Updated July 30 at 4:30pm

How Google’s example might prevent a bank crisis

Guest Column: Mark Buchanan
The mathematical insight that turned Google Inc. into a multibillion-dollar company has the potential to help the world avert the next financial crisis. If only banks made public the data required to do the job.

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How Google’s example might prevent a bank crisis

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The mathematical insight that turned Google Inc. into a multibillion-dollar company has the potential to help the world avert the next financial crisis. If only banks made public the data required to do the job.

Sixteen years ago, the founders of Google – computer scientists Larry Page and Sergey Brin — introduced an algorithm to measure the “importance” of Web pages relative to any set of keywords.

Known as PageRank, it works on the notion that Web pages effectively vote for other pages by linking to them. The most important ones, Page and Brin reasoned, should be those drawing links from many other pages, especially from other really important ones.

If this definition sounds circular, it is. It also captures an authentic reality, which is why respecting it gives far superior results. Page and Brin’s breakthrough involved using mathematics to make it work. The required ideas don’t go much beyond high-school algebra, although it takes lots of computing power to make something as sprawling as the World Wide Web possible.

What could this have to do with finance? Quite a lot. The systemic risk that turned the U.S. subprime-lending crisis into a global disaster is circular, too. We can’t identify it simply by looking for the banks with the most assets or the biggest portfolios of risky loans. What matters is how many links a bank has to other institutions, how strong those links are and how risky those other banks are, not least because they too have links to other risky banks.

Something like PageRank might be just the right thing to cut through it.

That’s the argument, at least, made by a team of European physicists and economists in a new study. Their algorithm, DebtRank, seeks to measure the total economic value that would be destroyed if a bank became distressed or went into default. It does so by moving outward from the bank through the web of links in the financial system to estimate all the various consequences likely to accrue from one failure. Banks connected to more banks with high DebtRank scores would, naturally, have higher DebtRank scores themselves.

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