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Seeing Order in Financial Chaos

Trying to predict the future of finance is a bit like trying to forecast the Dow Jones industrial average: a golden opportunity to look like an idiot and lose a lot of money, simultaneously.

But even a cursory examination reveals that the world’s financial markets are swiftly mutating. New concepts and technologies are seeping into the global capital pools. Clever financial instruments, turmoil and volatility will grow inexorably. Some very smart people are betting some very big dollars--and Japanese yen and German marks--that this decade of financial innovation will be even more dynamic than the last.

Much of that dynamism will arise from the fusion of vast computational power with emerging financial theory.

Twenty years ago, academic research in finance converged with nascent local markets to help spawn enormous global markets in options and futures. That revolution is still going strong.

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Twenty years from now, people may well look back on the 1990s as a time when comparable financial revolutions were launched.

That’s because technology is creating new playgrounds for theory, which offer radically different perspectives on old markets. This pending brew of technology and theory assures some heady market behavior.

The goal, quite simply, is to predict the future--in this case, future prices in a given market. At this point it’s easy for any such discussion to become philosophical, if not theological. How can anyone know what the future holds? And if future market behavior could be accurately foretold, wouldn’t everyone act accordingly, depriving the insight of its advantage?

That’s where Chaos Theory comes in. But first, a little background.

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Lately, in the world of financial innovation, there’s been a shift in emphasis away from codifying expertise and toward the detection of subtle patterns within the turbulent flow of financial data.

Neural networks--software systems that don’t merely execute commands but actually construct models of the world by establishing connections between seemingly disparate bits of data--are now becoming popular on Wall Street.

Thus, the computer is no longer just a device for tracking financial transactions, or machine-tooling new securities. Now it’s like a high-tech decoder ring, making out patterns where none were visible before.

The new computers that do this are extraordinarily powerful; they are to yesterday’s machines what scanning electron microscopes are to a magnifying glass, and they empower software to spot the unusual ecologies of relationships within and between financial markets. Naturally enough, new breeds of mathematical theory have emerged to explain such links.

In Chaos Theory, which purports to describe the order that underlies seemingly random events, there’s a saying that the rustle of butterfly wings in Texas can bring typhoons to Singapore. Mightn’t similarly non-linear--but critical--relationships exist in financial markets?

Might not the dip of a commodity price in Malaysia cause the price of IBM and Sony stock to tumble two years later? Is the stock market truly a random walk? Or is that only what our outmoded statistical techniques and technologies let us see?

“What we’re doing is finding the non-linear structures in all this financial data,” asserts Doyne Farmer, vice president of research at Prediction Co., a young Los Alamos, N.M., spin-off that is applying next-generation mathematical techniques and computer power to financial data. “We have methods for extracting patterns from data series, and we believe that we can use those patterns to predict the future of financial time series.”

Amazing, yes, but theoretically not impossible. Picture a performance hall packed with a thousand musicians--all but a dozen playing nothing but noise. Wouldn’t it be nifty to have a filter that could detect the 12 musicians playing the melody? This is at the heart of the ongoing research in financial data: to see if there really are patterns within the noise that can let you “predict” with confidence what will be “played” next.

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Farmer acknowledges that it is too early to say if all this will transform finance. On the other hand, he insists that this approach has so much potential precisely because it represents a different way to view financial data.

“I think it’s still far too early to predict the impact,” says Robert Merton, a Harvard Business School finance professor who helped pioneer financial theory back in the 1960s and 1970s.

But he notes that “over the past 30 years, the driving force in finance has been the ‘efficient market hypotheses,’ ” adding: “I would say that we aren’t satisfied completely with that paradigm, and people have gone off to explore the imperfections.

“I don’t want to say we’re in chaos ourselves,” says Merton, “but we’re going to get a more fragmented theory of finance.”

What new investment opportunities lie within those fragments? What tools, technologies and markets will enable investors to transform those theories into practice?

These questions help guarantee that financial markets will continue to be vital--and volatile. Will they also become more predictable? That’s the Holy Grail--and that’s what all this nascent innovation will discover.


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