Transparency benefit: Using big data to combat big risk

Regular readers of this blog know that your humble blogger has been calling for banks to be required to provide ultra transparency and disclose on an ongoing basis their current global asset, liability and off-balance sheet exposure details.

The number one argument against this level of transparency by the banks, their lobbyists, many economists and disbelieving journalists is that this amount of data will overwhelm market participants.

This argument highlights a feature and not a bug of banks providing ultra transparency.

For their argument to be true, the large banks must be overwhelmed by and unable to make heads or tails out of their own data.  This indicates that the banks must be shrunk until they reach a size where they can understand and successfully utilize the information disclosed under ultra transparency.

Of course, if this argument against ultra transparency is false, it says that in the information age banks can make sense of big data.

In an excellent column in Advanced Trading, Andrew Waxman discusses how big data can be used to combat risk.  In doing so, he confirms why requiring banks to provide ultra transparency will end proprietary trading by the banks and lower banks' risk taking.

Everyone is talking about the use of "big data" these days and so now is a good time to reflect on the potential uses of big data by different industries and policy makers to solve some of their long standing issues. 

Here we look at how banks and regulators can use the principles of "big data" to solve some problems -- like how to identify traders who are taking undue risks, or investment salesmen who are fronting a Ponzi scheme.

Or proprietary trading or simply gambling by banks.

First, banks should leverage data to expose the objective reality of different traders’ performance. ... Mining trades’ data could similarly be done to challenge similarly held views about the value and consistency of different traders. It would be very useful to counteract with actual data the halo effect on occasion bestowed on certain traders by past heroic trading exploits. Such heroism, for example, achieved by successfully taking high levels of risk in a tough market, is often rewarded by supervisors with latitude to take greater risks. 

In certain cases, as seems to have been the case in the London Whale episode, such latitude can be disastrous. 

A disciplined data driven approach would serve to assess traders performance on a more objective basis relative, for example, to contextual factors such as: amount of risk taken relative to reward, performance of market benchmarks, volatility of returns over a longer run period. 

Such data by providing a more objective basis for performance assessment would enable better calibration of pay, risk limits, and trader mandates and would lay bare the reality behind a trader's reputation which may or may not have been fairly earned. 

Solid data analysis of ongoing performance should help to separate out myth from reality and help to prevent encouragement of excessive risk taking.... 

Can we identify manipulative or cheating patterns of behavior [think manipulating Libor]? Now this is a field of great promise because, underlying many of banks' top risks, are patterns of behavior that are hard to detect but that can lead to disaster....