A gratifying ending.

This past holiday season I had a student give me a copy of the book "The Black Swan" by Nassim Taleb. The book makes the argument that much of human life is a result of largely unpredictable event that radically alter the world in an unforeseen and unplanned for way. The September 11th terrorist attacks were a black swan. The great depression was a black swan. The internet was a black swan. The list goes on and on. The author's background is that of a wall street risk analyst, who makes the argument that much of our risk analysis is based around the assumption of the gaussian distribution when the world just doesn't work that way in many cases.

The book was written in advance of the home mortgage collapse and broader recession of the past few years, but as I read through the book I realized the author's ideas have largely been vindicated by the collapse as he continually derides wall street analysts for using Gaussian models to determine risk while ignoring their exposure to the catastrophic events because their model under predicts its likelihood. In other words, their base assumption is flawed (i.e. real estate always goes up in value)

I found the book interesting, not so much for the ideas, as due to my education I already make a habit of dissecting models and identifying their assumptions and limitations in applicability, but because apparently professional people who's job it is to determine probabilities don't. Now if you're making decisions assuming Gaussian based risk you're going to be wrong, not often, but but on occasion. With Gaussian based risk both the upside and downside are limited in impact. But complex systems don't have Gaussian distributions because of interconnectedness. So, if you are a wall street firm that operates in an increasingly interconnected financial world and you decide make a bet on something like, say, the likelihood of a pool of mortgages going bad, and you use historical data rather than analyzing the actual mortgages, and you determine the probability of pay-off, and thus your exposure using Gaussian based analysis, than you stand the chance of losing ungodly sums of money. If you're wrong, you're bankrupt.

So I finished "The Black Swan" and started on a book Collyn got me for my birthday called "The Big Short" by Michale Lewis, another ex-wall street guy. It tells the story of a small handful of players who realized that the large Wall Street firms and rating agencies were creating a mortgage based industry doomed to failure, and set about to make money from it. By this point you may have heard of such terms as mortgage backed securities, collateralized debt obligations, credit default swaps, and similar. This book tells how they came about. Mike Burry was emailed a list of mortgage bonds from Goldman Sachs to pick the absolute worst from, to create a credit default swap which would pay-off if people defaulted on the underlying mortgages. "It was as if you could buy flood insurance on the house in the valley for the same price as flood insurance on the house on the mountaintop." So Mike Burry created credit default swaps based on the worst of the underlying mortgages, no stated income,100% floating rate negative amortizing mortgages with short interest rate reset dates. In mortgage terms, alt-A loans. Then, after Goldman Sachs allowed him to assemble these financial bombs and place a bet that they would blow up, they sent him a letter "congratulating him on being the first person, on Wall Street or off, ever to buy insurance on that particular item." It created an entire industry where the wall street firms would pool together toxic mortgages and then sell them off to unsuspecting interests. The firms would then create credit default swaps and take a commission by selling both the short and long position. It was is if you allowed someone to knowingly design a car with faulty brakes and then agreed to find a buyer for it. You then take the car to an intern at Car and Driver magazine to get it rated. (Moodys and Standard & Poors, the bond rating agency's rated the mortgage pools essentially by using historic data based on 80% fixed rate loans to buyers with high credit ratings.) Because you buy so much advertising space at Car and Driver you tell them it's just like all the other cars that you've historically brought them and they should make you happy because you keep them employed, so they give it a good rating. You then go sell the car to an unsuspecting buyer declaring that it's a good buy because the magazine said it was, and you charge them a commission for brokering the sale. Then you also broker a side bet between the manufacturer, who bets the car will crash, and someone else who thinks it won't because car and driver gave it a good rating, and charge them each a commission. You went out of your way to find two suckers, and then charged them for the privilege of working with you.

From "The Big Short": "Goldman Sachs had persuaded AIG FP to provide the same service to the booming market in subprime mortgage loans that it provided to the market for corporate loans." "In rapid succession, Goldman created a bunch of of multibillion dollar deals that transferred to AIG the responsibility of all future losses from $20 billion in triple-B-rated subprime mortgage bonds. It was incredible. In exchange for a few million bucks a year, this insurance company was taking the very real risk that $20 billion would simply go poof"

Goldman Sachs pioneered this type of investment in the housing market, and not surprisingly, weathered the down-turn the best. Oh, that's right, then there was this show of restraint. Of course, Goldman Sachs doesn't just bless the U.S. investor with their expertise.

So I finished the book on Wednesday. As I sat down to eat my lunch today I logged onto yahoo to find what can only be described as a gratifying ending to my recent literary pursuits. Now I'll just try not to be skeptical about the behind the scenes role of money in prosecuting white collar crime.

If you are interested in any of this, I highly recommend both The Black Swan and The Big Short