With markets, it doesn’t matter what people say.  What matters is what they rely upon.

Face it, people have opinions, and when asked only the most cautious or prudent won’t give an answer.  Talk is cheap.

But money talks.  What will people or institutions risk some of their financial well-being in order to make money?

Turning points are exceptionally difficult to call with time precision.  Anyone can say that a trend is going to break for a long while before it breaks; the trick is to be able to make the change within a short distance of the inflection point.  I’ve done it a few times, but I have little confidence in whether I can do it regularly.

Examples:

• Five months before the crest of the housing bubble, I wrote a piece on why it would pop soon.
• A few months before the subprime mortgage crisis hit, I wrote a piece on why it would end badly.
• In March of 2000, I told the investment department that I worked in that financing for tech companies was unwinding, and that the tech bubble would deflate.
• In October 2002, I sensed that the bear market was over and began taking risk in my own portfolio, as well as my main client’s portfolio.
• In May 2003, I told my boss that we were in a liquidity rally and that shorting lower grade companies was suicidal, because financing was becoming available for marginal ideas.  In early 2004, I confirmed that by writing a piece showing that the bull market had a way to go.
• And recently, I made predictions on munis, mortgage bonds, agency bonds, and investment grade and junk corporates.

Now part of this is that if you predict enough things, you will have some right ones to point to.  I am obviously picking and choosing here, but when I made these predictions, there was a method to my madness.  I am not like Cramer, who makes predictions every day.  I wait for points where markets are out of kilter, and then I act, and sometimes predict.

Calling turning points is very difficult.  I want to offer two bits of advice to those to try to do so.

1) Look for situations where the yield is unsustainable on the high side or on the low side.

Examples:

• Earnings yield too low during the tech bubble.  Also workers were relying on stock to rise, because they were getting much of their pay through options.
• Net yield on much residential investment real estate negative in 2005-7, without even factoring in maintenance costs.  When someone is relying on price appreciation in order to break even something is wrong.
• Toward the end of the commercial real estate bubble, the same was true.  Equity investors began to rely on price appreciation in order to break even.
• When spreads on high yield blew out, at its worst the market was assuming that half of all high yield issues would die, with low recoveries.  Even the Great Depression wasn’t that bad.  The same was true in a faint echo for BBB Corporates.
• During the recent bottom in March 2009, high quality companies could be bought for less than their net worth and at earnings yields unseen since 1973-74.

2) Look for a qualitative change when you think we might be near a turning point.

• Chatter changes at/near turning points.  Certainty gives way to uncertainty.  Uncertainty gives way to worry.  Worry gives way to panic.  In October 2005, Googlebots that I created tipped me off to the change in the residential real estate markets way ahead of most parties.
• Inflection points tend to be times of stasis as far as economic variables go, but confusion in terms of chatter.  During the tech bubble in early 2000, the chatter became decidedly less certain.

Inflection points are times of change, and chatter should reflect that.

Coming back to contrarianism, ask yourself, “What are people relying on to be true, that may not be true?”  That is what it means to be a contrarian.  Mere disagreement means little.  Where have men placed their bets?  Betting against the consensus is what a contrarian does.

I finished the first phase of a project today.  But first let me tell you a story.  It was 1990, and the Society of Actuaries Investment Section was holding a conference.  It was a great conference; I still have the binder from it.  There are few meetings from twenty years ago that still have relevance for me.

One of the presentations was by Stanley Diller, a managing director of Bear Stearns, who insulted all of the actuaries at the conference by telling them the the insurance industry was dead wrong for talking about yields and spreads.  Everything was duration and convexity, and those that did not understand that would lose.

He ended his presentation suddenly, did not take questions, and stormed out of the room.  I’m not sure why, but I had a seat in the back, and intercepted him.  I said, “You can’t just say this and not give any justification for your views, how do you back it up?”  He thrust a business card into my hand and said, “Call my secretary, she will send you the info.”  He stormed away.

The next day I called the secretary, and she told me she would send the information.  Two days later, I had it, and a few days later, I had replicated it in my own model.

Since then, I have used the model profitably many times.  Today I use it to describe the yields in Treasury Notes and TIPS.  I have used it to produce an estimate of future inflation expectations.

Using closing prices, here is my estimate of the coupon-paying yield curve:

And here is the spot curve (estimating where zero coupon bonds would price):

And finally, the forward curve, which estimates the expectations of future short-term rates, inflation, and real rates:

Pretty neat, huh?  Let me tell you a little about the model:

• Values are as of the close 12/22/2009, but the model can be run in real time.
• It is estimated from the full coupon-paying Treasury Note and Bond markets — over 200 bonds in the model.
• The model estimates a nominal spot curve, fitting prices with 4 parameters, over 99% R-Squared.
• The model estimates a forward inflation curve, fitting TIPS prices with 4 parameters, over 99% R-Squared.
• The two models are estimated jointly, through nonlinear optimization.
• The model has one constraint — nominal spot yields must be positive after 4 months.
• Every other curve is derived from those two curves.

What are the useful things that we learn from the model?

• There are mispricings in the Treasury and TIPS curves, but they are typically small, and would be hard to make money off of.  That’s  demonstrated by the high R-Squareds.
• The Fed has achieved its goal of making real rates negative in the short term.
• And, has made made nominal rates negative for some very short instruments inside 6 months of maturity.
• Inflation expectations start low, and peak around 2022, then tail off.
• Long term inflation expectations are still under 3.5% — ignore the portion of the inflation and real curves after 23 years, they are extrapolations.
• Implied short-term real yields go positive in 2011, peak in 2024 and tail off thereafter.
• The nominal forward curve is steep as a mountain on both sides.  Though there is a lot of fear over what will happen over the next 12-14 years, those fears have not been built into the prices of longer-dated Treasury securities.
• The nominal spot curve peaks after 22 years — in my experience, that is normal, and is a reason why longer nominal note yields decline.  US Treasury — take note.
• Inspecting the differences between coupon-paying yields on Treasuries and TIPS makes inflation expectations look more tame than they really are.  Federal Reserve — take note.
• 30-year TIPS would likely fund cheaper than 20-year TIPS — US Treasury, take note.  The scarcity value would help as well.

This is just the beginning.  I’m not planning on writing about this every day, but I should be able give you some updates every now and then.  Hopefully the firm I work for should be able to benefit through research that this enables me to create for institutional clients.

Full disclosure: I own shares in Vanguard’s TIPS fund.  And truth, we all own Treasuries somewhere if we look deep enough.