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 system."
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
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 of dollars.
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 predictions.
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 PowerBall.
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
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 Rule:"
"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
Kevin Cook is a Senior Stock Strategist with
BANK OF AMER CP (BAC): Free Stock Analysis
CITIGROUP INC (C): Free Stock Analysis Report
JPMORGAN CHASE (JPM): Free Stock Analysis
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