A Dash of Insight

Quantifying the Economic Impact of Housing Declines

Everyone wants to know about housing and the economy.  Is the sub-prime lending issue something that will extend to other mortgage classes?  How much might home prices fall?  Will the impacts lead to reduced consumer spending and an even greater economic effect?

Doug Kass, the popular hedge fund manager noted for his skill in short selling, has been one of the leading voices on this topic.  His view is that this is a major economic problem and one that will affect the home prices and portfolios of individual investors. 

Writing today in Street Insight, the valuable premium service of theStreet.com, Doug does a "back-of-the-envelope" calculation of the likely housing effects.  At "A Dash" we are big fans of such an approach.  Trying to think about quantities and actual impacts is essential.  Market participants often have knee-jerk reactions to news, mostly because they cannot estimate the real effect.

To get Doug's entire argument, you need to be a member at theStreet.com, but they might republish it later for the general public.  Since he is such a frequent and popular TV guest, you will also see it soon on CNBC.  I am quoting just one section, the place where he quantifies the likely impact of what we all read about every day:

Quantifying the Impact of Tightening Credit
I would conservatively estimate that about 55% of the subprime borrowers, 25% of the Alt-A borrowers and 15% of the prime mortgage lending borrowers will no longer be able to secure financing for new homes because of tightened conditions. (This will produce about a 25% drop in housing demand). Speculators and investors - who were responsible for nearly 20% of all home purchases in 2004-06 - will also find it more difficult to secure borrowings and it is likely that this buying category will revert back close to their historical demand role of about five percent of all homes. (This will result in another 10%-15% drop in housing demand). Finally, end of economic cycle conditions (lower consumer confidence, slowing economic growth and moderating job growth) should contribute to another 10% drop in housing demand - as it has done historically. In total (adding the above three influences), new home demand should fall off by almost 50% (vs. the rolling 12- month average showing a 17% drop off in 2007) - even before the effect of a market inundated by record foreclosures is considered.

What is Wrong?

This estimate will sound frightening and persuasive to most who hear it.  For students of economics, the problem leaps out.

A statement like "a 25% drop in housing demand" has no economic meaning.

Non-economists speak in terms like Doug.  Someone is either in the housing market or he/she is not.  A home is either on the market or it is not.  Completely lost is the concept that a buyer may not qualify for a loan of one size, but will qualify for a smaller loan.  Homes for sale at one price are withdrawn from the market if the price is lower.

The century-old concept  is shown in the supply-demand curve that one sees the first day of Econ 101.

As price declines, the quantity demanded increases.  As price increases, the quantity supplied increases.  The actual exchange price clears the market at the  quantity where the curves intersect.

When one wishes to describe underlying changes in either supply or demand, these are characterized as "shifts" in the curve.  Reduced demand, for example, shifts the downward-sloping blue curve to the left, meaning that there are fewer sales at a lower price.  Reducing prices shifts the supply curve to the right, increasing the quantity.

Is this News for Doug Kass?

Readers should understand that Doug Kass knows everything we have written here.  He has an MBA from Wharton and he certainly studied economics.  He knows full well that one cannot describe supply and demand as a binary function -- either in or out.  One of the first things you learn in economics is that shifting the curve has a much smaller price effect than one would think at first.  So why is he writing this?

Conclusion

We wish we could provide a good answer about the impact of housing and mortgages on price and quantity, but we cannot.  To do that, one would need some data about the shape of both curves, at least enough to make an estimate.

We can say with confidence, however, that the Doug Kass scenario is extremely unlikely  The relevant curves would need unusual shapes and make massive shifts to have the impact that he forecasts.

Mortgage Availability and Personal Consumption: Round Three

Background

During my vacation, Doug Kass posted again on the topic of housing and personal consumption.  His analysis can be divided into two parts.  Part one "replied" to our analysis and part two elaborated further his reasoning about the connection, the causal model, and what he expected to happen.

