For Immediate Release
Chicago, IL - May 16, 2012 - Zacks.com announces the list of
stocks featured in the Analyst Blog. Every day the Zacks Equity
Research analysts discuss the latest news and events impacting
stocks and the financial markets. Stocks recently featured in the
Bank of America
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Here are highlights from Tuesday's Analyst Blog:
Super-Models with Fat Tails
Everybody it seems wants to break up the big commercial banks
who use customer deposits to play fast and loose in derivatives
markets. But we hear few good arguments other than "they tend to
lose a lot of money" and "they pose risk to the financial
I am going to give you a nearly irrefutable argument: Once a
bank reaches a certain size of assets, its business becomes too
complex to manage, and it is therefore bound to get caught up in
occasional failures of risk management.
The complexity comes in two major forms that I will call
behavioral and mathematical. First, let's look at the actual size
of some big banks and see where the idea of "too big to manage"
might become relevant...
Total Deposits of 5 Largest US Banks at End of
JPMorgan Chase (
) $1.093 Trillion
Bank of America (
) $1.047 Trillion
Wells Fargo (
) $843 Billion
) $799 Billion
US Bank (
) $215 Billion
This one is simple to understand. Combine lots of variables
concerning the inflow and outflow of cash and other investments
with the tendency of people to do dumb things and you have a recipe
for money to get lost -- especially where the temptation of
leverage is involved in complicated derivatives.
The extreme example is the rogue trader. Sure you can have
controls to prevent theft, excessive risk, and mere stupidity, but
it gets a lot harder when you have tens of thousands of employees
responsible for the movement and accounting of hundreds of billions
There is also the pressure to please Wall Street with an
investment return on those mega billions. JPM's Jaime Dimon trusted
Ina Drew in their London Chief Investment Office to create outsized
returns (even if they called it "just hedging"). And she obviously
trusted the "London Whale" to make it happen.
The losses of big banks over the recent decades are in some ways
a never-ending tale of one trading pal saying to another, "You know
what you're doing... don't you?"
As if trying to tame big assets and lots of staff wasn't enough,
how about trying to tame "wild randomness?" This is the phrase that
Nassim Taleb introduced to us in 2007 in his seminal book
The Black Swan
Taleb taught us that the bell curve of standard deviation was
invented to measure the variation among physical phenomena. It is
also very useful for creating statistical representations of
expected outcomes in some forms of human behavior, like athletics,
academics, election results, and surveys. The normal distribution
gives us useful information to make fairly reliable
But standard deviation becomes extremely fragile when measuring
the fluctuations in asset prices,
especially where derivatives and leverage are involved
. This is because markets are social beasts unto themselves and the
crowd behavior of 100 or 1 million investors and traders can create
randomness that makes bell curves much more flat and wide... and
with very fat tails.
The "tails" are the outlier regions of the distribution. Think
of the event that happens very infrequently, like the 100-year
flood, or the asteroid in your back yard, or winning the
Super Models: Beautiful, But Rarely Real
Ever since the Black-Scholes option pricing model ushered the
dawn of the derivatives era, quantitative types have been busy
creating models of markets to gauge probability and risk.
The primary problem with most models built on standard deviation
is that they don't account for geometric complexity. If a trader or
risk manager assumes that the ten-year volatility of a particular
security will prevail for the next year or month, then he or she is
making a very risky assumption -- again, especially if the leverage
of the positions can create losses that multiply very quickly.
Why? Because markets are built on all sorts of social,
political, economic, even geological and meteorological variables.
Combine one variable each of the preceding and the complexity gets
awfully open to random outcomes outside the bounds of a "normal"
distribution. The standard bank VAR (value at risk) model can't
account for this complexity.
We deride the weather man when his 7-day forecast doesn't pan
out, and we forget when he gets it right most of the time.
Statistical modeling of weather is extremely advanced these days
and it can still miss when you get out past a week. Now imagine how
accurate its predictions would be if it had to deal with political
and economic variables too.
Stress Testing the Models
What was wrong with JPMorgan's risk model for its London
interest rate desk? Besides the fact I just described where it
could not account for many variables of market behavior -- possibly
one like a Greece exit the eurozone -- the model probably had the
wrong assumptions fed into it about all kinds of other variables,
such as the volatility and direction of interest rates.
One thing I started asking last week was this: "How did this get
past the Fed and Treasury earlier this year during the so-called
bank stress tests?" My instinct was simply what I've been talking
about: the models and the math are so complex, that if one flashes
enough numbers and graphs across a screen, a regulator might say
just nod and feign comprehension, saying "Oh, that looks good."
And, I found another part of my answer today. I saw an interview
with Sheila Bair, former chair of the FDIC. She heard that JPM had
actually tweaked their VAR model in and around the time of the
stress tests. She said she'd be asking a lot of questions about
that if she were still involved in protecting and guaranteeing
So this brings us back to the original question, what is an
appropriate and manageable size for a bank? Zacks Chief Equity
Strategist John Blank recently shared with us the "3% of GDP
"In that rule, no bank can have asset/liabilities over 3% of
GDP, which in U.S. terms, is around $50B.
The logic: Lock down the bank's size. From 10 massive banks
we get 60 mid-size banks. So 50 new managements in smaller,
more-focused banks, which, to me, is better risk management.
The new managers can only seek to raise cash flow returns
through a better business mix and loans; which ultimately forces
them back into high-dividend paying defensive stocks; which
stabilizes our now volatile high-beta banking stock sector.
So why haven't you heard of it? Because we went for 2,600
pages of Dodd-Frank regulation instead. If we are a
pro-competitive country, it mystifies me as to why throwing the
3%-of-GDP-Rule under the carpet continues to be tolerated."
Over-Confidence Replaces Probability Knowledge
Derivatives models have given Wall Street a magic genie to
create more leverage and elaborate stories about how things should
work. Unfortunately, even the PhDs in mathematics get seduced by
the beauty of their models and forget it's only pretend.
When the risk managers can simply use a "plug n play" formula to
value the firm's risk and leave it at that... well, we know how
that worked for Bear Stearns, Merrill Lynch, and Lehman.
Whether it's the rogue trader hiding bad bets on a separate
ledger or the giant bank that is "too big to manage"
over-confidence, hubris, and plain old mathematical ignorance can
burn money fast. Often, the trader and the risk manager most
responsible have a weak knowledge of probability and the gearing of
leverage which can get geometric pretty quick. (For the record, I
am not a mathematician.)
Even if one does understand the bell curve, as Taleb showed us
standard deviation is often not robust enough for multi-dimensional
financial markets. This is because markets are full of "wild
randomness." When anything can happen, there is no standard for
In closing, a word about "too big to manage." I worked for a
currency trading desk where I could trade $1-$2 million unhedged
positions. My loss limit for the day was $10,000 (I never hit it
thank goodness), our operation was small and focused, and our
internal risk controls were fantastic, with checks, double-checks,
and triple-checks by risk management staff. I couldn't dream of
hiding a bad trade or open position because it would have been
The JPM debacle is further proof that these giant banks cannot
manage all the wings of all the derivatives beasts they attempt to
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