Harry Newton's In Search of The Perfect Investment
Newton's In Search Of The Perfect Investment. Technology Investor.
8:30 AM EST, Friday, October 19, 2007:
The 20th anniversary of Black Monday, the day
the Dow fell 22%. That horrid day started with Wells Fargo trying to sell $2
billion of equities and it spiraled downwards as computers programmed to sell
on downward ticks took over and started selling. Then selling by individuals.
Then panic. Three things caused the downturn:
The lack of information. Selling orders came so fast, the Exchange's computers
couldn't keep up. No one knew what the bid and ask prices were. Without information,
one assumes the worst. Hence, more panic.
As prices plummeted, margin calls went out. Many people walked from their margin
calls, saying, "I can't meet them. I'm bankrupt." Many were.
The computers kept selling.
is different. There are limits. When the Dow is down a certain amount (I think
it's 200), computers are barred from trading. And the exchanges' computers work
better -- at least theoretically.
ever precludes the market taking a big hit. We could be ripe for one. Oil is
way up, on its way to $100. The dollar is way down, on its way to parity with
the Australian dollar. And the budget deficit remains horrendous.
no one can predict this stuff.
one lesson: don't use margin.
of 1987 have been replaced by the "quants" of 2007.
In Wall Street's
summer of scary numbers, all eyes have been on the mathematically trained financial
engineers known as "quants." These guys are now the major force in
world stockmarkets. Some are rich (way beyond your wildest imagination); some
(very few) are poor. But all are ultrasecretive. A wonderful writer called Bryant
Urstadt got inside their brains and published a two-part serious in a super
Here is Urstadt's
two-part series on quants.
August 8, not long after the markets closed, 200 of the smartest people on
Wall Street gathered in a conference room at Four World Financial Center,
the 34-story headquarters of Merrill Lynch. August is usually a slow month,
but the rows of chairs were full, and highly paid financial engineers were
standing by the windows at the back, which looked out over black Town Cars
below and the Hudson River beyond. They didn't look like Masters of the Universe;
they looked like members of a chess club. They were "quants," and
they had a lot to talk about, for their work was at the heart of one of the
most worrisome summer markets in decades.
was sponsored by the International Association of Financial Engineers (IAFE),
and its title asked, "Is Subprime the Canary in the Mine?"
"Subprime" borrowers are home buyers whose poor credit history means
they don't qualify for market interest rates. Loans to subprime borrowers,
which have become more common in recent years, typically have variable interest
rates; as those rates rose, many borrowers were failing to meet their mortgage
payments. Their defaults, in turn, had triggered unexpected problems in the
market for financial instruments known as derivatives.
is a tradable product whose value is based on, or "derived" from,
an underlying security. The classic example of a derivative is the option
to buy a stock at some time in the future. In comparison, more recent derivatives
are extraordinarily complex, and they had been invented by quants like the
ones at the Merrill Lynch headquarters.
Things had started
to go wrong in June, when the weakness in the subprime market had led to the
collapse of two huge funds at the investment bank Bear Stearns, costing investors
some $1.6 billion. When the quants gathered in August, the most pessimistic
among them imagined that the collapse of the subprime market could lead to
a shortage of credit as banks dealt with defaults. That would chill the economy,
causing worldwide job losses, still more defaults, decreased spending, and
withdrawals from the stock market, culminating in a global recession, or worse.
The panel was
moderated by Leslie Rahl, an MIT graduate and the founder of Capital Market
Risk Advisors. Her job is to advise companies on risk and help them understand
the products quants invent. But understanding was in short supply in August.
Some of the quants' financial products had collapsed in price, with unexpected
consequences in another financial sector: the trading of equities.
The stock market
had plunged in July and had been behaving erratically since. In the weeks
after the conference, an organizing narrative of sorts would develop. But
at the time, the economic view was dizzying. The market would drop precipitously
over the course of a day, then rebound nearly to its previous level in the
last 45 minutes of trading. Stranger still, stocks with strong financial reports
and a good outlook were falling; these were the blue chips, which normally
rose in uncertain times. Stocks with weak financials and a gray future were
rising. These were normally the dogs that got dumped.
