This comfort factor becomes embedded when there is mathematical modeling to support it. Modern financial math is daunting. Ph.D.s who spent years mastering the math have a vested interest in maintaining a façade. The math bolsters their credentials and excludes others less fluent with Ito’s calculus.
Financial math is also what practitioners call elegant. If you accept the modern finance paradigm, the math provides a wealth of neat solutions to difficult problems such as options pricing. No one stops to question the paradigm.
This financial façade is reinforced by the tyranny of academic advancement. A young scholar in a highly selective finance program is rightly concerned with fellowships, publication, and faculty appointment. Approaching a sexagenarian thesis adviser with an abstract that refutes what the adviser has held dear for decades is not an astute career move. Most deem it better to bang out the thousandth variation of a dynamic stochastic general equilibrium model using autoregressive conditional heteroscedasticity to explain the impact of quantitative easing on swap spreads. That’s the way to get ahead.
Then there is simple inertia, like staying in a warm bed on a cold morning. Academics have their comfort zones too. New knowledge is like a dive in the surf in winter—bracing, exhilarating, but not everyone’s cup of tea.
The preference for certainty over uncertainty, the allure of elegant mathematics, the close-minded academic mentality, and inertia are good explanations for why flawed paradigms persist.
If academic reputations were the only stakes, the world could be patient. Good science wins in the end. Still, the stakes are higher. The world’s wealth is at risk. When wealth is destroyed, social unrest follows. Investors can no longer indulge policymakers who refuse to seek better solutions for the sake of what is tried and not-so-true.
This book is about what works. Since the 1960s, new branches of science have been revealed. Since the 1980s, cheap computing power has allowed laboratory experimentation on economic hypotheses that cannot be tested in real-world conditions. The rise of team science, long common in medicine, facilitates interdisciplinary discoveries beyond the boundaries of any one area of expertise. Recently, a 250-year-old theorem, scorned for centuries, triumphantly reemerged to solve otherwise unsolvable problems.
The three most important new tools in the finance toolkit are behavioral psychology, complexity theory, and causal inference. These tools can be used separately to solve a particular problem or combined to build more robust models.
All three tools seem more inexact in their predictive power than current models used by central banks. Yet they offer a far better reflection of reality. It is better to be roughly right than exactly wrong.
Behavioral psychology is understood and embraced by economists. The leading theorist in behavioral psychology, Daniel Kahneman, received the Nobel Prize in economics in 2002. The impediment to the use of psychology in economics is not appreciation, it’s application. Finance models such as VaR are still based on rational behavior and efficient markets, long after Kahneman and his colleagues proved that human behavior in markets is irrational and inefficient (as economists define these terms).
For example, Kahneman’s experiments show that when subjects are given a choice between receiving $3.00 with 100 percent certainty and $4.00 with 80 percent certainty, they greatly favor the first choice. Simple multiplication shows the second choice has a higher expected return than the first, $3.20 compared with $3.00. Still, everyday people prefer the sure thing to the risky choice, which has a higher expected return, but leaves some chance of coming away empty-handed.
Economists were quick to brand the first choice as irrational and the second choice as rational. This led to the claim that investors who favor the first choice were irrational. But are they really?
It is true that if you play this game one hundred times, the choice of $4.00 with an 80 percent probability almost certainly produces more winnings than the $3.00 sure thing. What if you play the game only once? The expected return equations are the same. But if you need the money, the $3.00 sure thing has independent value not captured in the equations.
What Kahneman discovered must be combined with evolutionary psychology to redefine rationality. Imagine you are a Cro-Magnon during the last ice age. You leave your shelter and see two paths to hunt game. One path has plentiful game, but large boulders along the way. The second path has less game, but no obstacles. In modern financial parlance, the first path has a higher expected return.
Yet evolution favors the path with less game. Why? There could be a saber-toothed cat behind one of the boulders on the first path. If there is, you die and your family starves. The path with less game is not irrational when all costs are considered. The saber-toothed cat is the missing mammal of modern economics. Academics typically quantify first-order benefits (the game) and ignore second-order costs (the cat). Investors can use this book to see the saber-toothed cats.
The second new tool in the toolkit is complexity theory. The crucial question in economics today is whether capital markets are complex systems. If the answer is yes, then every equilibrium model used in financial economics is obsolete.
Physics provides a way to answer the question. A dynamic, complex system is composed of autonomous agents. What are the attributes of autonomous agents in a complex system? Broadly, there are four: diversity, connectedness, interaction, and adaptation. A system whose agents exhibit these attributes in low measure tends toward stasis. A system whose agents exhibit these attributes in high measure tends toward chaos. A system whose agents have all four traits in Goldilocks measure, not too high and not too low, is a complex dynamic system.
Diversity in capital markets is seen in the behavior of bulls and bears, longs and shorts, fear and greed. Diversity of behavior is the quintessence of markets.
Connectedness in capital markets is also manifest. With the use of Dow Jones, Thomson Reuters, Bloomberg, Fox Business, email, chat, text, Twitter, and telephone, it is difficult to imagine a more densely connected system than capital markets.
