that is not widely followed;
  • Contrarian investing
    • Determine the conventional wisdom
    • Find important mistakes in the popular, oft-repeated viewpoints
    • Consider what sectors and stocks would benefit if there is a return to reality


If you start asking yourself the right questions, following the points listed above, you will find some fresh ideas.  Here are a few examples:

Information — There are many important facts that are not widely known.  Worldwide demand for energy has increased every year, more this year than last.  Using energy prices as a gauge for the world economy is too pessimistic.  Bank exposure to energy companies is relatively modest and reserves are much better than in 2008.

If you accept this information, you can shop economically sensitive companies and banks.  This information is hiding in plain sight.

Speed — Good luck with this approach!  You really need to have a plan in advance and jump on breaking news, beating the computer algorithms.

Indicators — The page-view payoff for pessimistic news has inflated the perceived probability of a recession.  Insider buying has been strong in several crucial sectors.  CEO’s generally express confidence about their own business, even when less optimistic about others. The relevant data is easy to find.

Contrarian Analysis — The conventional wisdom has punished biotech because of a political debate about drug prices.  Oil prices are seen as hovering at a permanently depressed level.  Banks are targets for political rhetoric and exposed to bad loans.  Apple is too big and lacks new products.  And more.

Do we really believe that an aging population will not embrace the new drug discoveries?  That China, India, and other countries will not need enough energy to close a 1% gap between supply and demand?  That banks will not escape the political noise with more profit?


I do not expect everyone to agree with the specific trade ideas in this post, but I hope readers will consider the basic approach.

If you want trading or investment profits, think for yourself and think ahead!  Reading the news only helps to know what others are doing.



Finding the Real Expert

Successful investors are modest.  Overconfidence is dangerous.

When I started writing this blog more than ten years ago, I did not think I was an expert on everything.  My investment success had more to do with my ability to recognize the expertise of others.  I have given examples of what I learned from cab drivers as well as from sophisticated model developers.  Nearly everyone has interesting information.  You can often learn just by asking, “How’s business?”

This is in sharp contrast to the behavior of most investors.  It is just human to be impressed by people who make confident predictions of extreme events.  Doing some fact-checking is difficult.  Most people spend more time choosing a refrigerator than picking a stock.

But let us turn to a really serious decision — setting your fantasy football lineup!

[I know from my teaching experience that going to a problem in a different context is a great way to put our biases aside.  Even if you are not a sports fan or fantasy enthusiast, you will understand the point].

The fantasy sports business is popular and profitable.  Players, even those risking only a few dollars, spend many hours researching choices for their weekly lineup.  There is a cottage industry of experts — people who crunch numbers, do podcasts, and sell related services.  Suppose that we wanted to choose the best source from among the following:

  • An articulate newbie who had a new system that identified top players from the past weeks or years.
  • A great-sounding source with football knowledge but no track record.
  • Someone making less spectacular claims, but with a real-time record of reasonable success.

You can probably guess who gets the business.  Let’s turn to investment information.

Eighteen months ago I reported my enthusiasm about a great investment book:

Investors can be better consumers of this information with a little help from two insiders, Josh Brown and Jeff Macke. During my vacation I finished reading their entertaining and informative book — Clash of the Financial Pundits: How the Media Influences Your Investment Decisions for Better or Worse. I plan to do a complete review, but it is especially timely right now.

As you watch or read the news next week, you should realize the pressure on pundits to be bold, dramatic, and confident – even when their forecasts are a bit shaky. The financial incentives range from selling products to building a big reputation. Their analysis of these forces is supported with some compelling evidence from both history and interviews. Reading this book is inoculation against hype, and it is also a lot of fun.

It is now time to put this great advice to use!

Think back to the bogus fantasy advice.