Here is what he wrote:

Many, like "Mad Money's" Jim "El Capitan" Cramer, "Kudlow & Company's" Larry Kudlow and others, readily dismiss the potential spending consequences of substantially less capacity in the subprime mortgage lending market and the emerging trend by mainstream originators and lenders to reduce lending in the primary mortgage market and for refinancing cashouts. Indeed, Jim takes the subprime issue one step further, noting that the mortgage house of pain will have a salutary market and economic result, as it will hasten the Federal Reserve's path toward monetary ease. Shockingly (at least to me), many others can't comprehend the link between mortgage availability and consumer spending, claiming that the correlation between the two variables (seen below) is unclear.

Those wishing to see the entire statement can look here and here, but a subscription might be required.  I have quoted the section relevant to our work.

As you can see, "A Dash" is the unnamed "many" in Doug's statement.  Well, we have been in worse company than Kudlow and Cramer!

As purported refutation of our analysis first posted here and with more information here, Doug merely posts the same chart again, with no effort to respond to our reasoning.

There is a serious flaw in Doug's analysis, and it plays upon the perceptions of both traders and individual investors.  The concept of correlation has a measurable statistical basis.  It is not a matter of opinion. It is a fact.  We believe that the chart is an optical illusion.  Here is a familiar example, one that most readers have probably already seen:

You can either use your eyes to tell you which line is longer, or you get a ruler and find out the truth.

Readers looking at the Kass chart see with their eyes a strong correlation.  This is because the key elements of the chart have been pushed together to emphasize two key points.

To our surprise, readers at Calculated Risk commented both on our site and theirs that the areas we described as "no fit" in our chart actually showed a strong correlation.  Take a look again at our "no fit" chart.

Readers saw what they called the "Batman" segment as quite similar.  They also were undisturbed by the periods where the two lines ratcheted along, out of phase.  This is a trick played by the human mind, the most powerful computer.  Our minds take two patterns and look for similarity.  Sometimes that leads us astray.  Correlation means that the movement in one variable corresponds in both direction and magnitude to the other variable--and at the same time.  The fact that the two lines both have "Batman" characteristics is unhelpful to the trader unless the movements directly correspond.

Since this is difficult to see visually, it is important to use statistical techniques to measure correlation.

An Aside

To put this in the proper perspective, we should remind readers of our mission at "A Dash."  We review and analyze Wall Street Research where we find interesting errors.  We have no shortage of candidates.  We do not have a mission linked to a particular market viewpoint.  We choose our market stance based upon the evidence we see.

In the Doug Kass case there is a proposition that historical data show a strong correlation suggesting that personal consumption expenditure declines are imminent.  Using traditional statistical methods, we find that the data cited do not support this conclusion.  It does not mean that Doug is wrong in his ultimate conclusion -- just that his frequently-cited chart does not support his viewpoint.

In the areas of the chart that we cited as "no fit" the correlation is much lower or non-existent.  The entire strength of the chart depends upon a few data points.  If readers do not see this, then we have failed in our mission to educate about the danger of visual chart interpretation.

Going Back to the Classroom

There will always be some who make up their minds first and then see everything as evidence.  We cannot reach them.  It was surprising to us how many readers insisted on the correlation, based upon this chart.  What were we seeing that they were not?  To improve on our analysis in the first two posts on this topic, let us turn back to the yellowed notes of classroom instruction from decades ago.  To see why this is not a  strong or useful correlation, one must start by understanding what that means.

The concept of correlation is based upon a linear relationship between two variables.  With modern software, it is easy for anyone with access to the underlying data to calculate the correlation and statistical significance.  But that is not enough!

The concepts are from an undergraduate introductory statistics course where correlation and regression are two of many topics.

We present here four different data sets, all showing the same statistical characteristics -- averages, standard deviations, slope coefficients, etc.  The dramatic demonstration for students was that it was not enough to look at the numbers, one also had to consider a scatterplot of the data.  If the data did not fit a linear model, the entire concept of correlation was called into question.

In only the first case is the regression equation a good specification of the model -- a way of saying that the description and correlation really reflect the data.  In the case where the relationship is curvilinear, this tells the researcher that there is a missing independent variable.  The bottom two cases show a very strong relationship that is distorted by the presence of a single outlier, an unusual case deserving further study.  It is quite obvious that the regression equation is not a good description of the data in the bottom cases.

