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Capital Markets: A New Forecasting Method (w/ Jim Rickards) | Expert View | Real Vision™

2,784 views · Published 23 July 2018 · 2:16 · Indexed 22 September 2026

Channel: Real Vision Presents · 2018 · News & Politics

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Jim Rickards, a renowned author and the chief global strategist at Meraglim, is now predicting another global financial crisis. He explains why, and in so doing, reviews the unusual origins of his predictive analytics tool. He also explores complexity theory and Bayesian statistics. 

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Bringing A New Forecasting Methodology To Capital Markets (w/ Jim Rickards) | Expert View | Real Vision™
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Transcript:  So those four areas, complexity theory, Bayesian statistics, behavioral psychology, and history are the branches of science that we use. Now what do we do with it? Well, we take it and we put it into something that would look like a pretty normal neural network. You have nodes and edges and some influence in this direction, some have a feedback loop, some influence in another direction, some are influenced by others, et cetera. So for Fed policy for example, you'd set these nodes, and it would include the things I mentioned earlier-- inflation, deflation, job creation, economic growth, capacity, what's going on in Europe, et cetera. Those will be nodes and there will be influences. But then inside the node, that's the secret sauce. That's where we have the mathematics, including some of the things I mentioned. But then you say, OK, well, how do you populate these nodes? You've got math in there, you've got equations, but where's the news come from? That's where Watson comes in. Watson's reading all these records, feeding the nodes, they're pulsing, they're putting input. And then we have these actionable cells. So the euro-dollar cross rate, the Yuandollar cross rate, yen, major benchmark, bonds, yields on 10 year treasury notes, bunds, JGBs, et cetera. These are sort of macro indicators, but the major benchmark bond indices, the major currency across rates, the major policy rates, which are the short term central bank rates. And a basket of commodities-- oil, gold, and a few others-- they are the things we watch. We use these neural networks I described, but they're not just kind of linear or conventional equilibrium models. They're based on the science I describe. So all that good science, bringing it to a new field, which is capital markets, using what's called fuzzy cognition, neural networks, populating with Watson, this is what we do. We're very excited about it, getting great results.

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