  • We are seeing a rash of “instant experts” on recessions.  Most of them are cherry picking a single variable.  Those with stronger methods do data mining to fit several variables. There are at least a half dozen sources currently preaching doom and gloom.
    • Many of the sources are from “credit desks” writing to their current clients.  They are selling bonds.
    • Some sources are singing an old tune, enjoying their fifteen minutes of fame.
  • None of the confident voices have any record at recession forecasting.  CNBC posts their “street cred” but never shows a track record on this key subject.
  • The most successful recession forecasts get very little visibility. I did a massive search five years ago, inviting nominations. One key source, Bob Dieli, has had the best real-time forecasts for decades.  Other top analysts have analyzed past data with great care to avoid data mining.  I have a helpful resource on recessions here, and update the key information weekly.

The Choice of Experts

A CNBC anchor was conducting a recession discussion among a number of other anchors and one trader.  She noted that most of those forecasting a recession were traders, while economists had a different conclusion.  No one said much, but it was certainly accurate.  There is a divergence between those following commodities and those following economic data.

It is a shame that the best experts on recession forecasting are not getting more publicity — right now, when it really matters for investors.  There is probably no question that is more relevant.


Stock prices for economically-sensitive sectors are, in many cases, already at recession levels.  Oil prices are viewed by many as a sound forecast for the global economy, despite increasing energy demand.  The hot money understands this oil price correlation — both HFT algorithms and human traders.  The average investor infers from the market action that the recession theory is correct.

In the short term, this is the trade.  In the long term, investors should prefer real data and a genuine track record to the bombast of newbies.

And finally, I’m going with Aaron for my fantasy team this weekend!

The Most Common Error in Stock Market Research

There is a very common research mistake. It is pervasive in Wall Street research, even that presented by the big-name firms. My academic friends are not immune, partly because they have their own set of incentives.

My mission in this post is fourfold:

  1. Explain the problem in a way that it can be readily understood;
  2. Show how you can spot it in practice;
  3. Provide a clear example;
  4. Suggest some other applications.

The Problem – Selecting the Right Data

If you take a course in research design, one of the first topics will be determining the right data and sources for your analysis. To help us stay open-minded, I will use one of my favorite approaches – a sports analogy.

Let us suppose that we wanted to predict the total points that would be scored in tonight's basketball game between Wisconsin and (The) Ohio State University. The game is being played as I write this, so the exercise is purely academic. Here are some possible choices for our analysis:

  1. Take the entire history of college basketball and use the average as our forecast.
  2. Use data only from the time since the shot clock was put in place.
  3. Use data only since the three-point basket was introduced.
  4. Use only data from the Big Ten, which might differ from other conferences.
  5. Use data only from Ohio State and Wisconsin, who might have different team tendencies.
  6. Use data only from these teams in the last few years, perhaps reflecting their current personnel.

Note that the data becomes more relevant as it gets more specific. Please also note that there is still plenty of data for our problem, since college teams play 30 or so games each year. Even a few years of data would provide 100 cases.

The Stock Market Comparison

Let us take what we learned in step one and consider how it applies to the stock market. Suppose that we wanted to forecast tomorrow's trading volume at the NYSE. Here are just a few of the major changes in stock trading (readers are invited to add more) since the 1792 agreement signed under a Buttonwood Tree.

  • Stock quotes replaced a ticker tape.
  • Securities regulation to provide information.
  • Competitive commissions.
  • The invention of computers.
  • Options trading.
  • Futures trading and arbitrage.
  • Online trading.
  • New NASDAQ rules and deep pools.
  • Decimalization of stock prices.
  • SOX and regulation FD.
  • High frequency trading.
  • Individual stock circuit breakers.

There are other elements, including the more active role of the Fed, but you get the drift. If you were interested in predicting volume, you probably would not use data that was more than a few years old. Too much has changed.

Even when it comes to more general market analysis I am not interested in what happened in the Taft Administration, the FDR era, or even the Ike years. I do not care much about the Nixon years, or even Jimmy Carter. We at least need to get to the modern era of an active Fed, active stock trading with low commissions, and broader access to data through financial television and computers.

An Example

For the purposes of this post I want to use a very innocent example from two of my favorite sources – both valuable contributors to our understanding of markets and current issues.

Let us first look at this chart from my friend Doug Short:

This is a beautiful chart. It is accurate and provides the most comprehensive history available from any source. Doug notes that the long-term, inflation-adjusted increase in stock prices is an annualized growth of 1.73% and that current values are 48% above this trend.

