False Profits

Matthew 7: 15 “Watch out for false prophets. They come to you in sheep’s clothing, but inwardly they are ferocious wolves. 16 By their fruit you will recognize them.

Divination is the attempt to gain insight into a question or situation by way of an occultic, standardized process or ritual.

A charlatan (also called swindler or mountebank) is a person practising quackery or some similar confidence trick in order to obtain money, fame or other advantages via some form of pretense or deception.

“We really can’t forecast all that well, and yet we pretend that we can, but we really can’t.”

That last one is courtesy of Alan Greenspan, and it comes from John Mauldin’s recent Thoughts from the Frontline, entitled Economicus Terra Incognita.

Actually, I’m going to spend the first few pages demonstrating that the mathematical models used to forecast GDP and all sorts of interesting economic events are basically nonsense.

That’s very Austrian of you, John.

He begins with a look at the professionals who forecast returns of the stock market, in this case the S&P 500:

Housel calculates that the strategists’ forecasts were off by an average 14.7 percentage points per year.  His Blind Forecaster, who simply assumed 9% gains every year, was off by an average 14.1 percentage points per year.  Thus the Blind Forecaster beat the experts even if you exclude 2008 as an unforeseeable “black swan” year.

So why do people listen to any of these knuckleheads?  An answer is offered via the aforementioned Morgan Housel:

I think there’s a burning desire to think of finance as a science like physics or engineering….Finance is much closer to something like sociology. It’s barely a science, and driven by irrational, uninformed, emotional, vengeful, gullible, and hormonal human brains.

What about the Federal Reserve?

A 2015 study by Kevin J. Lansing and Benjamin Pyle of the San Francisco Federal Reserve Bank found the FOMC was persistently too optimistic about future US economic growth. They concluded:

Over the past seven years, many growth forecasts, including the SEP’s central tendency midpoint, have been too optimistic. In particular, the SEP midpoint forecast

(1) did not anticipate the Great Recession that started in December 2007,

(2) underestimated the severity of the downturn once it began, and

(3) consistently overpredicted the speed of the recovery that started in June 2009.

Of course, forecasting GDP is a waste of time in the first place:

One problem here is that GDP itself is a political construction. Forecasting the future is hard enough when you actually understand what you are forecasting. What happens when the yardstick itself keeps changing shape? You get meaningless forecasts. But this doesn’t stop the Fed from trying.

Of course not.  If they did, what would happen to the value of a Ph.D in Economics?

How about the Congressional Budget Office?

The Congressional Budget Office issues forecasts much as the Federal Reserve does. And like the Fed, the CBO grades itself. You can see for yourself in “CBO’s Economic Forecasting Record:2015 Update.”

Read that document, and you will find the CBO readily admitting that its forecasts bear little resemblance to reality. Their main defense, or maybe I should say excuse, is that the executive branch and private forecasters are even worse.

Mauldin then references something he wrote on this topic three years ago:

If you’ve suspected all along that economists are useless at the job of forecasting, you would be right. Dozens of studies show that economists are completely incapable of forecasting recessions. But forget forecasting. What’s worse is that they fail miserably even at understanding where the economy is today. In one of the broadest studies of whether economists can predict recessions and financial crises, Prakash Loungani of the International Monetary Fund wrote very starkly, “The record of failure to predict recessions is virtually unblemished.”

Tough to engineer a “soft landing” if you don’t even know that you are losing altitude.

Returning to the present day:

Central banks tell us that they know when to raise or lower rates, when to resort to quantitative easing, when to end the current policies of financial repression, and when to shrink the bloated monetary base. However, given their record at forecasting, how will they know?

John, you have already answered this question.  I am sure you do not need me to point it out.

OK, I will.  They won’t know.

The central banks tell us their policies are data-dependent, but then they use that data to create models that are patently wrong time and time again.

Economists do not measure what is important because it cannot be measured; they measure what they can although it is relatively unimportant.  This is the quality of the data on which economists are dependent.

Trusting central bankers now, whether in the US, Europe, or elsewhere, is a dicey wager, given their track record.

It most certainly is not a dicey wager; it is a certain wager – certain to be wrong.

The reason is that they base their models on flawed economic theories that can only represent at most a pale shadow of the true economy.

The “true economy” cannot be measured, and certainly cannot be forecast.  The “true economy” is made up of 7 billion individual actors, each making numerous decisions every day, each with his own subjective value scale – all complicated by the fact that subjective value is not objective; it cannot be measured.

What does Mauldin suggest?  Better models…I’m not kidding:

If I were a young and mathematically gifted economist, I think I would explore the use of complexity theory to model the economy, based not on Keynesian nonsense or the hubristic assumption that an economy can ever be in a state of equilibrium (it can’t), but using Claude Shannon’s information theory instead as a better way to demonstrate how economics works in the real world (an idea brilliantly suggested by George Gilder in Knowledge and Power).

What is Claude Shannon’s information theory?

Information theory is a branch of applied mathematics, electrical engineering, and computer science involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data. Since its inception, based on a landmark 1948 paper by Shannon, it has broadened to find applications in many other areas, includingstatistical inference, natural language processing, cryptography, neurobiology, the evolution and function of molecular codes, model selection in ecology, thermal physics, quantum computing, linguistics, plagiarism detection, pattern recognition, anomaly detection and other forms of data analysis. (Emphasis added)

John, you can’t be serious.  Go back and read what you wrote about Morgan Housel: economics is a social science, not a physical science.  Human action cannot be measured in such a manner such that it can be modeled.  I do not even know what decisions I will make tomorrow.  How can Claude Shannon know?

Mauldin then proceeds to offer his forecast.  Despite all of his earlier commentary and analysis, he offers a reason as to why:

This is the typically the most forwarded letter of the year.

Consider: if you told an economist to model the demand for a good that proved to be wrong 100 times out of 100, he would tell you the demand would approach zero.

And that would be one more incorrect model by and economist, apparently.

End the Fed.

Reprinted with permission from Bionic Mosquito.

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