Banking on outlier detection

Recent bank failures point to the continuing need for vigilance by regulators and investors. Now, a report in the International Journal of Operational Research, discusses the possibility of an early-warning system that spots the outliers before they fail.

The downfall of dozens of banks and financial organizations across the globe has been in the headlines since the meltdown of the subprime mortgage market, but even during the decade before, 1997 to 2007, more than forty banks failed in the US.

Randall Kimmel of the Department of Finance, at Kent State University, and colleagues David Booth and Stephane Elise Booth explain that there are numerous financial computer models that can predict specific outcomes for a given bank. However, these programs require large amounts of data available only to the banks themselves and the regulators and this data requires lots of preparation and manipulation for the model to work properly.

Bank regulators have the resources to handle the data and to use the complex computer models. But, these are significant barrier for researchers and individual investors. Kimmel and colleagues have now shown that a simple model with minimal data demands can be effective in the early detection of potentially troubled banks.

They suggest that banks that are similar to one another in size, based on total assets or total loans, should be similar to one another in terms of the associated returns or risk factors, namely net operating income and net loan losses respectively. “The further from its peers a bank is in terms of these variables, the more likely it is a potentially troubled bank,” Kimmel explains, “In terms of statistical analysis, such a bank would be an outlier.”

A problem bank can be defined as a bank with lots of high risk assets compared with total reserves, it is usually one that is performing poorly relative to other banks of a similar size – it is a statistical outlier, in other words. Such outliers distort the traditional statistical analyses making it difficult to spot them among the more average performers.

The KSU team has now developed a new application of a mathematical model, a Locally Weighted Scatter Plot Smooth, which they say requires minimal data preparation. More critically, it can be run in many off-the-shelf statistical software packages. Their model could be very effective as an early warning system for detecting potential bank failures.

“Our solution to this problem is to use a special type of regression, called LOESS,” explains Kimmel, “It gives more weight to the information about a particular bank the more similar it is to a peer bank, using this to build a profile (prediction equation) of what a bank should look like.” He adds that one then compares the actual information from the banks, which is in the public domain, with this profile. “Our research indicates that this technique, using readily available information and statistical software, is able to identify potentially problematic banks,” says Kimmel.

The researchers suggest that the same approach could be extended to the analysis of other industries, especially those that are highly regulated like utilities.

“The analysis of outlying data points by robust Locally Weighted Scatter Plot Smooth: a model for the identification of problem banks” in Int. J. Operational Research, 2010, 7, 1-15

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