No one quite
knew why, yet, but the market's odd behavior would turn out to be closely
linked to the work of the quants. In addition to creating arcane financial
products, quants have been pushing the frontiers of computer-driven trading
systems, and not enough of those systems were working the way they were supposed
to--or, to put it more precisely, the way they were supposed to work turned
out to be counterproductive in volatile times like these.
the ones at the August conference were knee deep in the troubles threatening
the global financial system. It all raised two very good questions: Who exactly
are the quants? And what do they really do?
is an elastic word that has meant different things at different times. Historically,
the term referred to back-room technicians who used quantitative analysis
to support the bankers who sold financial instruments. It came into wider
use in the 1980s, when academics--pure mathematicians and physicists, mostly--began
to appear in the financial world in larger numbers. Classic geeks, the newcomers
were at first treated as déclassé immigrants by the financial
establishment. Emanuel Derman was a theoretical physicist at Columbia University
before he joined Goldman Sachs in 1985, and he remembers in his fine memoir
My Life as a Quant when "it was bad taste for two consenting adults
to talk math or Unix or C in the company of traders, salespeople, and bankers."
But success lent the quants credibility. What was at first a disdainful term
was cheerfully embraced by those whom it was originally meant to insult. It
finally came to encompass a larger group of people, including, most broadly,
anyone involved in mathematical or computational finance. In this article,
the word "quant" refers to any practitioner of quantitative finance,
a wide-ranging discipline that includes, among other things, the pricing of
financial instruments, the evaluation of risk, and the search for exploitable
patterns in market data.
A quant sees
the financial world through a mathematical lens. This does not necessarily
describe the average Wall Street salesperson or trader, whose success is often
based as much on intuition and, maybe more important, connections and personal
charisma as on any understanding of a topic like stochastic calculus. To give
some idea of how far the quant mind is from that of the typical financier,
stochastic calculus--a branch of mathematics dealing with randomness--is sometimes
derided by quants as "folk math." The quant, unlike his slicker
counterpart, seeks to understand and profit from the markets on a purely numerical
basis. Or as Herbert Blank, a quant who devises algorithms for evaluating
the financial health of companies, says, "If you think you can find
out what you need to know by going to see the management of a company, then
I have nothing to say to you."
If quants in
one guise or another have been around for a while, they have also made trouble
before. The hedge fund Long-Term Capital Management, which collapsed in August
1998, boasted some of the founders of the field among its directors and officers.
Nonetheless, in recent years, quants' numbers and influence have grown. Over-the-counter
derivatives, such as the ones at the heart of the subprime crisis, have become
more popular, fueling a boom in lending by making loans easier to trade. The
value of over-the-counter derivatives, one shorthand measure of activity in
the market, went from $298 trillion in December 2005 to $415 trillion a year
later, according to statistics kept by the Bank for International Settlements.
By some measures, the money invested in two of the most common types of quant
funds has grown 60 percent in the last two years (including both expanding
assets and new investments), and the funds have generated some of the highest
returns in the financial industry.
among the industry's most mysterious organizations. Firms that keep their
methods secret are known as "black boxes," and the quant-driven
hedge funds are as black as any. It is not unusual for billions of dollars
to be invested in such firms with little revealed except the results. Previous
results, though, can be a powerful incentive for giving money to someone who
won't tell you what he's going to do with it. A case in point is James Simons's
Renaissance Technologies, which has earned an average of more than 30 percent
a year since its founding in 1988. Like other quant funds, it is ferociously
secretive. Still, so many investors have trusted Simons that the two funds
under his management now total more than $30 billion. In 2006 alone, he earned
$1.7 billion running the fund.
The press often
refers to Simons as the world's leading quant. A world-class mathematician
with a PhD from the University of California, Berkeley, he spent years in
academia, making significant contributions to mathematics. He worked primarily
in geometry and in a subfield called differential geometry, where his most
prominent contribution was the Chern-Simons theory, a topological description
of quantum field behavior that has been useful to string theorists. Many of
his employees have backgrounds in physics, astronomy, and mathematics.
The quants of
Renaissance Technologies are unusual in that many might have enjoyed significant
careers in academia. But quants of a less exalted sort are becoming ubiquitous
at financial institutions. There are quants at investment banks, developing
new loan structures. There are quants at hedge funds, crunching years of market
data to develop trading algorithms that computers execute in milliseconds.
