“Too big to fail”: thanks to the financial crisis, it’s a phrase we’re all familiar with. Now a group of European economists and physicists are suggesting that there might be a more useful way to think about banks. In “DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk,” a paper published this month in Nature’s Scientific Reports, Stefano Battiston, Michelangelo Puliga, Rahul Kaushik, Paolo Tasca and Guido Caldarelli argue that it’s not how a big a bank is that matters, but how connected it is to other banks. They’ve even devised a method, loosely based on Google’s PageRank algorithm, to analyze financial data and figure out which banks are the most connected, and, therefore, the most dangerous. The banks which are too connected to fail, it turns out, aren’t necessarily the biggest.
The financial system is deeply interconnected, and that interconnectedness makes it very hard to analyze. Banks borrow from, lend to, and own pieces of one another. Over time, a small number of banks can come to occupy central positions among the rest. If one of those banks stumbles, the repercussions ripple out in many directions – and, if many of them stumble simultaneously, a crisis can ensue. That’s true even for non-huge banks: Even a smallish firm can cause a crisis, if enough other firms are invested in it.
The problem, of course, is that identifying super-connected banks is very, very difficult. Banks can be connected in complex, recursive ways. You need to know not just how much a bank has borrowed, but what its “feedback centrality” might be when trouble starts. That, the researchers write, is where Google’s PageRank algorithm can serve as inspiration. Google’s search engine works by analyzing the links between webpages, with the goal of discovering the most influential ones. In their paper, the researchers take a similar approach, analyzing a large amount of data gathered by the Federal Reserve to figure out which banks might be influential, in a bad way. Their DebtRank algorithm, they write, “takes recursively into account the impact of the distress of an initial node across the whole network.” The higher a bank’s DebtRank, the more risk its failure would pose to the financial system as a whole. The algorithm even works across time: as a crisis unfolds, DebtRanks rise across the bank network, “meaning that the default of each of them would cause a larger economic loss.”
Right now the research is preliminary: In order to produce a truly accurate DebtRank table, the researchers would need access to a much larger dataset than the one they have now, which draws on a limited amount of information about a limited number of banks. (Greater financial transparency would help with that.) Still, they write, “our results suggest that the current public discussion on too-big-to-fail institutions should be broadened to the network-theory notion of too-central-to-fail.” Read the whole paper at Nature.
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