The Death of Money

by James Rickards

  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.

  CHAPTER 11

  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 exa
mple 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. . . .