The Fed Can’t Match Complexity Theory and Bayes’ Theorem
Michael Covel: Yesterday, we discussed Long Term Capital Management’s collapse and how you helped negotiate the deal that may have prevented that crisis from turning into a disaster worse than 2008. Today, we’re going to explore your own personal journey to understanding how the markets really work.Your direct involvement with the Wall Street bailout in 1997 led you to the conclusion that the Fed has no idea what it’s doing, right? But you weren’t always that skeptical, were you, Jim?
Jim Rickards: No, not up to that point, Michael. I had no real reason to question the system. Back in 1997, I was a lawyer for LTCM. We had two Nobel Prize winners on our staff. We had a team of Ph.D.s from MIT, Harvard, Yale, Stanford, etc.We had some of the best brains in finance working for us, in other words.
I trusted all those Ph.D.s and Nobel Prize winners because our had made a lot of money. I had no reason to doubt them.
But by the time the crisis was over, I lost 92% of my own money. But nobody got sued, nobody was really punished. Many at LTCM have since gone on to great success. That doesn’t surprise me because they’re brilliant people. But imagine losing $4 billion in a month and walking away without a scratch.
I was just their lawyer, so I wasn’t making these deals. And I didn’t understand the deeper complexities of the financial system at the time. I’ve since learned the truth. The truth is that their models had nothing to do with the real world. If their models were right, the crisis never would have happened.
Michael Covel: So you were basically a victim of your own firm’s misguided policies. Is that what set you off on your mission to figure out how the financial markets really work?
Jim Rickards: Exactly, Michael. I began studying the dynamics of capital markets on my own. I took some university courses, but I did most of the research on my own.
I studied physics, network theory, complexity theory, applied mathematics and so on. I took all that learning and applied it to the markets, which the Fed does not do. The Fed assumes so many things about markets that are simply false, like that markets are always efficient, for example. They’re not. So, the Fed’s models are empirically false. But it’s not just my opinion. Studies have proven how faulty their models are.
I discovered that applying complexity theory to markets, in contrast, got much better results.
Michael Covel: It seems like these other sciences, like the behavioral sciences, offer a superior view of the markets. But the financial experts at LTCM and the Fed ignore them, correct?
Jim Rickards: That’s absolutely true. I actually use three separate branches of science. And none of them are conventional financial theory. Behavioral science is very powerful. It refutes many of the pillars of modern financial theory, like the efficient market hypothesis.
But I also use complexity theory. It’s not mainstream. And I guarantee it’s not being used by the Fed because I’ve discussed it with them. And they have no idea what I’m talking about.
Michael Covel: When you say complexity theory, Jim, please define that for the audience.
Jim Rickards: Of course. People assume that if you had perfect knowledge of the economy, which nobody does, that you could conceivably plan an economy. You’d have all the information you needed to determine what should be produced and in what number. But complexity theory says that even if you had that perfect knowledge, you still couldn’t predict economic events. They can come seemingly out of nowhere.
For example, it was bright and sunny one day out in the eastern Atlantic in 2005. Then it suddenly got cloudy. The winds began to pick up. Then a hurricane formed. That hurricane went on to wipe out New Orleans a short time later.
I’m talking about Hurricane Katrina. You never could have predicted New Orleans would be struck on that bright sunny day. You could look back and track it afterwards. It would seem rational in hindsight. But on that sunny day in the eastern Atlantic, there was simply no way of predicting that New Orleans was going to be devastated. Any number of variables could have diverted the storm at some point along the way. And they cannot be known in advance, no matter how much information you have initially.
And the same applies to financial panics. They seem to come out of nowhere. Traditional forecasting models have no way of detecting them. But complexity theory allows for them.
Michael Covel: So, what does complexity theory actually entail? What’s the science behind it?
Jim Rickards: Complexity theory has four main pillars. The first is the diversity of actors. You’ve got to account for all of the actors in the marketplace. When you consider the size of global markets, that number is obviously vast.
The second pillar is interconnectedness. Today’s world is massively interconnected through the internet, through social media and other forms of communications technology.
The third pillar of complexity theory is interaction. Markets interact on a massive scale. Trillions of dollars of financial transactions occur every single day.
The fourth pillar, and this is the hardest for people to understand, is adaptive behavior. Adaptive behavior just means that your behavior affects my behavior and my behavior affects yours. That in turn affects someone else’s behavior, and so on.
If you look out the window and see people bundled up in heavy jackets, for example, you’re probably not going to go out in a T-shirt. Applied to capital markets, adaptive behavior is sometimes called herding.
Assume you have a room with 100 people. If two people suddenly sprinted out of the room, most of the others probably wouldn’t make much of it. But if half the people in the room suddenly ran outside, the other half will probably do the same thing. They might not know why the first 50 people left, but the second half will just assume something major has happened. That could be a fire or a bomb threat or something along these lines.