And there are more and more quants at pension funds, trying to understand
and value the tools created by the banking quants, and trying to evaluate
the methods of the investing quants.
used to send our graduates mainly to the big banks," says Andrew
Lo, the director of MIT's Laboratory for Financial Engineering, where many
quants are trained. "Now they're going everywhere, to pension funds,
insurance companies, and companies that aren't finance companies at all."
MIT's lab was founded in 1992, one of a host of academic programs in the discipline
that have sprung up on campuses around the United States and abroad; a new
institute at the University of Oxford is one of the most recent additions.
"Financial markets and investment processes are becoming more quant
across the board," says Lo.
who they were and what they were doing, I spoke with current and former quants,
on and off the record. Many would speak happily and at length. Others spoke
guardedly or anonymously--especially those using proprietary analysis and
algorithms to conduct trades. I read memoirs of quants--a recently expanding
genre--and dipped into an introductory textbook for quants, Paul Wilmott
Introduces Quantitative Finance, a 722-page condensation of the author's
1,500-page, three-volume anvil of a book, Paul Wilmott on Quantitative
Finance. And I went to a quant drinking party, which convened in the basement
of a pub next to Grand Central Station. The name of that event proves, as
much as anything, that the quants have geek in their veins: it was the August
meeting of the New York chapter of the Quantitative Work Alliance for Applied
Finance, Education, and Wisdom, or QWAFAFEW.
were simpler once, they were never very simple. The breakthrough in the valuation
of derivatives in general, and options in particular, was the model and formula
know as Black-Scholes, first proposed by Fischer Black and Myron Scholes in
the 1970s and formalized by Robert Merton in 1973. (Merton, like so many of
the best quants, came not out of Wall Street but out of academia, earning
a PhD in economics from MIT in 1970.)
finance, the formal expression of Black-Scholes by Robert Merton is so important
that everything that followed has been called a "footnote." The
Black-Scholes model assumes that a stock's price changes partly for predictable
reasons and partly because of random events; the random element is called
the stock's "volatility." The idea can be represented mathematically
by a simple equation:
St is the value
of the stock, and dSt is the change in stock price. The symbol µStdt
represents the stock's predictable change and its volatility. That final,
kabbalistic combination of letters, dWt, is the mathematical expression for
randomness, known as either Brownian motion or the Wiener process. (Chemically,
Brownian motion is the random movement of particles in solution, identified
by the botanist Robert Brown in 1828 and mathematically described by the great
MIT mathematician Norbert Wiener. Black-Scholes shares some qualities with
heat and diffusion equations, which describe everyday events like the flow
of heat and the dispersion of populations. That some physical processes seem
relevant to finance has inspired all kinds of far-out work, such as efforts
to bend general relativity to a theory of finance.) Black-Scholes prices an
option according to the amount of randomness in a stock's price; the greater
the randomness, the higher the stock could climb, and thus the more expensive
since refined Black-Scholes, and with the increasing power of computers, they
have developed other, more processing-intensive methods of valuing derivatives.
In Monte Carlo simulations, for instance, powerful computers model the performance
of a stock millions of times and then average the results. Where Black-Scholes,
as a mathematical shortcut, assigns a constant value to a stock's volatility,
Monte Carlo simulations vary the volatility itself. In theory, this provides
a better approximation of price fluctuations in the real world. And quants
have devised yet more arcane methods of derivatives pricing. Some particularly
complicated models track other economic factors--like the stock market as
a whole, or even larger macroeconomic factors--in addition to a stock's price.
computationally intensive simulations has become a lot easier in the last
decade. Gregg Berman, a former experimental astrophysicist who left the academy
for the world of finance in 1993, is one of what he calls "a plethora
of PhDs" at RiskMetrics, a firm that provides models, tools, and data
to the majority of important banks, brokerages, and hedge funds. (Among other
things, the company tries to predict how a derivative will behave in a variety
of market conditions--how it might respond, for instance, to weakening exchange
rates or increased interest rates.) When Berman started in the business, he
says, "full-blown simulations [of the Monte Carlo type] were rare."
Now that computers can be so easily linked, however, Berman might put as many
as 1,000 processors to work at once to run "simulations within simulations,"
which might measure risk on a product like a mortgage-backed security.
