Financial Economics

At about the same time that Paul Samuelson and others were developing their Keynesian theories, another group of economists were developing a theory of capital markets. From the faculties of Yale, MIT and the University of Chicago came a torrent of carefully reasoned academic papers by future Nobel Prize winners such as Harry Markowitz, Merton Miller, William Sharpe and James Tobin. Their papers, published in the 1950s and 1960s, argued that investors cannot beat the market on a consistent basis and that a diversified portfolio that broadly tracks the market will produce the best results over time. A decade later, a younger generation of academics, including Myron Scholes, Robert C. Merton (son of famed sociologist Robert K. Merton) and Fischer Black, came forward with new theories on the pricing of options, opening the door to the explosive growth of financial futures and other derivatives contracts ever since. The work of these and other scholars, accumulated over fifty years and continuing today, constitutes the branch of economic science known as financial economics.

University biologists working with infectious viruses have airtight facilities to ensure that the objects of their study do not escape from the laboratory and damage the population at large. Unfortunately, no such safeguards are imposed on economics departments. For every brilliant insight there are some dangerous misconceptions that have infected the world’s financial bloodstream and caused incalculable harm. None of these ideas has done more harm than the twin toxins of financial economics known as “efficient markets” and the “normal distribution of risk.”

The idea behind the efficient market is that investors are solely interested in maximizing their wealth and will respond in a rational manner to price signals and new information. The theory assumes that when material new information arrives it is factored into prices immediately, so that prices move smoothly from one level to another based on the news. Since the markets efficiently price in all of this new information immediately, no investor can beat the market except by pure luck, because any information that an investor might want to use to make an investment decision is already reflected in the market price. Since the next piece of new information is unknowable in advance, future price movements are unpredictable and random.

The idea of normally distributed risk is that since future price movements are random, the severity and frequency of price swings will also be random, like a coin toss or roll of the dice. Mild events happen frequently and extreme events happen infrequently. When the frequent mild events and infrequent severe events are put on a graph, it takes the shape of the famous bell curve. The large majority of outcomes are bunched in the area of low severity, with far fewer events in the high severity region. Because the curve tails off steeply, highly extreme events are so rare as to be almost impossible.

In Figure 1 below, the height of the curve shows how often events happen and the width of the curve shows how severe they are, either positive or negative. The area centered on 0 traces those mild events that happen frequently. Consider the area of the curve beyond −3 and +3 This area represents events of much greater severity, events like stock market crashes or the bursting of housing bubbles. Yet, according to this bell curve, they almost never happen. This is shown by the fact that the curve practically touches the horizontal baseline, which signifies things that never happen at all.

FIGURE 1: A bell curve showing a normal distribution of risk

The problem with the Nobel Prize–winning theories based on the bell curve is that empirical evidence shows they do not correspond to the real world of markets and human behavior. Based on an enormous body of statistical and social science research, it is clear that markets are not efficient, that price movements are not random and risk is not normally distributed.

The academic counterattack on these tenets of financial economics have come from two directions. From the fields of psychology, sociology and biology came a flood of studies showing that investors are irrational after all, at least from the perspective of wealth maximization. From iconoclastic mathematical genius Benoît Mandelbrot came insights that showed future prices are not independent of the past—that the market had a kind of “memory” that could cause it to react or overreact in disruptive ways, giving rise to alternating periods of boom and bust.

Daniel Kahneman and his colleague Amos Tversky demonstrated in a series of simple but brilliantly constructed experiments that individuals were full of irrational biases. The subjects of their experiments were more concerned about avoiding a loss than achieving a gain, even though an economist would say the two outcomes had exactly the same value. This trait, called risk aversion, helps to explain why investors will dump stocks in a panic but be slow to reenter the market once it turns around.