In real life, examples are never as clear as the pure cases from the classroom, but the Doug Kass chart comes pretty close.  We showed, in the chart repeated below, the scatterplot for the hypothesized relationship. 

When the optical illusion of the time series is stripped away, the reality is clear.  This is mostly a cloud of data with a few outliers that have a strong effect on the equation.  It is not really a good candidate for a linear model, and that means that the term "correlation" is meaningless.  (Those who really want to understand this should also read the prior two articles on the topic.)

The Real Conclusion

The data show that for a few quarters in the early 90's a decline of over 15% in mortgage availability occurred at the same time as a decline of about one percent in personal consumption expenditures.  There was a recession that probably caused both effects.  The data include no other examples of big declines, and the smaller moves are all part of a data cloud.

If someone told you this in words, you would not find it very persuasive.  Somehow the chart has an overpowering visual effect.  Do not bet real money on what you think you see.

Mortgage Availability and Personal Consumption -- An Update

Calculated Risk, a site on our regular reading list, has a story today discussing the same chart we analyzed yesterday.  (Thanks to reader "RB" for pointing this out).  Here is their conclusion:

We can be 99% confident that the YoY changes in real PCE are positively correlated with loosening mortgage lending standards.

Some observers are interpreting this statement to mean there is a .99 correlation -- a very different thing.  (Perhaps this is also what Doug Kass meant in his comment on CNBC.)

The data presented have a correlation of .43,  evidence of some relationship but far from a .99 correlation.  In a .99 correlation, the scatter plot shows every point in a straight line.  This is far different from the plot we showed yesterday.

This has a few outliers that define the relationship and a data cloud.  Any good analyst looks at a scatter plot when doing regression and correlation.  Looking at the residuals is the only way to know if a linear model is appropriate.  In this case, the warning flags should go up.

A correlation of .43 means an r-squared of .19.  The statistical interpretation of this is that about 19% of the variation in one series, as defined by the squared deviations from the regression line, is "explained" by the variation in the other series.  This is how one looks at substantive significance -- whether the relatinoship is important.

So where does the .99 come in?  A linear regression analysis calculates a slope coefficient and an intercept.  In this case, the slope coefficient for the equation is .10, as we noted on our chart.  This means that every 1% change in the mortgage availability measure is associated with a one/tenth point change in year-over-year PCE.  The slope coefficient has a standard error, calculated from the number of cases and the degrees of freedom.  The standard error for the slope coefficient is .02.

Since the coefficient is much larger than the standard error, we can be 99% sure (making some other assumptions about the typicality of the data we have) that the  "true" slope coefficient is not zero.    This is a test of statistical significance.

The confusion of statistical and substantive significance, and which measures are used for each, is one of the most common mistakes made by those without a strong background in research reseasrch methods.

To summarize, the Calculated Risk statement that we can be 99% sure of some relationship between the two variables is correct.

Everything in our article yesterday is also correct.  The degree of association is not as strong as the misleading graph suggests.  The entire relationship rests upon something that happened for a year or so in the early nineties.  The measure of mortgage availability is not very good for the purpose.  There is not enough data.  The resulting relationship is probably spurious.

As someone who taught these classes at the graduate level, I have no illusion that the average reader is going to appreciate these distinctions, however important.  It is a good illustration of how easy it is to be fooled by the eyes, and how difficult it can be to reach the truth.

Mortgage Availability and Personal Consumption: Some Dubious Evidence from Doug Kass

Doug Kass is on a mission!  He has posted the same chart four times in the last ten days and mentioned it even more often.  He has also cited it on CNBC appearances and probably other places.  Here is the key point, drawn from his column today (subscription required), entitled "The Worst Is Yet to Come: A Second Downleg in Housing":

I have tried to document the clear relationship of mortgage availability to personal consumption expenditures, which occurs in every cycle (up and down). You simply can't deny this relationship.

He sees the following chart as powerful evidence of some forthcoming bad news.  [click on all charts to enlarge.]

At "A Dash" we shall take the other side of Doug's mission -- since he is so powerful, let's call it Mission Impossible.  We are going to show that the evidence he presents should not persuade one of his conclusion.

