BID® Daily Newsletter
Apr 28, 2009

BID® Daily Newsletter

Apr 28, 2009

DECISION-MAKING AND THE BLACK SWAN


Much has been made of the "Black Swan" concept lately so we wanted to highlight its relationship to banking. The popular book by Nassim Nicholas Taleb, called The Black Swan: The Impact of the Highly Improbable, argues that we should devote more resources in preparing for rare, low probability events that have a major impact. Written in 2007, the book eerily foreshadowed the risk environment around our current economic crisis.
The central thesis of the book relates to the enigmatic black swan. Before black swans were discovered in Australia, the world had no reason to believe these beautiful birds could be any other color than white. Without knowledge of such, no one stopped to ask the question if anyone had seen a black swan or if a black swan was even possible. As it relates to risk, the arrival of black swans can instantaneously change the game.
Black Swan events are not all bad. In fact, these unforeseen changes can help as much as hurt. For example, the rise of the Internet or the availability of TARP Capital was not something forecasted, but each has served to change how banks conduct strategy. From a business model standpoint, banks want to take the maximum amount of exposure to Black Swan events that could turn out to be positive, while minimizing exposure to negative Black Swan events.
That all sounds straightforward enough, except banking is largely based on the opposite view - a systemic problem in the industry. Loans and securities, for instance, have limited upside, but catastrophic downside. It is this asymmetric effect that is causing our industry to restructure. In 2008, for example, we found all the good times of 15% plus ROE didn't compensate for the negative times when loan losses shoot up. Since we are still living this down cycle, it is hard to speculate, but one possible outcome could be greater pricing going forward with possible more equity participation for exposure to commercial development or even subprime loans. While this wouldn't offset the credit exposure, it would improve the return in good times, allowing for a more symmetrical set of outcomes.
Another problem with Black Swan events is that since we don't know what we don't know, how do we plan? While it is difficult to predict Black Swan-type risk, there are some things we can do about it. For starters, we can better understand the limits of our models. Asset-liability, liquidity, pricing, credit stress, value-at-risk and other commonly used models all fell short at predicting actual risk in the last year. To come to the conclusion that models are worthless misses the point. All models contain predictive errors. The predictive error in our models now, for example, is a whole lot less than it was 18 months ago. These errors can stem from an adjustment in distributions, wrong probabilities or just missing a key risk element. The important point is that you know the model's limitations.
The classic example right now is our mortgage prepayment model, that helps predict cashflows in loan and securities portfolio. Historically, we have provided banks with one of the most accurate forecasts of future cash flow. Now that this huge Black Swan showed up at our door, we know that given the potential for loan modifications, spread changes and liquidity, we have a huge error factor introduced to prepayment prediction. As such, we do more planning around events outside of our model's analysis.
The importance of the Black Swan is to ask more questions around what other risks or opportunities you could be missing and how "fragile" or exposed your models are to error. Understanding your own decision-making process is the first step to making the Black Swan a welcome sight.
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