On December 20, 1994 Mexico?s newly installed president Ernesto Zedillo devalued the currency, the peso, by 15%. As a candidate he had said he would ?defend the peso like a dog.? That day the peso went from 3.47, where it had been for a year, to 3.95 and the trading floors of Wall Street were filled with the sounds of barking dogs.
On that day I was in research working with the emerging markets trading desk of Salomon Brothers. They had invested heavily in Mexico, as had most of the major investment banks.
That afternoon the Salomon trading floor was giddy; the first drop in the peso had been profitable for the firm. That happiness was short-lived as it began to understand the complexity of its positions in Mexico.
The majority of the risk came from complex financial structures called Principal Protected Peso Linked Notes. These were leveraged investments in local Mexican bonds sold to customers, who put down 20% to 30% cash and were lent the remainder.
The first losses were born by the customer, later losses by the firm. It had been profitable trade in a world where the peso moved less than 1% a month, but it was awful in the new world of daily moves of 15%.
By the end of that first day it was clear Mexico could not hold the peso at its new level and that they would have to devalue further. It was also becoming clear to the firm that it had a big mess on its hands and that any further losses would come from its pockets.
How was Salomon keeping track of its investments? In a spreadsheet built by the traders.
As the crisis continued to unfold it became clear that few, including myself, had understood what could go wrong. What had seemed a relatively straightforward asset was too complex to be managed in such an ad hoc manner.
This is far more common on Wall Street than most realize. Just last year JP Morgan revealed a $6 billion loss from a convoluted investment in credit derivatives. The post mortem revealed that few, including the actual trader, understood the assets or the trade. It was even found that an error in a spreadsheet was partly responsible.
Since the peso crisis, banks have become massive, bloated with new complex financial products unleashed by deregulation. The assets at US commercial banks have increased five times to $13 trillion, with the bulk clustered at a few major institutions. JP Morgan, the largest, has $2.5 trillion in assets.
Much has been written about banks being ?too big to fail.? The equally important question is are they ?too big to succeed?? Can anyone honestly risk manage $2 trillion in complex investments?
To answer that question it?s helpful to remember how banks traditionally make money: They take deposits from the public, which they lend out longer term to companies and individuals, capturing the spread between the two.
Managing this type of bank is straightforward and can be done on spreadsheets. The assets are assigned a possible loss, with the total kept well beneath the capital of the bank. This form of banking dominated for most of the last century, until the recent move towards deregulation.
Regulations of banks have ebbed and flowed over the years, played out as a fight between the banks? desire to buy a larger array of assets and the government?s desire to ensure banks? solvency.
Starting in the early 1980s the banks started to win these battles resulting in an explosion of financial products. It also resulted in mergers. My old firm, Salomon Brothers, was bought by Smith Barney, which was bought by Citibank.
Now banks no longer just borrow to lend to small businesses and home owners, they borrow to trade credit swaps with other banks and hedge funds, to buy real estate in Argentina, super senior synthetic CDOs, mezzanine tranches of bonds backed by the revenues of pop singers, and yes, investments in Mexico pesos. Everything and anything you can imagine.
Managing these banks is no longer simple. Most assets now owned have risks that can no longer be defined by one or two simple numbers. They often require whole spreadsheets. Mathematically they are vectors or matrices rather than scalars.
Before the advent of these financial products, the banks? profits were proportional to the total size of their assets. The business model scaled up linearly. There were even cost savings associated with a larger business.
This is no longer true. The challenge of risk managing these new assets has broken that old model.
Not only are the assets themselves far harder to understand, but the interplay between the different assets creates another layer of complexity.
In addition, markets are prone to feedback loops. A bank owning enough of an asset can itself change the nature of the asset. JP Morgan?s $6 billion loss was partly due to this effect. Once they had began to dismantle the trade the markets moved against them. Put another way, other traders knew JP Morgan were in pain and proceeded to ?shove it in their faces?.
Bureaucracy creates another layer, as does the much faster pace of trading brought about by computer programs. Many risk managers will privately tell you that knowing what they own is as much a problem as knowing the risk of what is owned.
Put mathematically, the complexity now grows non-linearly. This means, as banks get larger, the ability to risk-manage the assets grows much smaller and more uncertain, ultimately endangering the viability of the business.
Large banks will argue about the power of a diverse portfolio. That is true the 96% of the time the markets are benign, but as past crises have shown, when markets fall apart they do so in unison. Put another way, the power of diversity disappears exactly when banks need it.
The diversity argument also reminds me of a quote about Yankees manager Joe Torre, who was notorious for changing pitchers during a game. Said one reporter, ?He kept changing pitchers ?til he found one who sucked that night.?
The major banks prior to 2008 had a similar problem; they kept buying assets till they found the ones that would blow them up.
Citibank, Salomon Brothers successor, is a good example of this. In 2007 it had operations and income streams from almost every country in the world. If any bank could weather a downturn in one part of the US economy Citibank should have been it. Yet they didn?t. Instead they ended 2008 with a government bailout.
Why?
Citibank had so many diverse assets ($2 trillion worth) that it lost focus on what it owned while also making sure that if there was a problem in the world it would participate in it.
Much of its losses came from esoteric investments in the US mortgage markets, Super Senior Tranches of CDOs. Congressional testimony shows that these assets were seen by the firm as risk free and generated little scrutiny. They also added very little value, yielding barely more than US treasuries. Owning them proved a fool?s game, small initial returns but with a massive downside. It was like, as one trader commented, ?Picking up pennies ahead of a steamroller.?
It?s understandable why board members and CEOs want to grow the banks to gorge on complex financial structures despite the risk they pose. They come from the old world and still think profits grow with size. They know the biggest upfront profits come from opaque financial structures that allow them to extract bigger margins.
And for nine out of ten years they are right and get paid well, but they are building banks destined to fold under their own size and complexity. It?s the tenth year that is the cost to the equity holders and the general public.
Chuck Prince was the CEO of Citibank from 2003 to 2007. He roughly doubled the assets owned, from $1.3 to $2.2 trillion. He was fired in late 2007 as the stock collapsed from 55 to 2. During that period he was paid roughly $130 million.
The Mexican peso crisis ended with a $50 billion bailout engineered by the then US Treasury Secretary, Robert Rubin in early 1995. Days prior to the announcement rumors circulated that Lehman Brothers was teetering near bankruptcy, facing deadly losses should Mexico default.
Thanks to the bailout they didn?t. They needed another thirteen years and a few more stupid investments before accomplishing that.
Source: http://rss.sciam.com/click.phdo?i=79198ff13fbb94845dce6a6b04b5afdc
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