Some Background

Doug Kass is the highly influential writer of The Edge on Street Insight, theStreet.com's useful product aimed at market professionals.  Doug also gets plenty of exposure on CNBC, in Barron's, and other places.  He modestly states that this is because there are so few bears left.  In fact, he has an appealing delivery both on television and in writing.  He is not just another loud, arguing voice and is quite persuasive.  Personally, I read everything he writes, but I use his information carefully.  Somehow he has been successful in managing his fund while being on the wrong side with his economic and market forecasts for several years.  How does he do it?  He does a lot of trading against his overall short position, makes some great calls, and takes profits on shorts when he is right.

The Chart -- A First Impression

When one wishes to make a convincing argument, adjusting the scales of a chart to fit the most visible features makes a powerful impression.  The Kass chart is scaled to emphasize the two "V" troughs and the current plunge in mortgage availability.  Whoever did this chart was familiar with Tufte's classic work (featured in our reading list).

An Objective Look

An expert view of the Kass chart would include looking for areas of no fit as well as the "V" troughs and also choosing a scale level that reflected the overall patterns, without undue emphasis on any feature.  With the supporting help of Renae, we found the underlying data and produced the alternative views.
This is exactly the same chart with a different visual emphasis.  The emphasized areas of "no fit" show that there is often no "relationship".

Another approach is to allow the Excel defaults to choose the scales, instead of trying to emphasize the two troughs.  Here is what one finds.
While the general shapes are similar, the discrepancies are very obvious.

Regression and Correlation
In his Kudlow appearance, Doug claimed a very high correlation for this relationship.  (My TIVO window scrolled off before I could replay it, but it sounded like he said .9).  Let us examine the regression equation, the correlation, and the corresponding scatter plot.
The overall regression equation has an r-squared of .19, meaning that one variable "explains" about 20% of the variation of the other.  Nearly all of the explanatory power comes from the handful of data points on the left -- the outliers.  Without these points there is little relationship -- a data cloud.  This suggests the need for examining the influential cases.

Looking at the Data

At "A Dash" we have maintained the importance of considering the variables in a relationship.  We insist on asking what is being measured, how good the measures are, and specifying a causal model.  We also have argued that it is important to select the right time frames for analysis, seeking enough similar cases from history, while not reaching back too far.  Let us apply these tests to the Kass bivariate relationship hypothesis.

The alleged dependent variable is personal consumption, taken from the Department of Commerce.  The causal variable is availability of mortgage credit, taken from a survey conducted by the Fed.

The Fed survey included 55 banks, so a shift of ten banks to "somewhat tightened" created the 18% decline showed in the chart.  No bank officer reported a "substantial tightening".  Moreover, it does not indicate whether standards were already tight or loose -- something that relates to size of mortgages as well as applicant qualifications.  Here is the key part of the question asked:

If your bank's credit   standards have not changed over the relevant period, please report   them as unchanged even if the standards are either restrictive or   accommodative relative to longer-term norms. If your bank's credit   standards have tightened or eased over the relevant period, please   so report them regardless of how they stand relative to longer-term   norms.

The result is that we know little about whether the current standards are high, medium, or low by historic norms.  We know that ten banks somewhat tightened mortgage standards, none substantially, and forty-five did not.

The Time Period

Kass asserts a relationship that occurs in "every cycle".  The problem is that his data reports only one "cycle".  The reason is that the Fed survey only goes back to 1990, so this is all of the available data.  We do not know about any history before 1990.

Instead of a powerful chart showing many cycles, the proposition is much simpler.  In the early nineties banks became somewhat more restrictive in mortgage lending at the same time personal consumption was declining.  The question confronting those examining the data is whether the current situation is similar to exactly one prior case.  That is all we have.

The Causal Model

When asserting causation, it is important to specify the causal model and to explain the relationship.  We are mystified by Doug Kass's assertion.  If a bank is more reluctant to grant a fresh mortgage, how does that affect consumption?  The potential buyer remains a renter, or continues to live in an existing home.