The net result
of this improved ability to assign values to increasingly complex derivatives
was an explosion in their variety. That meant there was a derivative to suit
every investor's appetite for risk. In consequence, investors were increasingly
willing to put more money into derivatives.
of the most popular of these new instruments has been collateralized debt
obligations, or CDOs. Crucially for our story, CDOs are also the product most
closely associated with the summer's subprime mess. The CDO has been called
a "derivative of a derivative," and to further confuse things, there
are CDOs of CDOs, and even CDOs of CDOs of CDOs. A CDO combines both high-
and low-risk securities that might derive their cash flow from mortgages,
car loans, or more esoteric sources like movie revenues or airplane leases.
Investors in a CDO can buy the rights to different levels of income and associated
risk, called "tranches." Generally, the most risky tranche of a
CDO pays the most income. Created by quants and priced by quants, CDOs have
become a popular way for hedge funds, pension funds, insurance companies,
and other investors to buy pieces of high-risk but high-profit sectors like
subprime loans. According to the Securities Industry and Financial Markets
Association, annual issues of CDOs worldwide nearly doubled between 2005 and
2006, going from $249.3 billion to $488.6 billion.
The quants who
devise such derivatives work more or less in public view. They're obscured
mainly by the complexity of their work. But our knowledge of the quants who
design trading strategies is additionally occluded by the secrecy of the big
fund operators like Renaissance Technologies. I did manage to speak with some
current traders, who gave me a general idea of their approach, and with some
ex-traders, who were slightly more specific.
One common method
that quants use to identify market opportunities is pairs trading. Pairs trading
involves trying to find securities that rise in tandem, or that tend to go
in opposite directions. If that relationship falters--if, say, the values
of two stocks that travel together suddenly diverge--it's likely to indicate
that one stock is undervalued or overvalued. Which stock is which is irrelevant:
a trader who simultaneously bets that one will go up and the other one down
will probably make money. It's a strategy that lends itself to the use of
computers, which can sort through huge numbers of price correlations over
many years of stored data--although the final decision to speculate on the
relative pricing of paired stocks generally rests with a fund's managers.
also been pursuing a strategy known as "capital structure arbitrage,"
which seeks to exploit inefficient pricing of a company's bonds versus its
stocks. Again, computers do the searching, looking for instances where, for
one reason or another, the securities are slightly misaligned.
In a similar
technique, Max Kogler, a principal at the newly launched MM Capital in New
York, uses computers to look for inconsistencies in value between the option
on an index fund and the options on the stocks that compose that index. Kogler
has a master's from the University of Cambridge in pure mathematics with a
focus on statistics. He says his algorithms look for "baskets of options
that are not doing what they're supposed to be doing." When his computers
find such a basket, he and his partners discuss whether or not to buy.
his algorithms on "one Linux box." "Part of the allure of our
algorithm," he said in an e-mail, "is that it cuts down computational
requirements dramatically. Nonetheless, you'll want to have a speedy machine
with pretty decent clock speed and a couple of parallel CPUs."
In what's called
nondiscretionary trading, computers both find the inefficiencies and execute
the trades. The Aite Group, a financial-services research firm, estimates
that roughly 38 percent of all equities may be traded automatically, a number
it expects to increase to 53 percent in three years.
underlie another developing frontier, high-frequency trading, which is a fantastically
exaggerated form of day trading. The computer looks for patterns and inefficiencies
over minutes or seconds rather than hours or days. An algorithm, for instance,
might look for patterns in trading while the Japanese are at lunch, or in
the moments before an important announcement. There is a massive amount of
such data to crunch. Olsen Financial Technologies, a Zürich-based firm
that offers data for sale, says it collects as many as a million price updates
One trader I
spoke with at a $10 billion hedge fund based in New York said that his computer
executed 1,000 to 1,500 trades daily (although he noted that they were not
what he called "intra-day" trades). His inch-thick employment contract
precluded my using his name, but he did talk a little bit about his approach.
