Morgan Stanley in 2007

Throwback Thursday. Let's look today at the way in which underestimating the nature and extent
of a loss can be disastrous, even if one is right about the binary question of an asset’s direction.

As Nassim Taleb has put it, “In 2007 the Wall Street firm Morgan Stanley decided to ‘hedge’ against
a real estate ‘collapse’, before the market in real estate started declining. The problem is that they
didn’t realize that ‘collapse’ could take many values.”

It didn’t work out well for them. Morgan Stanley’s hedges would have worked in the face of a
modest decline, but in the event MS lost $10 billion in the face of the one they got.

Taleb also offers some good news for risk managers: machine learning is on the right track. There
are “various machine learning functions that produce exhaustive non-linearities,” that is, that do
control for the fat tails. They do this through “cross-entropy.”

Cross-entropy is a concept taken from the field of information theory, the difference between two
probability distributions. This can be employed (as Taleb explains in a footnote) to “gauge the
difference between the distribution of a variable and the forecast,” which captures the non-linearity
of a payoff function.

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