If the argument is that refinancing is more difficult, we have a different problem.  There are no data on that subject.  It also does not speak to existing lines of credit, or how those might be used.

Most importantly, the mortgage variable does not lead the consumption variable.  The Kass chart shows a simultaneous change.  As we have pointed out in a typical example, this is often the case when there is a spurious relationship.  Simply put, this means that some third (unspecified) variable is the underlying cause of two different effects.  If one merely looks at the effects, there is some apparent correlation.  In 1990, a weakening economy probably caused both effects.

Briefly put, the relationship in the one relevant prior case (a few points in the early nineties) shows two variables that are a result of something else.

Conclusion

The current mortgage market is much different from that of 1990.  Nearly everyone -- especially Doug Kass -- believes that recent standards have become quite loose.  Some tightening is a logical reaction.  There is nothing in the data he presents that shows that this should result in a decline in consumer spending.

Such a decline might occur, of course.  There may be other reasons to link housing and the economy.  That is beyond the scope of Doug Kass's argument and this response.  The point here is that the data in the original Kass chart do not support his conclusion.

CNBC Story on Market Correction: A Good Example and a Big Error

This afternoon CNBC's Street Talk featured two analysts discussing the prospects of a market correction.  The topic is both timely and important.  The participants were analysts using two different methodologies.  At "A Dash" this is the kind of segment that has us getting some popcorn and pulling up our chairs.

Hosting the segment was Erin Burnett, whose star is rapidly rising at CNBC.  Her strong educational and work background helps her to be a good interviewer.

What Happened

The first to speak of her two guests was Mark Arbeter, chief technical strategist at S&P.  He explained that he had developed a system to predict market corrections.  He looks for Nasdaq volume 40% higher than the NYSE over a three-week period.  It has provided signals in 2004, 2005, and 2006.

The second guest was Paul Hickey, an analyst at Birinyi Associates.  While acknowledging that there was always the chance of a correction, he put the odds at no greater or worse than at any other time.  Hickey noted that conclusions about the current bull market were often erroneous because the increase in stock prices has been far more gentle than in past cycles.

Erin gave Arbeter a chance to reply.  He acknowledged that he was bullish on the year as a whole, but thought there were be a better opportunity to buy in March or April.

So far so good, but now Erin slipped in one last question to make things more interesting.  She asked if Mark saw a chance, not of a correction of eight percent or so, but the possibility of a crash like that of 1987 (where there was a one-day decline of 23%).  She asked this question (we suppose) partly because experts on corrections might know about crashes.  It might also have been because Jim Paulsen of Wells Capital Management, a frequent CNBC guest, has recently been suggesting this possibility. [link corrected 2/7  JM]

Two Bad Things Happen

The first bad thing to happen was that Mark Arbeter answered the question without offering any warning to the viewer.  He responded that if the market did not correct as he was predicting, it might lead to something worse later in the year.

The problem is that Mark Arbeter had exceeded his expert status, something we have warned about.  Even if one accepts his system for predicting corrections (more on that in a minute), he has no evidence that the lack of a correction will lead to a crash.  He should have said, "That is Not My Job."  It must be difficult to say that when Erin asks one more question.

Anyone following Arbeter's advice must first wait out his prediction --70% chance of a correction by April.  Then, if he was wrong, the investor should be even more afraid of a big decline.  And this is from an analyst who says he is bullish on the year!

The second bad thing is the result of the new CNBC web site.  We are enjoying the ability to go back and replay video segments that we missed.  It is also handy to have a summary of the segment in text form, a speedier way to get the message.  If the story looks interesting, the video is there for more detail.  The problem is that the text segment (at least at the time of this writing) has a big mistake.  It reports as follows:

U.S. Treasury Secretary Henry Paulson has said that he’s expecting a crash on the scale seen in October 1987. Arbeter does see potential for that to happen, which is why he’s hoping the market corrects now rather than building the entire year and then making a major adjustment in the third or fourth quarters.

“I would be worried if we didn’t get the correction now, early in the year,” Arbeter says.