"Our system has a touch of genetic theory and a touch of physics,"
he said. By genetic theory, he meant that his computer generates algorithms
randomly, in the same way that genes randomly mutate. He then tests the algorithms
against historical data to see if they work. He loves the challenge of cracking
the behavior of something as complex as a market; as he put it, "It's
like I'm trying to compute the universe." Like most quants, the trader
professed disdain for the "sixth sense" of the traditional
trader, as well as for old-fashioned analysts who spent time interviewing
executives and evaluating a company's "story."
trading is likely to become more common as the New York Stock Exchange gets
closer and closer to a fully automated system. Already, 1,500 trades a day
is conservative; the computers of some high-frequency traders execute hundreds
of thousands of trades every day.
high-frequency trading is the developing science of event processing, in which
the computer reads, interprets, and acts upon the news. A trade in response
to an FDA announcement, for example, could be made in milliseconds. Capitalizing
on this trend, Reuters recently introduced a service called Reuters NewsScope
Archive, which tags Reuters-issued articles with digital IDs so that an article
can be downloaded, analyzed for useful information, and acted upon almost
All this works
great, until it doesn't. "Everything falls apart when you're dealing
with an outlier event," says the trader at the $10 billion fund,
using a statistician's term for those events that exist at the farthest reaches
of probability. "It's easy to misjudge your results when you're successful.
Those one-in-a-hundred events can easily happen twice a year."
Part II: The
How the financial engineers known as "quants" contributed to Wall
Street's summer of scary numbers.
The events of
August were outliers, and they were of the quants' own making. (Some dispute
that verdict: see "On
Quants.") To begin with, quants were indirectly responsible for
the boom in housing loans offered to shaky candidates.
allow banks to trade their mortgages like bubble-gum cards, and the separation
of the holder of a loan from the writer of a loan tended to create an overgenerous
breed of loan officer. The banks, in turn, were attracted by the enormous
market for derivatives like CDOs. That market was fueled by hedge funds' appetite
for products that were a little riskier and would thus produce a higher return.
And the quants who specialized in risk assessment abetted the decision to
buy CDOs, because they assumed that the credit market would enjoy nine or
so years of relatively benign volatility.
It was a perfectly
rational assumption; it just happened to be wrong. Matthew Rothman, a senior
analyst in quantitative strategies at Lehman Brothers, called the summer a
time of "significant abnormal performance"; according to his calculations,
it was the strangest in 45 years. James Simons's Renaissance Technologies
fund slid 8.7 percent in the first week of August, and in a letter to his
investors, he called it a "most unusual period." As Andrew Lo put
it, "Unfortunately, life has gotten very interesting." The Wall
Street Journal called it an "August ambush."
The damage quickly
spread beyond the market for low-quality debt instruments. It was almost as
if the financial world had become a market for nothing so much as standard
deviations, the mathematical term for the spread of values straying from a
mean. In fact, the summer might be described as a time when too many investors
had purchased standard deviations that were too high for their means.
Among the lessons
that August taught is that there may be a finite number of viable investing
strategies--a suspicion borne out by the oddly synchronous decline of many
quant funds this summer, including Simons's Renaissance Technologies. August's
bizarre market behavior, according to Rothman and others, was probably the
product of some large hedge funds' seeking cash to meet their debt obligations,
as the value of their CDOs declined, by selling those securities that were
easiest to shed, chiefly stocks. (And which funds? In another example of the
secrecy of fund managers, no one really seems to know, or wants to say.)
most of those to whom I spoke, something like the following occurred this
summer. Quants had, in the ordinary nature of their jobs, "shorted"
many stocks. Shorting is an arrangement whereby an investor borrows a stock
from a broker, guaranteeing the loan with collateral assets placed in what
is called a margin account. The investor straightaway sells the borrowed stock;
if the stock then declines in value, the investor buys it back and pockets
the difference in price when he returns the stock to the broker. But if the
stock unexpectedly increases in value, even for a little while, the investor
must either place additional collateral in the margin account to cover the
difference or buy back the shorted stock and return it to the broker.
CDOs had functioned
as the collateral on the quants' short positions. When the subprime crunch
squeezed the financial markets, the value of those CDOs declined, forcing
quants to increase the collateral in margin accounts, buy back the shorted
stocks, or both. But in either case, in order to supplement their shrinking
collateral, quant funds were forced to sell strong blue-chip stocks, whose
prices consequently fell. At the same time, as quants bought back shorted
stocks, the prices of those stocks increased, demanding the posting of yet
more collateral to margin accounts at the very time that the value of CDOs
was suffering. That the quants were, apparently, long on the same strong stocks
and short on the same weak stocks was a result of a number of strategies,
pairs trading among them.
explanation for the August downturn was that the quants' models simply ceased
to reflect reality as market conditions abruptly changed. After all, a trading
algorithm is only as good as its model. Unfortunately for quants, the life
span of an algorithm is getting shorter. Before he was at RiskMetrics, Gregg
Berman created commoditytrading systems at the Mint Investment Management
Group. In the mid-1990s, he says, a good algorithm might trade successfully
for three or four years. But the half-life of an algorithm's viability, he
says, has been coming down, as more quants join the markets, as computers
get faster and able to crunch more data, and as more data becomes available.
