8. False Gods, Fake Prophecies

Finance is what economists call money; financial economics is the economics of money. Its pioneering scholars emanated from Chicago’s Graduate Business School (GBS) rather than the economics department because business schools paid better. The GBS shaped modern finance, investments, markets, asset pricing, and especially the measurement and management of risk.

In the 1950s, finance curriculums were rooted in institutional arrangements, legal issues, generalization, common sense, and judgment. Sitting through a Harvard case study on finance, Merton Miller complained: “the solution was not obvious to me...it was frustrating to have no sense of a theory...to tie all this material together.”¹ Chicago changed that, using the rigorous analysis favored by the university’s economic department. Today, Chicago’s Booth School of Business advertises: “In 1960, we...transformed finance from a guild into a science.”

Larry Summers called finance theory “ketchup economics.”² MIT economist David Durand argued that the new finance had “lost virtually all contact with terra firma...[being] more interested in demonstrating... mathematical prowess than in solving genuine problems; often...playing mathematical games.”³ Even Friedman, sitting on Harry Markowitz’s doctoral dissertation committee, grumbled: “It’s not math, it’s not economics, it’s not even business administration.”⁴

Mystery of Price

If Galileo Galilei was obsessed with the motion of celestial objects, then financial economists are obsessed with oscillations of stock prices.

In the 1950s, stocks still traded below 1929 levels and stock ownership by individuals and institutions was considered risky. In 1969, the head of the Ford Foundation, McGeorge Bundy, who as U.S. national security adviser escalated American involvement in Vietnam, increased investment in equities. Harry Markowitz and his 1952 paper Portfolio Selection influenced Bundy.

Markowitz’s modern portfolio theory linked risk and return. Risk was defined and quantified as variance—how far the returns could vary from the expected average (mean) return. The mean-variance approach became the foundation of risk management, starting finance’s fascination and reliance on the bell-shaped normal distribution curve.

Markowitz distinguished between the risk of an individual security and a portfolio. The risk of a portfolio was a function of the risk of an individual security but also how each security moved relative to others (the covariance or correlation between the price movements of the two securities). Diversifying a portfolio among a number of securities, whose prices do not move closely together, reduced risk.

Markowitz was restating Antonio, in William Shakespeare’s Merchant of Venice: “My ventures are not in one bottom trusted, nor to one place; nor is my whole estate upon the fortune of this present year; therefore my merchandise makes me not sad.” Despite Friedman’s grumbling, Markowitz received his Ph.D.

Building on Markowitz’s work, in the 1960s, Jack Treynor, William Sharpe, John Lintner, and Jan Mossin developed the capital asset pricing model (CAPM). The CAPM calculated a theoretically appropriate required rate of return for assets, such as an individual security or a portfolio. Where an asset is added to a well-diversified portfolio, the additional return required is related to the risk unique to that security, which cannot be diversified away.

The CAPM is one of modern finance’s iconic equations:

E[Ri] = Rf + Beta [E[Rm] – Rf]

where:

E[Ri] is the expected return on the asset.

Rf is the risk-free rate of interest on government bonds.

Beta is the sensitivity of the asset returns to market returns.

E[Rm] is the expected return of the market.

[E[Rm] – Rf] is sometimes known as the market premium or risk premium (the difference between the expected market rate of return and the risk-free rate of return).

The CAPM’s insight was that the general risk of markets (systematic risk) could be reduced by diversification but the unique risk of a security (unsystematic risk) could not. A volatile or risky stock (high beta) would need to have a low price (that is, a high expected return) to attract investors. Conversely, a less volatile or lower risk stock (low beta) would have a higher price (a lower expected return). By plugging the inputs into the model, investors could determine what a security should return.

Michael Jensen, a graduate student at Chicago, used a measure developed by Sharpe called the information ratio to compare actual returns earned by investment managers adjusting for the risk taken. Jensen found that few funds outperformed the broad market. On average, investors buying all the stocks in the market would earn higher returns with lower risk. Fund managers with high returns simply took higher risk rather than possessing supernatural skill.