This had us checking the video, to see if we had missed some big story.  Erin clearly says "Jim Paulsen" not "Hank Paulson".  If the former Goldman CEO and current Treasury Secretary predicted a market crash, it would be big news.  There is no obvious way to notify CNBC of this error, or we would do so.  Meanwhile, anyone reading the summary could make a big financial mistake.

And Finally, the Debate

It is difficult to evaluate a system for predicting corrections without looking at the data, but Arbeter's system sounds a number of warning bells.

• He describes his three recent accurate forecasts.  We like to see a much longer time period.  Surely there are some "false positives" as well as accurate calls if one looks further back.  The on-screen graphic said "seventy percent."
• How has he tested the parameters?  For example, what is defined as a correction?  How did he arrive at 40% as the Nasdaq/NYSE volume difference.  What if it were only 37%?
• How does he determine the length of time before a correction will occur?

Our experience with such systems is that by playing around with the values for a few variables it is easily possible to find something that seems to explain some past corrections.  A system developed in that way has questionable predictive power.

One very obvious point is that tech stocks have lagged the market during the current rally.  Would it be so surprising to see those sectors catch up for a bit?  Wouldn't Nasdaq volume gain if that happened?  What if the Vista launch does stimulate a new equipment buying cycle?

By contrast, we find Paul Hickey's conclusions to be quite sound.  There is certainly a chance for a correction, but our research shows that market bottoms are easier to identify than tops.

In particular, we recommend that our readers pay close attention to Hickey's careful job of distinguishing this bull market from past cycles, something we have also tried to demonstrate.

Conclusion

How stories are communicated may prove to be just as important as the story itself.  MSM web sites do not offer the same potential for instant correction as there is for an investment blog.  Maybe the medium is the message.

Typical Research Mistakes

A noted quantitative analyst from a major firm made a series of predictions over the course of some months using the decline in the ISM survey as a sign of economic collapse.  Taking note of some methodological problems with the work, I watched with interest.  What would happen when the ISM survey showed an uptick?  Would the analyst change his predictions?

What actually happened is typical of Wall Street research.  While I have carefully studied all of the reports, I cannot cite chapter and verse from this proprietary research.  Besides, it could be almost anyone guilty of these two serious errors.

First, I was not surprised to see that his predictions remained the same.  He just changed the model so that it would make the same forecast!  His readers now were to use the LEI as the true leading indicator not the ISM.  Many quantitative analysts "refine" their models to fit what has happened in the last couple of years.  This frequent backfitting of models seems to make them look smart, whether they have been right or wrong.  But it does not work.  Models should help to identify deviant situations, not constantly change to fit the mood of the moment.  Genuine backtesting would reveal the weakness of this approach.

Second, there is a major problem in using the LEI series for techniques like multiple regression. Those watching last week's release of the LEI may have noted that the Conference Board announced a small miss for October while making a significant upward revision for September.  The Conference Board maintains that these indicators have a true "leading" quality.  Maybe so, maybe not.  Anyone developing a regression  model should use the numbers first reported, not the eventual revisions.  Since the researchers did not state that they did so, I suspect that they just used the posted historic series.

The LEI uses ten different indicators.  Not all of these indicators are in final form when the first report comes out.  So be it.  The Conference Board is doing its best, just as the BLS and other government agencies do their best in reporting and revising data.

We do not always have the data we need at the time we want.  Pretending otherwise just leads us astray.  Our team has found that the most common error in model-building is a failure to specify correctly what data were actually available at the time the forecast must be made.

How Good Are Economic Forecasters?

Economic forecasters make an easy target!  They seem dull and boring, their work is complicated, and they interfere with the preferred method of the punditry:  Speculation and anecdotes.

If forecasters are no good, we are all free to ignore them and their data.  Each person's idea is as good as anyone else's in the data-free world.  Thanks to Crossing Wall Street for covering a research note by Martin Fridson dealing with this interesting question.

Fridson, is a prolific and respected financial author, with strong educational and professional credentials.  His writing on this subject will get a lot of attention, no doubt.  The media and pundits will love it, since it tells them what they want to hear.

Our conclusions?