Interaction in capital markets is measured by trillions of dollars of stock, bond, currency, commodity, and derivatives transactions executed daily, each of which involves a buyer, seller, broker, or exchange interacting. No other social system comes close to capital markets in interaction measured by transaction volume.
Adaptation is also characteristic of capital markets. A hedge fund that loses money on a position quickly adapts its behavior to get out of the trade or perhaps double down. The fund changes its behavior based on the behavior of others in the market as revealed by market prices.
Capital markets are demonstrably complex systems; capital markets are complex systems nonpareil.
The failing of prevailing risk models is that complex systems behave in a completely different manner from equilibrium systems. This is why central bank and Wall Street equilibrium models produce consistently weak results in forecasting and risk management. Every analysis starts with the same data. Yet when you enter that data into a deficient model, you get deficient output. Investors who use complexity theory can leave mainstream analysis behind and get better forecasting results.
The third tool in addition to behavioral psychology and complexity theory is Bayesian statistics, a branch of etiology also referred to as causal inference. Both terms derive from Bayes’ theorem, an equation first described by Thomas Bayes and published posthumously in 1763. A version of the theorem was elaborated independently and more formally by the French mathematician Pierre-Simon Laplace in 1774. Laplace continued work on the theorem in subsequent decades. Twentieth-century statisticians have developed more rigorous forms.
Normal science including economics assembles massive data sets and uses deductive methods to derive testable hypotheses from the data. These hypotheses often involve correlations and regressions used to forecast future events deemed likely to resemble past events. Similar methods involve the us
e of stochastics, or random numbers, to run Monte Carlo simulations, which are high-output versions of coin tosses and dice rolls, to infer the likelihood of future events.
What if there are no data, or little data to start? How do you estimate the likelihood of a secret accord among a small group of central bankers? Bayesian probability provides the means to do just that.
Mainstream economists assume the future resembles the past within certain bounds defined by random distributions. Bayes’ theorem stands this view on its head. Bayesian probability posits that certain events are path dependent. This means some future events are not independent like random coin tosses. They are influenced by what precedes them. Bayes’ theorem begins with a sound prior hypothesis formed inductively from a mixture of scarce data, history, and common sense.
Bayesian probability is solid science, not mere guesswork, because the prior hypothesis is tested by subsequent data. New data tend either to confirm or to refute the hypothesis. The ratio of the two types of data is updated continually as new data arrive. Based on the updated ratio, the hypothesis is either discarded (and a new hypothesis formed) or accepted with greater confidence. In brief, Bayes’ theorem is how you solve a problem when there is not enough initial data to satisfy the demands of normal statistics.
Economists reject Bayesian probability because of the grubby guesswork in the initial stages. Yet it is used extensively by intelligence agencies around the world. I encountered analysts using Bayesian probability in classified settings at the CIA and Los Alamos National Laboratory. When your task is to forecast the next 9/11 attack, you can’t wait for fifty more attacks to build up your data set. You work the problem immediately using whatever data you have.
At the CIA, the potential to apply Bayesian probability to forecasting in capital markets was obvious. Intelligence analysis involves forecasting events based on scarce information. If information were plentiful, you would not need spies. Investors face the same problem in allocating portfolios among asset classes. They lack sufficient information as prescribed by normal statistical methods. By the time they do have enough data to achieve certainty, the opportunity to profit has been lost.
Bayes’ theorem is messy, but still it’s better than nothing. It’s also better than Wall Street regressions that miss the new and unforeseen. This book explains how to use Bayesian probability to achieve better forecasting results than the Federal Reserve or International Monetary Fund.
This book parts ways with the “Big Four” schools—classical, Austrian, Keynesian, and monetarist. Of course, all have much to offer.
Classical economists including Smith, Ricardo, Mill, and Bentham, among others, appeal in part because none of them had Ph.D.s. They were lawyers, writers, and philosophers who thought hard about what works and what does not in the economies of states and societies. They lacked modern computational tools, yet were filled with insights into human nature.
Austrians made invaluable contributions to the study of choice and markets. Yet their emphasis on the explanatory power of money seems narrow. Money matters, but an emphasis on money to the exclusion of psychology is a fatal flaw.
Keynesian and monetarist schools have lately merged into the neoliberal consensus, a nightmarish surf and turf presenting the worst of both.
In this book, I write as a theorist using complexity theory, Bayesian statistics, and behavioral psychology to study economics. That approach is unique and not yet a “school” of economic thought. This book also uses one other device—history. When asked to identify which established school of economic thought I find most useful, my reply is Historical.
Notable writers of the Historical school include the liberal Walter Bagehot, the Communist Karl Marx, and the conservative Austrian-Catholic Joseph A. Schumpeter. Adherence to the Historical school does not make you a liberal, a Communist, or an Austrian. It means you consider economic activity to be culturally derived human activity.
Homo economicus does not exist in the natural world. There are Germans, Russians, Greeks, Americans, and Chinese. There are rich and poor, or what Marx called bourgeoisie and proletarians. There is diversity. Americans are averse to discussion of class, and soft-pedal concepts like bourgeoisie and proletariat. Nevertheless, integration of class culture with economics is revealing.