When I look at Doug's chart my eye does not follow his proposed regression line, mostly because I am totally uninterested in the old data. I imagine a different line, starting with the post-war period – surely more relevant. I also imagine a line beginning in 1982, where the data become even more relevant.

The starting point of 1871 is represented not because it is best, but because that was the earliest year for which Dr. Shiller could generate data. There is not a strong research reason for the choice.

While I was pondering this question and considering developing my own chart, I discovered this presentation from Scott Grannis:

Scott's chart is not inflation-adjusted, but it also does not include dividends. The conclusion is dramatically different, showing that stocks are in the middle of the long-term trend – growing at almost 7% a year plus dividends.

Neither source gives any particular reason for the choice of starting point – and that is my main focus here.

Great analysis begins with choosing the right data. Everyone has heard the expression "Garbage in, garbage out." This is where it starts.

Other Applications

If you understand this problem, you have jumped the first (and most important hurdle) in identifying strong research.

It will help you grasp the mistakes of most recession and business cycle forecasters. They simply do not have enough relevant cases to do a good job of ex-post analysis.

You can see the mistakes of those whose research identifies "bad times to invest." They also do not have enough past cases, so the inferences are unsound.

You can see the shortcomings of leading academics. They get respect for exhaustive and thorough analysis, finding data that others have missed. That is fine for their book reviews. You and I need to apply a higher (different?) standard. The popular book about why "this time is different" book has only a handful of truly relevant cases.

Investment Implication

The world wants "actionable investment advice." Fair enough. I have been acting on the principle described here for several years – with weekly articles to explain.

The basic conclusion is that many of the popular pundits, despite their apparent use of data, have developed inaccurate and over-fit models. It is better to have simple models with more relevant data. These may not seem as impressive at first glance, but prove to be more robust in practice.

More to come on this important theme…..

Business Cycle Forecasting: The First-Rate Results of Robert F. Dieli

This article is probably the most exhaustive and challenging piece I have written.  It was worth the effort because understanding the business cycle is crucial to making great investment decisions.  To get the full benefit, I urge readers to spend some time reading the background links and watching the videos.

I am going to follow up with another piece describing how I use this information for investment decisions.  For now, let us all focus on the method, understanding how and why it has worked so well throughout history.


In May of 2011 I embarked on a search for the best recession forecasting methods.  I had been a long-time fan of the ECRI approach.  They were still very positive on the economy at the time, and my quest was not driven by their conclusions.  I was uncomfortable with the methodology and the lack of transparency.  I had many reader suggestions, and I reviewed them all.  The criteria were stringent — "Jeff's Acid Test."  The easy winner of this competition was Robert F. Dieli's "Mr. Model."  (This article described the competition and the results).

The main conclusion from Bob's work was that there was no imminent recession.  This ran counter to some other well-publicized and popular forecasts.  Some readers complained in the comments that the history of the forecast included some imperfections.  Others disagreed with the methods.  The subject was too difficult for simple responses to these questions.  I promised to follow up in more detail, but I wanted to do so in a convincing fashion.

A Year Later

A year later, some key elements of my rationale should be even more convincing:

  1. Bob was right — once again, as so many times before.  And he did it in real time, not on a back-tested basis.
  2. Imperfections in real-time forecasting are acceptable — even desirable.  When I see a perfect forecast, it always means that the model has been tweaked and changed to fit all of the past data.
  3. Simple is good.  Methods that over-specify the number of variables and numerical trigger points also imply excessive back-fitting and poor predictability.
  4. Theory is important.  The model should make sense.

Most recession forecasting models fail because they emphasize weakness.  This is backwards.  A recession begins at a business cycle peak, something that I explain more carefully here.  A recession starts with excessive strength.  Seen any of that lately?

Dr. Dieli explains this quite clearly in this chart.


Phases of business cycle

Your intuition about the business cycle would be better if you completely forgot the "R" word and took Bob's lead:  Substitute "business cycle peak." 