1. We are confident that the evidence reported does not support the principal conclusion, that the market is a better forecaster than the economists.
2. We suspect that Mr. Fridson has never done any quantitative forecasting.
3. We believe that investors who follow his advice will find their choice an expensive one.
4. We recommend that investors ignore his little research note and instead read one of Mr. Fridson's several other fine, instructive, and entertaining books emphasizing market history.

For the rationale ....read on.

Wall Street Research and What is Normal?

One of our themes at "A Dash" is the woeful state of Wall Street Research.  (Readers new to our site should follow the links for full evidence.)  There are several problems:

• Reseachers must look smart and get on TV.  That means that they must "explain" the current market.  Sometimes what has recently happened has no good explanation!  It might be the frequent over-reaction to some anecdotal data point, pounced upon by the ever-growing population of young hedge fund managers.  It might be that the market is missing the big picture, while focusing on local efficiency, assuming that today's price is a true valuation for a stock or the market.  This is exactly what happend in 1999-2000 where the leading Street gurus told us that there was New Paradigm.  Earnings didn't matter.  If you were not "explaining" what was happening in those days, you were out of touch.
• I follow the quantitative work from several bulge bracket firms.  The top strategists have all changed their models over the last three years -- sometimes multiple times -- to adjust to the "new circumstances."  They think that it's different this time, just as it was in 1999.
• There is no peer review.  Street research is a club.  Researchers are always on perilous ground, since their work cannot be directly related to corporate trading and profits.  If they seem out of touch, they are "history."  Since researchers never know when they will be looking for work, it is best not to be too critical of others, even if the resesarch is suspect.
• There is no real science, which depends upon falsifiability.  If a theory cannot be challenged or disproven, it cannot really contribute to a body of science.  Anyone trained in social science research, as we were in the Old Days, understands this.
• There is not time for review, since researchers must analyze each twist and turn of the market, "explaining" everything.  They publish on weekly schedules.  No one can have meaningful insights that rapidly.

The result of this is an ascientific approach heavily reliant upon anecdotal evidence.  In the current market, it means that those without a solid basis for market valuation interpret data as follows:

• If there are fantastic corporate profits without concomittant wage gains, the worker is losing out and will not be a good consumer so it is bad for the market.  If wages start to rise, that is also bad (even if corporations can afford to pay a little more and still have great profits) because that is inflationary.
• If payroll employment declines to a rate of growth consistent with the long-term healthy trend for the market, that is bad.  It shows a weakening economy.  Direction nonsensically trumps level.  If employment growth is strong, then the Fed is going to step in and kill the economy.
• If commodity prices decline, it shows the market wisdom that the economy is heading for a recession.  If prices increase, we will have excessive inflation and the Fed will kill the economy.
• If GDP growth is a bit over the long-term trend, the economy is "too hot."  If it is a bit under, the economy is headed for a recession.  There is no understanding that GDP quarterly figures vary significantly around the trend line.  That is normal data for a healthy economy, but most market pundits do not understand what "normal" looks like.
• The yield curve is inverted, that is bad, even though it keeps fixed rate mortgages affordable.  If the ten-year rates rise, that is also bad, since stocks compete with bonds on valuation.  And of course -- the Fed will kill the economy!
• If some companies warn about earnings, or give a cautious forecast, the current punditry jumps on each data point, ignoring the overall picture.  This pattern has now persisted for thirty months.  It has happened partly because many pundits are drawing upon the hype of analysts and corporations from 1999-2000.  They are fighting the last war!  There is a certain arrogance in thinking that as a pundit or hedge fund manager one learns from the past, but corporate executives have ignored the same lessons.  Jim Cramer wrote today on TheStreet.com site that analysts no longer have a reason to hype -- that they are tripping over each other with "sell" ratings.

There is a way to profit from this.  It begins with knowing three things:

1. What is currently "baked in" by the current market valuation?  Any pundit who does not have a good valuation model is suspect.  Valuation is the ultimate test of sentiment.  Indicators like the AAII bull/bear report or what hedge fund managers are doing can change on a heartbeat and do not reflect the withdrawal of individual investors from mutual funds and the market.  They shall return.
2. What is the risk?  What if there is a real economic slowdown?  What would happen to corporate earnings and the PE multiple for the market?  I have written about this, but my voice is a lonely one.
3. What economic numbers are consistent with The Glide Path?  Any time the economy slows, there is an increased risk of recession.  How big is this risk?  A "normal" risk rate might well be 20%.  What should we expect when we all want to see some slowing in the economy and a cessation of rate increases?

