The Death of Money (35 page)

Sakakibara is not unaware of the impact of deflation on the real value of debt. The
Japanese debt-to-GDP ratio is mitigated by zero interest rates, which prevent the
debt from compounding rapidly. Most Japanese government debt is owned by the Japanese
themselves, so a foreign financing crisis of the kind that struck Thailand in 1997
and Argentina in 2000 is unlikely. Sakakibara’s most telling point is that Japan’s
growth problems are structural, not cyclical, and therefore cyclical remedies such
as money printing will not work; he sees no chance of Japanese inflation hitting the
2 percent target rate.

Sakakibara’s insights, that monetary remedies will not solve structural problems,
and that real growth is more important than nominal growth, are being ignored by central
banks in both the United States and Japan.
The Federal Reserve and the Bank of Japan will pursue the money-printing pseudoremedy
as far as possible until investors finally lose confidence in their currencies, their
bonds, or both. Japan, the canary, will likely suffer this crisis first.

The Federal Reserve’s supporters ask defensively,
What else could the Fed have done?
If the Fed had not resorted to extraordinary money creation in 2008 and the years
since, it does seem likely that asset prices would have plunged further, unemployment
would have been significantly higher, and GDP growth significantly worse. A sharp
contraction with rising bankruptcies and crashing industrial output, akin to the depression
of 1920, might have resulted. In short, the Fed defenders argue, there really was
no choice except to create money on an unprecedented scale.

In this view, the problems of executing an exit strategy from monetary expansion are
more manageable than the problems of economic depression. Defenders assert that the
Fed took the right path in 2008 and persevered with great skill. This is the mainstream
view that has resulted in the contemporary lore of Bernanke-as-hero, a halo that has
now been transferred to Janet Yellen.

The history of depressions in the United States from 1837 onward supports another
perspective on the Fed’s actions. Under this view, the Fed should have provided only
enough liquidity to mitigate the worst phase of the financial panic in late 2008.
Thereafter the Fed should have capped the amount of excess reserves and normalized
interest rates in a range of 1 to 2 percent. Most of the large banks—including Citibank,
Morgan Stanley, and Goldman Sachs—should have been temporarily nationalized, their
stockholders wiped out, and their bondholders subject to principal reductions as needed
to restore capital. Nonperforming assets could have been stripped from these banks
in receivership, then placed in a long-term government trust, to be liquidated for
the taxpayers’ benefit as circumstances permitted. Management of the banks should
have been fired, while enforcement actions and criminal prosecutions were pursued
against them as the facts warranted. Finally asset prices, particularly housing and
stocks, should have been allowed to fall to much lower levels than were seen in 2009.

In this scenario, bankruptcies and unemployment in 2009–10 would
have been much higher and asset values much lower than what actually occurred. The
year 2009 would have resembled 1920 in the severity of its depression, with skyrocketing
unemployment, collapsing industrial production, and widespread business failure. But
an inflection point would have been reached. The government-owned banks could have
been taken public with clean balance sheets and would have exhibited a new willingness
to lend. Private equity funds would have found productive assets at bargain prices
and begun investing. Abundant labor, with lower unit labor costs, could have been
mobilized to expand productivity, and a robust recovery, rather than a lifeless one,
would have commenced. The depression would have been over by 2010, and real growth
would have been 4 to 5 percent in 2011 and 2012.

The benefit of a severe depression in 2009 is not severity for its own sake. No one
wishes to play out a morality tale involving greedy bankers getting their just deserts.
The point of a severe depression in 2009 is that it would have prompted the structural
adjustments that are needed in the U.S. economy. It would also have diverted assets
from parasitic pursuits in banking toward productive uses in technology and manufacturing.
It would have moved unit labor costs to a new, lower level that would have been globally
competitive when higher U.S. productivity was taken into account. Normalized interest
rates would have rewarded savers and helped strengthen the dollar, making the United
States a magnet for capital flows from around the world. The economy would have been
driven by investment and exports rather than relying on the lending-and-spending consumption
paradigm. Growth composition would have more nearly resembled the 1950s, when consumption
was about 60 percent of GDP, instead of recent decades, when consumption was closer
to 70 percent. These types of healthy, long-term structural adjustments would have
been forced on the U.S. economy by a one-time liquidation of the excesses of debt
and leverage and the grotesque overexpansion of finance.