This book will follow these threads—complexity, behavioral psychology, causal inference, and history—through the dense web of twenty-first-century capital markets into a future unlike anything the world has ever seen.
CHAPTER 1
THIS IS THE END
Nice, nice, very nice—
So many different people
In the same device.
From Cat’s Cradle, a novel by Kurt Vonnegut, 1963
The Conversation
Aureole is an elegant, high-ceilinged restaurant of sleek modern design on West Forty-second Street in Manhattan. It sits midway between tourist throngs in Times Square and Bryant Park’s greenery. The neoclassical New York Public Library, whose entrance is attended by the twin marble lions, Patience and Fortitude, looms nearby.
I was there on a pleasant evening in June 2014 with three companions at a window table. We arrived at Aureole after a short walk from the library lecture hall where I had earlier delivered a talk on international finance.
The library offered free access to the lecture. Free access to any event in New York City guarantees an eclectic audience, more diverse than my typical institutional presentation. One gentleman in attendance wore an orange suit, bow tie, sunglasses, and lime-green derby hat. He was seated in the front row. His appearance did not raise an eyebrow.
New Yorkers are not only bold dressers, they’re typically astute. In the question-and-answer session after the lecture, one listener raised his hand and said, “I agree with your warnings about systemic risk, but I’m stuck in a company 401(k). My only options are equities and money market funds. What should I do?” My initial advice was “Quit your job.”
Then I said, “Seriously, move from equities to half cash. That leaves you some upside with lower volatility, and you’ll have optionality as visibility improves.” That was all he could do. As I gave the advice, I realized millions of Americans were stuck in the same stock market trap.
At Aureole, it was time to relax. The crowd was the usual midtown mix of moguls and models. I was with three brilliant women. To my left was Christina Polischuk, retired top adviser to Barclays Global Investors. Barclays was one of the world’s largest asset managers before being acquired by BlackRock in 2009. That acquisition put BlackRock in a league of its own, on its way to $5 trillion of assets under management, larger than the GDP of Germany.
Across the table was my daughter, Ali. She had just launched her own business as a digital media consultant after four years advising Hollywood A-list celebrities. I was among her first clients. She brought millennial savvy to my lecture style with good success.
To my right was one of the most powerful, yet private, women in finance; consigliere to BlackRock CEO Larry Fink. She was BlackRock’s point person on government efforts to suppress the financial system following the 2008 meltdown. When the government came knocking on BlackRock’s door, she answered.
Over a bottle of white Burgundy, we conversed about old times, mutual friends, and the crowd at the lecture. I had addressed the audience on complexity theory and hard data that showed the financial system moving toward collapse. My friend on the right didn’t need any lectures on systemic risk; she stood at the crossroads of contagion in her role at BlackRock.
Under Larry Fink’s direction, BlackRock emerged over the past twenty-five years as the most powerful force in asset management. BlackRock manages separate accounts for the world’s largest institutions as well as mutual funds and other investment vehicles for investors of all sizes. It sponsors billions of dollars of exchange-traded funds, ETFs, through its iShares platform.
Acquisiti
ons engineered by Fink including State Street Research, Merrill Lynch Investment Management, and Barclays Global Investors, combined with internal growth and new products, pushed BlackRock to the top of the heap among asset managers. BlackRock’s $5 trillion of assets were spread across equities, fixed income, commodities, foreign exchange, and derivatives in markets on five continents. No other asset management firm has its sheer size and breadth. BlackRock was the new financial Leviathan.
Fink is obsessively driven by asset growth, and the financial power that comes with it. He typically rises early, devours news, keeps a grueling schedule punctuated by power lunches and dinners, and is asleep by 10:30 p.m., ready to do it all again the next day. When he’s not shuttling between his east side Manhattan apartment and his midtown office, Fink can be found on the global power elite circuit including Davos in January, IMF meetings in April, St. Petersburg, Russia, in June for “white nights,” and so on around the calendar and around the globe, meeting with clients, heads of state, central bankers, and other lesser-known yet quietly powerful figures.
Such power does not go unnoticed in Washington. The U.S. government operates like the Black Hand, a Mafia predecessor portrayed in The Godfather Part II. If you pay protection money in the form of campaign contributions, make donations to the right foundations, hire the right consultants, lawyers, and lobbyists, and don’t oppose the government agenda, you are left alone to operate your business.
If you fail to pay protection, Washington will break your windows as a warning. In twenty-first-century America, government breaks your windows with politically motivated prosecutions on tax, fraud, or antitrust charges. If you still don’t fall into line, the government returns to burn down your store.
The Obama administration raised the art of political prosecution to a height not seen since 1934, when the Roosevelt administration sought the indictment of Andrew Mellon, a distinguished former secretary of the treasury. Mellon’s only crimes were being rich and a vocal opponent of FDR. He was eventually acquitted of all charges. Still, a political prosecution played well among FDR’s left-wing cohort.
The Road to Ruin Page 2