When economists began searching capital markets data for the kinds of irrationality that Kahneman and Tversky had demonstrated, they had no trouble finding it. Among the anomalies discovered were that trends, once set in motion, were more likely to continue than to reverse—the basis of “momentum” investing. It also appeared that small-cap stocks outperform large-cap stocks. Others identified the so-called January effect, which showed that stocks performed better in January than other months. None of these findings are consistent with efficient markets or random price movements.

The debate between the efficient markets theorists and the social scientists would be just another arcane academic struggle but for one critical fact. The theory of efficient markets and its corollaries of random price movements and a bell curve distribution of risk had escaped from the lab and infected the entire trading apparatus of Wall Street and the modern banking system. The application of these flawed theories to actual capital markets activity contributed to the 1987 stock market crash, the 1998 implosion of Long- Term Capital Management and the greatest catastrophe of all—the Panic of 2008. One contagious virus that spread the financial economics disease was known as value at risk, or VaR.

Value at risk is the method Wall Street used to manage risk in the decade leading up to the Panic of 2008 and it is still in widespread use today. It is a way to measure risk in an overall portfolio—certain risky positions are offset against other positions to reduce risk, and VaR claims to measure that offset. For example, a long position in ten-year Treasury notes might be offset by a short position in five-year Treasury notes so that the net risk, according to VaR, is much less than either of the separate risks of the notes. There is no limit to the number of complicated offsetting baskets that can be constructed. The mathematics quickly become daunting, because clear relationships such as longs and shorts in the same bond give way to the multiple relationships of many items in the hedging basket.

Value at risk is the mathematical culmination of fifty years of financial economics. Importantly, it assumes that future relationships between prices will resemble the past. VaR assumes that price fluctuations are random and that risk is embedded in net positions—long minus short—instead of gross positions. VaR carries the intellectual baggage of efficient markets and normal distributions into the world of risk management.

The role of VaR in causing the Panic of 2008 is immense but has never been thoroughly explored. The Financial Crisis Inquiry Commission barely considered trading risk models. The highly conflicted and fraudulent roles of mortgage brokers, investment bankers and ratings agencies have been extensively examined. Yet the role of VaR has remained hidden. In many ways, VaR was the invisible thread that ran through all the excesses that led to the collapse. What was it that allowed the banks, ratings agencies and investors to assume that their positions were safe? What was it that gave the Federal Reserve and the SEC comfort that the banks and brokers had adequate capital? Why did bank risk managers continually assure their CEOs and boards of directors that everything was under control? The answers revolve around value at risk and its related models. The VaR models gave the all clear to higher leverage and massive off–balance sheet exposures.

Since the regulators did not know as much about VaR as the banks, they were in no position to question the risk assessments. Regulators allowed the banks to self-regulate when it came to risk and leverage. It was as if the U.S. Nuclear Regulatory Commission allowed the builders of nuclear power plants to set their own safety specifications with no independent review.

Many scholars and practitioners had been aware of the flaws and limitations in VaR. The truth is that the flaws were well known and widely discussed for over a decade both in academia and on Wall Street. The banks continued to use VaR not because it worked but because it permitted a pretense of safety that allowed them to use excessive leverage and make larger profits while being backstopped by the taxpayers when things went wrong. Using VaR to manage risk is like driving a car at a hundred miles per hour while the speedometer has been rigged to stay at fifty miles per hour. Regulators in the backseat of the car glance at the speedometer and see 50, then go back to sleep. Meanwhile the car careens wildly, like something from a scene in Mad Max.

The destructive legacy of financial economics, with its false assumptions about randomness, efficiency and normal risk distributions, is hard to quantify, but $60 trillion in destroyed wealth in the months following the Panic of 2008 is a good estimate. Derivatives contracts did not shift risk to strong hands; instead derivatives concentrated risk in the hands of those too big to fail. VaR did not measure risk; it buried it behind a wall of equations that intimidated regulators who should have known better. Human nature and all its quirks were studiously ignored by the banks and regulators. When the financial economy was wrecked and its ability to aid commerce was well and truly destroyed, the growth engine went into low gear and has remained there ever since.

Pages: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46