It is not correct to say the Federal Reserve had no choice in its handling of the
economy at the start of the Depression. It is correct to say, in Tom Friedman’s phrase,
that there was a failure of imagination to see that the economy’s problems were structural,
not cyclical. The Fed applied obsolete general equilibrium models and took a blinkered
view of the structural challenge. Policy makers at the Fed and the Treasury avoided
a
sharp depression in 2009 but created a milder depression that continues today and
will continue indefinitely. Federal Reserve and U.S. Treasury officials and staff
said repeatedly in 2009 that they wanted to avoid Japan’s mistakes in the 1990s. Instead,
they have repeated every one of Japan’s mistakes in their failure to pursue needed
structural changes in labor markets, eliminate zombie banks, cut taxes, and reduce
regulation on the nonfinancial sector. The United States is Japan on a larger scale,
with the same high taxes, low interest rates that penalize savers, labor market rigidities,
and too-big-to-fail banks.

Abenomics and Federal Reserve money printing share a frenzied focus on avoiding deflation,
but the underlying deflation in both Japan and the United States is not anomalous.
It is a valid price signal that the system had too much debt and too much wasted investment
prior to the crash. Japan was overinvested in infrastructure, just as the United States
was overinvested in housing. In both cases, the misallocated capital reached the point
where it had to be written off in order to free up bank balance sheets to make new,
more productive loans. But that isn’t what happened.

Instead, as a result of political corruption and cronyism, regulators in both countries
preserved the ailing balance sheets in amber along with banker job security. The deflationary
price signals were muted with money printing, the same way pain in athletes is masked
with steroids. But the deflation did not go away, and it will never go away until
the structural adjustments are made.

The United States may find false courage in Japan’s apparent success, using its model
as ammunition for evaluating its own QE policies. But the signs in Japan are misleading,
consisting of more money illusion and new asset bubbles. Japan reached the crossroads
first; it opted for Abenomics. The Fed needs to look more critically at Japan’s putative
escape from depression. If it follows the Japanese path, both nations will be headed
for an acute debt crisis. The only difference may be that Japan gets there first.

MAELSTROM

Nobody really understands gold prices, and I don’t pretend to understand them either.

Ben Bernanke

Former Federal Reserve Board chairman

July 18, 2013

I think that, at this time, this global civilisation has gone beyond its limits . . .
because it has created such a cult of money.

Pope Francis

July 26, 2013

■
The Snowflake and the Avalanche

An avalanche is an apt metaphor of financial collapse. Indeed, it is more than a metaphor,
because the systems analysis of an avalanche is identical to the analysis of how one
bank collapse cascades into another.

An avalanche starts with a snowflake that perturbs other snowflakes, which, as momentum
builds, tumble out of control. The snowflake is like a single bank failure, followed
by sequential panic, ending in fired financiers forced to vacate the premises of ruined
Wall Street firms carrying their framed photos and coffee mugs. Both the avalanche
and the bank panic are examples of complex systems undergoing what physicists call
a phase transition: a rapid, unforeseen transformation from a steady state to disintegration,
finally coming to rest in a new state completely unlike the starting place. The dynamics
are the same, as are the recursive mathematical functions used in modeling the processes.
Importantly, the
relationship between the frequency and severity of events as a function of systemic
scale, called degree distribution, is also the same.

In assessing the risk of financial collapse, one should not only envision an avalanche
but study it as well. Complexity theory, first advanced in the early 1960s, is new
as the history of science goes, but it offers striking insights into how complex systems
behave.

Many analysts use the words
complex
and
complicated
interchangeably, but that is inexact. A
complicated
mechanism, like the clockworks on St. Mark’s Square in Venice, may have many moving
parts, but it can be assembled and disassembled in straightforward ways. The parts
do not adapt to one another, and the clock cannot suddenly turn into a sparrow and
fly away. In contrast,
complex
systems sometimes do morph and fly away, or slide down mountains, or ruin nations.
Complex systems include moving parts, called autonomous agents, but they do more than
move. The agents are diverse, connected, interactive, and adaptive. Their diversity
and connectivity can be modeled to a limited extent, but interaction and adaptation
quickly branch into a seeming infinity of outcomes that can be modeled in theory but
not in practice. To put it another way, one can know that bad things might happen
yet never know exactly why.