Berman thinks two or three months might be the limit now, and he expects it
Bookstaber, a quant who has managed hedge funds and risk for companies like
Salomon Brothers and Morgan Stanley, the August downturn proved that concerns
he'd long harbored were well founded. Bookstaber was on the panel sponsored
by the IAFE; in fact, he is everywhere these days. His book A Demon
of Our Own Design, which appeared in April, was eight years in the
making, and it made some very prescient predictions.
a quiet, thoughtful man, with sharp brown eyes and an attentive look. He studied
with Merton in the 1970s at MIT, where he got his doctorate in economics.
Today, he is very worried about the tools and the methods of the quants. In
particular, he frets about complexity and what he calls "tight coupling,"
an engineer's term for systems in which small errors can compound quickly,
as they do in nuclear plants. The quants' tools, he feels, have became so
complicated that they have escaped their creators. "We have gotten to
the point where even professionals may not understand the instruments,"
he says. This, to Bookstaber, was perfectly demonstrated this summer, when
the subprime troubles touched off a reactionary wave of selling in equities
that would nominally seem unrelated, or, as Wall Street puts it, "uncorrelated."
knew that what happened in the subprime market could affect what was going
on in the quant equity funds," he says. "There's too much complexity,
too much derivative innovation. These are the brightest people in the business.
If it could happen to them, it could happen to anyone. No one could have predicted
Linkage is one
of Bookstaber's favorite topics. He believes that quants' instruments have
"linked markets together that wouldn't normally be linked,"
and that such linkages are dangerous because they are unforeseen.
Berman and others
I spoke to agreed with many of Bookstaber's concerns. "The products
are getting an order of magnitude more complex," says Berman. "Things
change slightly, and get correlated where they weren't correlated before."
Or, as he put it a little less gnomically, "You can't make it without
understanding it, but you can buy it."
this beats the great hope of the quants: namely, that the financial world
can be understood through math. They have tried to discover the underlying
structures of financial markets, much as academics have unlocked the mysteries
of the physical world. The more quants learn, however, the farther away a
unified theory of finance seems. Human behavior, as manifested in the financial
markets, simply resists quantification, at least for now.
remembers dreaming of such a unified financial theory in the early 1990s,
a little after he had made the leap from the university to the Street. But
those dreams, he says, are dead. Quantitative finance "superficially
resembles physics," he says, "but the efficacy is very different.
In physics, you can do things to 10 significant figures and get the right
answer. In finance, you're lucky if you can tell up from down."
So up was down
and down was up this summer, and Bookstaber and others hope it is a warning
that will be heeded, rather than the beginning of a major systemic crisis.
And was subprime
the canary in the mine? It was a question the panelists and the audience who
showed up at Four World Financial Center last August are only beginning to
answer. Leslie Rahl, for instance, cautiously told me in a follow-up email
that it is "looking more and more like the answer is yes."
Many signs have suggested so, from job losses to a continuing credit drought
to a weakening dollar, but that history has not yet been written.
As a prelude
to the panel discussion, Rahl asked the audience to predict whether credit
spreads would shrink or widen in the coming months. She was talking about
the difference between the price of a treasury bond and the price of a riskier
corporate bond, a standard Wall Street gauge for the health of the economy.
A widening credit spread is generally seen as a sign of uncertainty, and a
narrow spread as a sign of optimism.
many think spreads will widen?" she asked.
The hands of
about half the smartest people on Wall Street shot up.
how many think they'll narrow?"
The other half--equally
smart--raised their hands.
she said. "That's what makes a market."
If they didn't
know, nobody could.
shortcuts now fixed.
Sorry about that. My son's best Excel shortcuts
now work. Click
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This column is about my personal search
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