The key driver of Bob's forecast is what he calls the "Aggregate Spread."  By reviewing results over decades we can see that this method actually provides a warning of about nine months.  The image below describes the composition of the spread, using example data from August.


The most recent aggregate spread is shown below. Just as it did last year, it provides strong evidence that the US economy is not nearing a recession.

Aggregate spread jan 2013

And Now — The Show

Get some popcorn and your favorite beverage and settle back to watch the show.  I recently met with Bob Dieli to discuss economic forecasting and to create some videos.  The result is an eight-part series in which we discuss each of the recessions of the latter 20th Century.  [Thanks to Derek Miller for helping in the production of the videos and producing the key summaries.]

In this first video, Bob and I discuss National Bureau of Economic Research and why their definitions of a recession are important. The nonpartisan NBER looks at both the peaks and troughs of the business cycle to conclude when past recessions have happened, effectively making "autopsies, rather than forecasts" – as Bob says. Therefore, it is important for the Mr. Model to use the same criteria when it forecasts for recessions, providing a clearer picture than other models.




In part two, Bob and I take a close look at the recession of 1957. In doing so, they describe exactly how Mr. Model works. The model signals 9 months ahead that the business cycle will be heading towards a peak or trough when it crosses the 200 basis points (shown as a red line on the chart). 




Bob and I illustrate the ways in which policymakers can and do impact the business cycle and how this interacts with Mr. Model. In the run up to 1960, tightening by the Federal Reserve as well as fiscal cuts by the Eisenhower Administration led to an economic downturn. In 1967, when the Fed again tightened the yield curve, the model signalled a recession. Shortly thereafter the Fed eased up, thereby avoiding a recession. At the end of the day, the NBER never called a recession in '67.




Mr. Model had nearly spotless performance in predicting the recessions of the 1970's. Contrary to popular belief, the 1973 recession had less to do with OPEC and more to do with other government policies that laid the foundation for an economic downturn. 




Mr. Model shows the result of Fed Chairman Volker's monetary policy, which inverted the yield curve and brought the Fed funds rate to 20%. The result was a short 6-month recession, then a short recovery which was stifled by other policies. Interestingly enough, the recovery never took Mr. Model past 200 basis points – meaning a new peak could not have been established for the "second" recession.




After the "double dip" recession of the 80's, the recovery brought the business cycle to record highs. This led to the third-longest period of economic expansion into the summer of 1990. A combination of tightening monetary policy and changing policies regarding the first war in Iraq were both responsible in part for the downturn. In 2000, Mr. Model signaled a recession in an election year – something that was sure to happen regardless of who was elected. However, in both instances the model predicted short and shallow recessions unlike the seriousness of the early 80s. 




In the most recent recession, Mr. Model's results were decidedly different than they had been for any previous recession. The model alerted that the 200 basis point line had been crossed in 2007 but did not decline sharply. This is in part because tightening by the Fed did not effect the yield curve as they had in past events. Quick reactions by the Bush and Obama administrations also helped to prevent a dramatic decline in Mr. Model's basis points.




In this final video, Bob and I focus heavily on the 2007-2009 recession. The model appears to show a false positive as it crosses the 200 basis point line in 2006, but continues sideways for some time before the recession was officially called. In a sense, this suggests severe instability rather than the dramatic declines of the past. In any case, we had ample warning that a recession was coming. It did not take us by surprise.





If you have studied the evidence, you will see that recessions usually involve the Fed!

You might also have noticed that business cycle peaks do not typically come from a problem of "stall speed" but one of excess stimulation.

Market observers are completely mistaken:

  • Wrong indicators;
  • Wrong interpretation (weakeness versus strength);
  • Wrong sources;
  • Wrong point of the business cycle; and finally
  • Wrong stocks.

These will be the subjects of the next installment.

A Bull Market in Bad Predictions

Why does this happen whenever I try to take a few days off?  The market for dubious predictions has geared up in earnest!

While on vacation I was watching the market (but without my customary TIVO), events developed exactly as I predicted.  I warned about signal and noise, the challenge to traders, and the opportunity for long-term investors.