Clocks, watches, and motors are examples of constrained systems that are complicated
but not complex. Contrast these with ubiquitous complex systems, including earthquakes,
hurricanes, tornadoes—and capital markets. A single human being is a complex system.
One billion human beings engaged in trading stocks, bonds, and derivatives constitute
an immensely complex system that defies comprehension, let alone computation. This
computational challenge does not mean policy makers and risk managers should throw
up their hands or use make-believe models like “value at risk.” Risk management is
possible with the right combination of complexity tools and another essential: humility
about what is knowable.

Consider the avalanche. The climbers and skiers at risk can never know when an avalanche
will start or which snowflake will cause it. But they do know that certain conditions
are more dangerous than others and that precautions are possible. Snow’s wetness or
dryness is carefully observed, as is air temperature and wind speed. Most important,
alpinists
observe the snowpack size, or what physicists call systemic scale. Those in danger
know that a large snowpack can unleash not just a large avalanche but an
exponentially
larger
one. Sensible adaptations include locating villages away from chutes, skiing outside
the slide paths, and climbing ridgelines above the snow. Alpinists can also descale
the snowpack system with dynamite. One cannot predict avalanches, but one can try
to stay safe.

In capital markets, regulators too often do not stay safe; rather, they increase the
danger. Permitting banks to build up derivatives books is like ignoring snow accumulation.
Allowing JPMorgan Chase to grow larger is like building a village directly in the
avalanche path. Using value at risk to measure market danger is like building a ski
lift to the unsteady snowpack with free lift tickets for all. Current financial regulatory
policy is misguided because the risk-management models are unsound. More unsettling
still is the fact that Wall Street executives know the models are unsound but use
them anyway because the models permit higher leverage, bigger profits, and larger
bonuses. The regulators suspect as much but play along, often in the hope of landing
a job with the banks they regulate. Metaphorically speaking, the bankers’ mansions
are high on a ridgeline far from the village, while the villagers, everyday Americans
and citizens around the world, are in the path of the avalanche.

Financial avalanches are goaded by greed, but greed is not a complete explanation.
Bankers’ parasitic behavior, the result of a cultural phase transition, is entirely
characteristic of a society nearing collapse. Wealth is no longer created; it is taken
from others. Parasitic behavior is not confined to bankers; it also infects high government
officials, corporate executives, and the elite societal stratum.

The key to wealth preservation is to understand the complex processes and to seek
shelter from the cascade. Investors are not helpless in the face of elite decadence.

■
Risk, Uncertainty, and Criticality

The prototypical explication of financial risk comes from Frank H. Knight’s seminal
1921 work
Risk, Uncertainty and Profit.
Knight distinguished between
risk,
by which he meant an unknown outcome that can nevertheless be modeled with a degree
of expectation or probability, and
uncertainty,
an unknown outcome that cannot be modeled at all. The poker game Texas hold’em is
an example of risk as Knight used the term. When a card is about to be turned up,
a player does not know in advance what it will be, but he does know with
certainty
that it will be one among fifty-two unique possibilities in one of four suits. As
more cards are turned up, the certainty increases because some outcomes have been
eliminated by prior play. The gambler takes risks but is not dealing with complete
uncertainty.

Now imagine the same game with a player who insists on using “wild cards.” In a wild
card game, any card can be deemed to be any other card by any player to help her make
a high hand like a full house or a straight flush. Technically, this is not complete
Knightian uncertainty, but it comes close. Even the best poker players with superb
computational skills cannot compute the odds of making a hand with wild cards. This
is why professional poker players detest wild card games and amateurs enjoy them.
The wild card is also a good proxy for complexity. Turning the two of clubs into an
ace of spades on a whim is like a phase transition—unpredictable, instantaneous, and
potentially catastrophic if one is on the losing side of the bet.

Knight’s work came forty years before complexity theory emerged, before the advent
of the computer made possible advanced research into randomness and stochastic systems.
His division of the financial landscape into the black-and-white worlds of risk and
uncertainty was useful at the time, but today there are more shades of gray.

Random numbers are those that cannot be predicted but can be assigned values based
on a probability of occurrence over time or in a long series. Coin tosses and playing
cards are familiar examples. It is impossible to know if the next coin toss will be
heads or tails, and you cannot know if the next card in the deck is the ace of spades,
but you can
compute the odds. Stochastic models are those that describe systems based on random
number inputs. Such systems are not deterministic but probabilistic, and when applied
to financial markets, they allow prices and values to be assigned based on the probabilities.
This was Knight’s definition of
risk
. Stochastic systems may include nonlinear functions, or exponents, that cause small
input changes to produce massive changes in results.