A Glide Path solution will involve some variation in GDP growth, payroll employment growth, and inflation, as (temporary) energy price shocks work through the economy.  Wages should move higher.  There will always be some earnings misses, but will the ratio continue to be as strong as it has for nearly three years?  Companies, in general, are avoiding overly bullish forecasts.  Analysts are already low-balling earnings because they think they are economists.

The thoughtful individual investor or hedge fund manager can profit nicely from this situation.

After the Fed - Adding to the Ned Davis Story

There is a difference between a compilation of data and research.  Several recent "studies" of market behavior after the Fed finishes tightening have listed the stock market returns in the subsequent period.  Despite some rather obvious weaknesses that we have described in a prior discussion, these reports continue to get a lot of play in the financial media.

Institutional investors pay a lot of money for research like this, and one assumes that they have staff that can help them with the interpretation.  When the studies get so much play in the media, however, it starts to have an impact on my own clients.  They depend upon me to follow this work and assess the validity.  That is my mission here.

I have added some information (some from the Federal Reserve Board's official site, and some calculated, adding 1971 which was left out by Davis) to illustrate the various ways one might interpret the compilation.

[click to enlarge]

There is little to be learned from this compilation.  There are too many different variables and not enough cases.  I wish that the next journalist who runs a story about the "Ned Davis findings" would ask them why they thought the depression era changes were relevant.  The Fed does not even include these in their database.  They only show the ending dates, so you do not see that the 1953 cycle took seven years to increase the discount rate from one percent to two percent.  Who cares?  Does anyone really believe that these changes have anything to do with market prospects after this tightening cycle?

You could look at the data here and try to consider the length of the cycle, the amount of the increase, the starting or ending rate, or many other factors.  If you feel compelled to draw an inference from similar cases, why not look at the results since the creation of econometic modeling.  Since I was working in this field in 1976, I can put a nice starting point on the analysis!  All of a sudden the conclusion is much different, a normal positive result.

I addressed the lack of similarity in Fed tightenings in this post, and also in my discussion of Ed Keon's excellent work.  Ed pointed out that the key factor in the Fed cycle was whether or not they were successful -- not any "average" analysis.  I am sure that a good quantitative guy like Ed was not putting too fine a point on this.  Any multivariate analysis requires plenty of data.  But his conclusion is excellent.

Investors would be wise to ignore all of these pseudo relationships and look to the market fundamentals.  As we will show in upcoming posts, there is a lot of negativity "baked in" to current pricing, partly because of the widespread publicity and acceptance of reports like that from Ned Davis.

Watch the Research Ripples - Art Cashin on CNBC

Art Cashin is the best source of what people on the NYSE floor are thinking.  In our office, someone will sing out "Art's on!" and we pay attention.  We are not saying that he is always right in his opinions.  We believe he is an excellent source of information.  He talks to traders and knows what worries them.

His interview today shows the signficiance of what we have been writing about Wall Street Research.  He does not comment directly on that subject very often, nor did he do so today.  But take a look at the interview and I'll connect the dots after the market closes.

Here are some of his comments in an interview with Erin Burnett -

Art:  ...the transports have been the weakest of the whole lot.
Erin:  ...What's going on there.  Some have said...if we can't get the transports to recover we're not going to get the market to rise either.
Art:  Absolutely.  [goes on to show double top]  The most recent top was on May 10th.  They have gone straight down since then....That's an indication, at least a hint to us, that maybe the economy IS weaker than people think.  If you and I are not buying things...and having them shipped on FedEx and overnight then the transports will be weak.  They are down 20% and have frightened other markets.

So Art is saying that traders are viewing the weakness in the transports as an indication that the economy will be weak.  Many of them place more weight on that information than any written forecast or report.

So what are the factors behind the weakness in transports?  [to be continued]