by Jim Rickards, Daily Reckoning:

The first time I appeared on live financial television was August 15, 2007. It was a guest appearance on CNBC’s Squawk Box program at the early stages of the 2007-2008 financial crisis.

Of course, none of us knew at that time exactly how and when things would play out, but it was clear to me that a meltdown was coming; the same meltdown I had been warning the government and academics about since 2003.

I’ve done 1,000 live TV interviews since then, but that first one remains memorable. Carl Quintanilla conducted the interview with some participation from Becky Quick, both of whom could not have been more welcoming.

They and the studio crew made me feel right at home even though it was my first time in studio and my first time meeting them. Joe Kernan remained off-camera during my interview with his back turned reading the New York Post sports page, but that’s Joe. We had plenty of interaction in my many interviews over the years that followed.

When I was done, I was curious about how many guests CNBC interviewed over the course of a day. Being on live TV made me feel a bit special, but I wanted to know how special it was to be a guest. The answer was deflating and brought me right down to earth.

CNBC has about 120 guests on in a single day, day after day, year after year. Many of those guests are repeat performers, just as I became a repeat guest on CNBC during the course of the crisis. But, I was just one face in the midst of a thundering herd.

What were all of those guests doing with all of that airtime? Well, for the most part they were forecasting. They predicted stock prices, interest rates, economic growth, unemployment, commodity prices, exchange rates, you name it.

Financial TV is one big prediction engine and the audience seems to have an insatiable appetite for it. That’s natural. Humans and markets dislike uncertainty, and anyone who can shed some light on the future is bound to find an audience.

Which begs a question: How accurate are those predictions?

No one expects perfection or anything close to it. A forecaster who turns out to be accurate 70% of the time is way ahead of the crowd. In fact, if you can be accurate just 55% of the time, you’re in a position to make money since you’ll be right more than you’re wrong. If you size your bets properly and cut losses, a 55% batting average will produce above average returns.

Even monkeys can join in the game. If you’re forecasting random binary outcomes (stocks up or down, rates high or low, etc.), a trained monkey will have a 50% batting average. The reason is that the monkey knows nothing and just points to a random result.

Random pointing with random outcomes over a sustained period will be “right” half the time and “wrong” half the time, for a 50% forecasting record. You won’t make any money with that, but you won’t lose any either. It’s a push.

So, if 70% accuracy is uncanny, 55% accuracy is OK, and 50% accuracy is achieved by trained monkeys, how do actual professional forecasters do? The answer is less than 50%.

In short, professional forecasters are worse than trained monkeys at predicting markets.

Need proof? Every year, the Federal Reserve forecasts economic growth on a one-year forward basis. And it’s been wrong every year for the better part of a decade. When I say “wrong” I mean by orders of magnitude.

If the Fed forecast 3.5% growth and actual growth was 3.3%, I would consider that to be awesome.

But, the Fed would forecast 3.5% growth and it would come in at 2.2%. That’s not even close considering that growth is confined to plus or minus 4% in the vast majority of years.

If you have defective and obsolete models, you will produce incorrect analysis and bad policy every time. There’s no better example of this than the Federal Reserve.

The Fed uses equilibrium models to understand an economy that is not an equilibrium system; it’s a complex dynamic system. The Fed uses the Phillips curve to understand the relationship between unemployment and inflation when 50 years of data say there is no fixed relationship.

The Fed uses what’s called value-at-risk modeling based on normally distributed events when the evidence is clear that the degree distribution of risk events is a power curve, not a normal or bell curve.

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