Stochastic models are supplemented by integral calculus, which measures quantity,
and differential calculus, which measures change. Regressions, which are backward-looking
associations of one variable to another, allow researchers to correlate certain events.
This taxonomy of random numbers, stochastic systems, nonlinear functions, calculus,
and regression comprises modern finance’s toolkit. The application of this toolkit
to derivatives pricing, value at risk, monetary policy, and economic forecasting takes
practitioners to the cutting edge of economic theory.

Beyond the cutting edge is complexity theory. Complexity has not been warmly embraced
by mainstream economics, in part because it reveals that much economic research for
the past half-century is irrelevant or deeply flawed. Complexity is a quintessential
example of new science overturning old scientific paradigms. Economists’ failure to
embrace the new science of complexity goes some way toward explaining why the market
collapses in 1987, 1998, 2000, and 2008 were both unexpected and more severe than
experts believed possible.

Complexity offers a way to understand the dynamics of feedback loops through recursive
functions. These have so many instantaneous iterations that explosive results may
emerge from minute causes
too small even to be observed
. An example is the atomic bomb. Physicists know that when highly enriched uranium
is engineered into a
critical state
and a neutron generator is applied, a catastrophic explosion will result that can
level a city; but they do
not
know precisely which subatomic particle will start the chain reaction. Modern economists
spend their time looking for the subatomic particle while ignoring the critical state
of the system. They are looking for snowflakes and ignoring the avalanche.

Another formal property of complex systems is that the size of the worst event that
can happen is an exponential function of the system
scale. This means that when a complex system’s size is doubled, the systemic risk
does not double; it may increase by a factor of ten or more. This is why each financial
collapse comes as a “surprise” to bankers and regulators. As systemic scale is increased
by derivatives, systemic risk grows exponentially.

Criticality
in a system means that it is on the knife-edge of collapse. Not every complex system
is in a critical state, as some may be stable or subcritical. One challenge for economists
is that complex systems
not
in the critical state often behave like noncomplex systems, and their stochastic
properties can appear stable and predictable right up to the instant of criticality,
at which point emergent properties manifest and a catastrophe unfolds, too late to
stop. Again, enriched uranium serves as an illustration. A thirty-five-pound block
of uranium shaped as a cube poses no risk. It is a complex system—the subatomic particles
do interact, adapt, and decay—but no catastrophe is imminent. But when the uranium
block is precision engineered in two parts, one the size of a grapefruit and one like
a baseball bat, and the parts are forced together by high explosives, an atomic explosion
results. The system goes from subcritical to critical by engineering.

Complex systems can also go from subcritical to critical spontaneously. They morph
in the same way a caterpillar turns into a butterfly, a process physicists call “self-organized
criticality.” Social systems including capital markets are characterized by such self-organized
criticality. One day the stock market behaves well, and the next day it unexpectedly
collapses. The 22.6 percent one-day stock market crash on Black Monday, October 19,
1987, and the 7 percent fifteen-minute “flash crash” on May 6, 2010, are both examples
of the financial system self-organizing into the critical state; at that point, it
takes one snowflake or one sell order to start the collapse. Of course, it is possible
to go back after the fact and find a particular sell order that, supposedly, started
the market crash (an example of hunting for snowflakes). But the sell order is irrelevant.
What matters is the system state.

■
Gold Games

Central bank gold market manipulation is an example of action in a complex system
that can cause the system to reach the critical state.

That central banks intervene in gold markets is neither new nor surprising. To the
extent that gold is money, and central banks control money, then central banks must
control gold. Prior to gold’s partial demonetization in the mid-1970s, central bank
involvement in gold markets was arguably not manipulative but a matter of policy,
although the policy was conducted nontransparently.

In the post–Bretton Woods era, there have been numerous well-documented central bank
gold market manipulations. In 1975 Federal Reserve chairman Arthur Burns wrote a secret
memorandum to President Gerald Ford that stated:

The broad question is whether central banks and governments should be free to buy
gold . . . at market-related prices. . . . The Federal Reserve is opposed. . . .

Early removal of the present restraints on . . . official purchases from the private
market could well release forces and induce actions that would increase the relative
importance of gold in the monetary system. . . .

Such freedom would provide an incentive for governments to revalue their official
gold holdings at a market-related price. . . . Liquidity creation of such extraordinary
magnitude would seriously endanger, perhaps even frustrate our efforts . . . to get
inflation under control. . . .