I have also been reading Nate Silver's book, The Signal and the Noise, which includes a lot of wisdom on these topics.  I plan a full review when I finish.

One of Silver's points concerns predictions without any confidence interval.  Many themes will be familiar to readers of "A Dash" since I highlight pundits who claim expertise outside of their "happy zone."  Let us highlight the three worst items from the past week.

  • The fiction –  the ECRI claims that we are now in a recession.  This is ECRI 4.0 after their 2011 forecast failed, their revised 2012 forecast failed, and their complaint about seasonal adjustments being wrong has not proven out.  They are now playing out the last straw, that they are the only ones who can forecast recessions in advance and that no one else knows until after it is over.  This will obviously require a deeper look.  Let me cite the most obvious incorrect statement in their claims: The business cycle has peaked and they are the only ones who know this.

The reality.  No one knows whether the current period will eventually be defined as a recession.  A recession requires a significant decline (which you do not know until you have seen it).  At that point the NBER goes back to the last peak.   The ECRI presentation last week "assumed facts not in evidence."  They are ignoring the reduction in business spending before the election and the fiscal cliff.  They are exploiting the Super storm Sandy effects.  We can expect them to pound the drum even more during the next month, since the weak patch will take a couple of months to sort out.

I have a personal sadness about this, since I like and admire the ECRI principals.  I am going to write another piece about how and why their methods failed.  I wish that they had just been willing to accept the changing evidence — and maybe open the kimono a little bit.

  • The fiction — the decline to zero growth.  GMO's Jeremy Grantham opines that the US economy is on a zero growth path until 2050.  He focuses on the two best drivers of growth — population and productivity.  In this CNBC segment Maria Baritromo breathlessly praises Grantham:

"…He gets paid to make predictions, steve. that's what he's doing. by the way, his former predictions have been right. let's give him that."

The reality.  No one knows what will happen in 2050.  Grantham has ignored a decline in immigration (something that has helped US GDP in the past) to support his perma-bear position.  Pretending to this kind of knowledge gets headlines, but should be a warning signal to investors.  The media commentary points out that he manages a gazillion dollars or so.  Maybe a few of those investors should look to managers who are more grounded in facts.

And by the way, maybe Maria should cite Granthams track record — 47% — before claiming that he has made so many great calls.  Anyone who takes this silly prediction seriously should look back forty years for a comparison.

  • The fiction.  The latest new and greatest recession indicator.  This is from Lance Roberts (who without apology highlighted the bogus 100% recession indicator).  He is now back with a new entry, endorsed by John Hussman.  Roberts takes some existing economic forecasting indicators that do not initially give the result he hopes for.  He then does some arithmetic and creates something that has a lame correlation to past recessions.  Hussman (who does similar things) embraces this approach.

The Reality.  The St. Louis Fed creates about 60,000 data series.  If you do some math transformations as Roberts did, you can turn this into a million or so possibilities.  If you then set a "trigger"  at an arbitrary level based upon a handful of past cases (the way Hussman does)  you can multiply this into the hundreds of millions range.

It is bad research, bad methodology, and a seriously misleading result.  It is impossible to prove, since the bad guys used all of the data.  There is nothing left to prove them wrong.


So much bogus commentary, and so little time.  Can't a guy take a few days off?

I will follow up on all of these themes.  Here are the main ideas:


The ECRI errors will require a more careful review — it is on my agenda.  Meanwhile you can get the basic concept of their mistake by reviewing my recession forecasting page.

Fiscal Cliff

This theme continues with silly trading in the absence of information.

Listen up!!  We have no new information since the election.

We will not know anything new for a few weeks.  Trade at your peril.


It almost seems too obvious.  So many have much at stake in scaring investors.  They have clearly won the battle, with aggressive money flowing into anything with a high yield and conservative money going to farmland and ammunition.  My conversations with investors show that many are scared witless (TM OldProf).

Since the big rewards go to the contrarian investor, there are some great opportunities.

I like CAT as the proxy stock for an economic rebound, although it is (incorrectly) China-centric.