The key is to determine the tipping point that compels people to act. Two people fleeing isn’t enough. 50 certainly is. But maybe 20 people leaving could trigger the panic. Or maybe the number is 30… or 40. You just can’t be sure. But the point is, 20 people out of 100 could trigger a chain reaction. And that’s how easily a total collapse of the capital markets can be triggered.
Michael Covel: Nowthat’s a sobering thought. And your main point is that the Fed’s models don’t account for that kind of complexity, right?
Jim Rickards: Exactly correct. Complexity theory explains financial panics much better than the Fed’s old-fashioned models. It accounts for market shocks that seem to come out of nowhere. And I promise you, because I know first-hand, the Fed doesn’t use complexity theory. Like I said, I’ve discussed it with them and they know nothing about it.
They use equilibrium models which treat the markets like a clock you can just wind and unwind, depending on the need. They treat markets like they’re some kind of machine. It’s a 19th century, mechanistic approach. But 21st-century markets aren’t machines and they don’t work in this clockwork fashion.
Michael Covel: OK, that pretty much covers complexity theory. What other analytical tools do you use to forecast markets?
Jim Rickards: I also use something called inverse probability. The allies used it to crack the German naval codes during WWII. The British hired the best minds in physics and mathematics, not people in financial markets. Another term for inverse probability is Bayes Theorem.
It seeks to solve problems when you don’t have enough initial information to make a firm conclusion. It’s highly valuable when clues are sketchy. You first form a hypothesis using the best information you have. Then you take that hypothesis and test it against subsequent events. We call those indications and warnings. That’s why it’s called inverse probability. You’re working backwards to test the validity of your original hypothesis.
The intelligence community uses inverse probability to prevent terrorist attacks. But you can also apply it to the capital markets.
Janet Yellen would say, for example, if you gave her 5 million data points she could predict economic outcomes. But that’s impossible. She’ll never have that data.
So it should come as no surprise that the Fed has the worst forecasting record in the world. It had been wrong six years in a row since 2009.
We do the exact opposite of what Janet Yellen does. We realize we don’t have the data. We instead develop a hypothesis, but we need to test it constantly against subsequent data. So we continually check our hypothesis with real world data. If it’s consistent with our hypothesis, we keep pursuing it. But if the data doesn’t match, then we have to abandon our initial hypothesis and devise a new one. And the new hypothesis is also subjected to the same tests. It’s not a static model like the Fed uses.
Michael Covel: That’s fascinating stuff, Jim. With the Fed’s models, it’s garbage in, garbage out, right?
Jim Rickards: Yes. The forecasting models the policy makers use are absolutely incorrect. I’ve spent 17 years studying complexity theory and inverse probability. I’ve found it applies to the real world in ways the Fed’s models never can. We know the old models don’t work. Empirically, they don’t work. Just look at the results.
Michael Covel: You’ve become very famous talking about the currency wars. Can you please discuss the actors and their various motivations?
Jim Rickards: Sure, Michael. I wrote Currency Wars in 2011. But I could have written it today because nothing’s changed. If any of our listeners haven’t read it yet, I suggest they do.
Currency wars aren’t always underway. But when they are, they can last 10–15 years. And we’re in one now. It started in 2010. I think it’ll be going on in 2020 if the system doesn’t collapse first, which it might.
Currency wars happen when there’s not enough growth in the world and it’s saddled with too much debt. Why does that matter? Economies need growth to pay off debt. Without growth nations are going to default on that debt. And the banking system would be destroyed. The economy will also fall into a depression.
When you’ve got too much debt and not enough growth, the only way to pay it off is through inflation. Nations accomplish that by cheapening their currency. Deflation increases the burden of debt, so virtually every nation with high debt levels pursues inflation.
In currency wars, nations try to steal growth from each other. In 2009, China had the cheap currency. The U.S. was complaining about currency manipulation by the Chinese. But in 2011, the U.S. dollar was the cheap currency. Ben Bernanke gave a speech in Tokyo explaining that the U.S. was the world’s largest economy, so the rest of the world had to let the U.S. maintain a cheap dollar to drive global growth.
But in 2013, the Japanese yen was the cheap currency because Japan launched a series of efforts to stimulate the economy. Weakening the yen was a key part of that strategy. Who suffered? Europe. The euro had remained the strongest currency to that point. But Europe had two recessions in five years.
Finally, in 2014, Europe went to negative rates. That was followed by quantitative easing early in 2015. A cheaper euro resulted and some European economies like Spain and Ireland got a boost. But who suffered from a weaker euro? The United States.
One of the main reasons our growth has been so weak is because of the strong dollar. Two currencies cannot devalue against each other at the same time. It’s mathematically impossible. If one goes down, the other must rise. And vice versa. So the dollar is strong today. It won’t last forever, but that’s where we stand today.
Michael Covel: It’s a never-ending volley back and forth, essentially.
Jim Rickards: Exactly. There’s no logical conclusion.
Michael Covel: Thanks so much for joining me today, Jim.
Jim Rickards: Take care, Michael.