I also like some health care insurers and defense stocks — UNH and LMT as examples — as winners in the fiscal cliff compromise.

AFL is a good play if there is no disaster in Europe.

These are complex questions, so I plan to write more on each issue.




September Employment Report Preview

For many years I have written a regular monthly preview of the Employment
Situation Report.  I have done extensive research on all of the methods
and even visited the stat guys at the BLS to discuss their approach.

My preview gives appropriate respect to the BLS, but also to the leading alternative methods. Why is this so important?  I have created a little story which may be helpful on this front.

A Helpful Bit of Fiction

Suppose we have a contest to guess the number of coffee beans in a jar.  Here is a picture of such a contest in Bluffton, Ohio, just down the road from where I grew up.  Let us take some liberties with the contest rules.  The contest is done every week.  Every contestant gets to know how many beans were in the jar the week before, but the shop is withdrawing and adding beans each day.

2471-icon-and-common-grounds-invites-you-enter-count-coffee-bean-contestLet's consider four different approaches for determining the number of beans.

  1. 1)  Lift the jar to estimate the weight.  Compare the weight with what you think it was last month and adjust your guess.
  1. 2)  Track the amount of coffee served during the week.  Estimate how many beans must be added to replenish the supply.

3)  Count the scoops of beans added.  Use a sampling procedure to estimate the number of beans in a scoop.


4)  Measure the jar.  Determine its volume.  Determine the volume of a bean.  Do the math, sort of like this contestant in a similar contest.

 CalculationsThere is an actual count of the beans, but that report is available only after five days.  (Fast counters are in short supply!)  The contestants want to have a winner declared each week at the barn dance.  To meet this demand, the contest administrators decide to pick one contestant approach and declare that to be the "official" result.  Since the math contestant has a good long-term record, that is the one chosen.

A few days later the actual count is known.  By then no one cares, since the prize has already been awarded.

And that is what we all do each month in trying to squeeze meaning from the employment situation report.  Even though the BLS is just a competing contestant (readers are invited to unmask the others) their estimate is anointed as official.


We rely too much on the monthly employment report.  It is a
natural mistake.  We all want to know whether the economy is improving
and, if so, by how much. Employment is the key metric since it is
fundamental for consumption, corporate profits, tax revenues,
deficit reduction, and financial markets.  Whenever there is an important question, we all seize on any available information.  While we might know the limitations of the data, any concern is briefly acknowledged — if at all — and then swiftly put aside.

The Data

We would like to know the net addition of jobs in the month of September.

To provide an estimate of monthly job changes the BLS has a complex methodology that includes the following steps:

  1. An initial report of a survey of establishments.
    Even if the survey sample was perfect (and we all know that it
    is not) and the response rate was 100% (which it is not) the
    sampling error alone for a 90% confidence interval is +/- 100K
  2. The report is revised to reflect additional responses over the next two months.
  3. There is an adjustment to account for job creation — much maligned and misunderstood by nearly everyone.
  4. The final data are benchmarked against the state employment data
    every year. This usually shows that the overall process was very
    good, but it led to major downward adjustments at the time of the
    recession. More recently, the BLS estimates have been too low, as revealed in the just-released report.  For the year ending in March, 2012, the BLS estimate was off by about 30K jobs per month overall, and 35k jobs per month on private employment.

Competing Estimates

The BLS report is really an initial estimate, not the ultimate
answer.  The BLS is actually like one of the contestants, with the full report coming later.  The market uses this estimate as "official" and declares winners and losers on that basis.  No one pays any attention to the final data, which we do not see for eight months or so.

  • ADP has actual, real-time data from firms
    that use their services. The firms are not completely
    representative of the entire universe, but it is a different and
    interesting source. ADP reports gains
    of 162K private jobs on a seasonally adjusted basis.  In general, the
    ADP results correlate well with the final data from the BLS, but not
    always the initial estimate.  It is an independent measure that deserves
    respect.  The revisions noted above moved the BLS closer to the ADP conclusions over the time period cited.
  • TrimTabs looks at income tax withholding data. The idea is that this is the best current method for determining real job growth. TrimTabs forecasts gains of about 210,000.  TrimTabs thinks that the BLS is wrong.  They write as follows:

"(The BLS) is missing the current acceleration in job growth.  In August, the BLS
reported job growth of only 96,000 new jobs, nearly half of the job
growth TrimTabs reported.  TrimTabs reports that the BLS survey
typically captures employment growth more effectively in government and
large corporations while nearly 84% of the recent employment growth is
occurring in small and medium sized businesses."

  • Economic correlations. Most Wall Street
    economists use a method that employs data from various inputs,
    sometimes including ADP (which I think is cheating — you should
    make an independent estimate).
    • Jeff Method.  I use the four-week moving average of initial
      claims, the ISM manufacturing index, and the University of
      Michigan sentiment index. I do this to embrace both job creation
      (running at over 2.3 million jobs per month) and job destruction
      (running at about 2.1 million jobs per month). In mid-2011 the
      sentiment index started reflecting gas prices and the debt ceiling
      debate rather than broader concerns. When you know there is a
      problem with an input variable, you need to review the model. For
      the moment, the Jeff model is on the sidelines.  The recent uptick in consumer confidence, despite gas prices, the fiscal cliff, and Europe, is encouraging for jobs.  It remains difficult to account for the effect of
      headlines about Europe and the fiscal cliff.  The model inputs are
      improving a bit, but I do not think we have a good grasp on job
    • Street estimates generally follow my method, but few reveal much
      about the specific approach.  These estimates usually adjust for the ADP report, but there was little reaction to the strong estimate for this week.  Everyone cites the "poor" ADP record in matching the BLS.
  • Briefing.com cites the consensus estimate
    as 120K, while their own forecast is for 165K.  Their private jobs
    forecasts are about 10K higher, since the loss of public jobs is
    well known.
  • Gallup sees unemployment as stable  at 8.1% on a seasonally adjusted basis in mid-September, the time of the BLS
    data collection.  This is interesting since they have a different
    survey from the government, a relatively new approach to seasonal
    adjustment, and an extremely bearish and political approach in past
    commentaries.  Gallup's methods deserve respect, so I am watching

Failures of Understanding

There is a list of repeated monthly mistakes by the assembled jobs punditry:

  • Focus on net job creation.  This is the
    most important.  The big story is the teeming stew of job gains and
    losses.  It is never mentioned on employment Friday.  The US economy
    creates over 7 million jobs every quarter.
  • Failure to recognize sampling error.  The
    payroll number has a confidence interval of +/- 105K jobs.  The
    household survey is +/- 450K jobs.  We take small deviations from
    expectations too seriously — far too seriously.
  • False emphasis on "the internals." 
    Pundits pontificate on various sub-categories of the report, assuming
    laser-like accuracy.  In fact, the sampling error (not to mention
    revisions and non-sampling error) in these categories is huge.
  • Negative spin on the BLS methods.  There
    is a routine monthly question about how many payroll jobs were added
    by the BLS birth/death adjustment.  This is a propaganda war that
    seems to have ended years ago with a huge bearish spin.  For anyone
    who really wants to know, the BLS methods have been under-estimating
    new job creation.  This was demonstrated in the latest benchmark
    revisions, which added more jobs.

It would be a refreshing change if your top news sources featured any of these ideas, but don't hold your breath!

And most importantly, it would be helpful if anyone would realize that the BLS is just one estimate.  The bean counter example illustrates this.

Trading Implications

The rules are changing for trading the employment report.  You can still expect the aggressive bearish spinfest that usually provides a "dip to cover," but things are a bit different now.

With the Fed intentions declared, we have left the environment where good news might be bad and bad news might be good — all because the Fed might be more aggressive.

My operating expectation is that good news is now good.  It will not stimulate the Fed to tighten.  Bad news is bad, but if it is bad enough, the Fed might add to the QE3 quantities.

I understand that most traders and pundits are in denial about this (as I explained here), but that just provides a better opportunity for the rest of us.

The sophisticated investor does not complain about government policy.  He includes likely outcomes as part of his investment plan.