Demon of Chance

The efficient market hypothesis (EMH) stated that the stock prices followed a random walk, a formal mathematical statement of a trajectory consisting of successive random steps. Pioneers Jules Regnault (in the nineteenth century) and Louis Bachelier (early twentieth century) had discovered that short-term price changes were random—a coin toss could predict up or down moves. Bachelier’s Sorbonne thesis established that the probability of a given change in price was consistent with the Gaussian or bell-shaped normal distribution, well-known in statistical theory.

Aware of the importance of his insights, Bachelier claimed: “the present theory resolves the majority of problems in the study of speculation.”⁵ His examiners disagreed. Paul Levy, the leading French probability theorist, thought Bachelier’s work had too much finance. Bachelier’s thesis received a mention honorable, below the très honorable needed to gain a place in the academic world.

Random movements in prices, devoid of any trend or cycle, were a depressing prospect for economists. Maurice Kendall, a British statistician, described it as the work of “the Demon of Chance,” randomly drawing a number from a distribution of possible price changes, which, when added to today’s price, determined the next price.

While working for a stock market newsletter, Eugene Fama noticed patterns in stock prices that would appear and disappear rapidly. In his doctoral dissertation, he laid out the argument that stock prices were random, reflecting all available information relevant to its value. Prices followed a random walk and market participants could not systematically profit from market inefficiencies. The EMH does not require market price to be always accurate. Investors force the price to fluctuate randomly around its real value. As economist Paul Samuelson put it: “if one could be sure that a price will rise, it would already have risen.”⁶

The EMH was the finance version of The Price Is Right, the corollary of Chicago’s belief in free markets. Reviewing Markowitz’s work, David Durand observed that the “argument rests on the concept of the Rational Man.” Durand did not think such a creature existed and thought the whole thing had “an air of fantasy.”⁷

Corporate M&Ms

In the late 1950s, Franco Modigliani and Merton Miller, two professors at Carnegie Mellon University, developed two propositions influencing a company’s capital structure (the mix of debt and equity) and dividend policy. In an efficient market without taxes, bankruptcy costs and differences in knowledge between participants (known as asymmetric information), Proposition 1 states that the value of a firm is unaffected by how that firm is financed. It does not matter if the firm finances by issuing stock or selling debt. Proposition 2 argues that the firm’s dividend policy does not matter.

The Modigliani-Miller propositions crucially introduced the irrelevance principle and arbitrage. They argued that the earnings of a firm were independent of financing decisions with the financing mix between debt and equity only determining how earnings were split between lenders and shareholders. A company with more debt was normally considered riskier than one with less debt. Modigliani and Miller argued that, in a perfect world, investors would demand higher return for the shares and debt where a company had more borrowing. This would negate any benefits of additional borrowing, which consists mainly of the lower cost of debt when compared to the cost of equity.

The process was driven by arbitrage. Classically, arbitrage took advantage of price differentials between two markets. Assume cocaine is trading at $1,000/ounce in London and $1,100/ounce in New York, and the cost of transportation between the two centers is $25/ounce. An arbitrager could purchase an ounce in London, transport it to New York, and sell it to lock in a profit of $75 ounce without any financial risk.

In an arbitrage-free world, where the value of a firm’s debt or equity differed from its intrinsic value driven by its earnings or cash flows, Modigliani and Miller showed that investors would take advantage of any discrepancy in market prices. By investing in different combinations of debt and shares, the investor could create a future income stream of the same size and risk. Dividends were irrelevant, as investors could sell shares to generate income, being indifferent to dividend policy. When the assumption of no taxes was removed, the fact that interest payments reduced tax made debt much cheaper than shares, driving increased leverage and private equity transactions.

In 1976, Michael Jensen and his co-author William H. Meckling published “Theory of the firm: managerial behaviour, agency costs and ownership structure.” The paper’s argument that managers’ interests are different from those of shareholders was not original. What made it influential was the emphasis on using market forces, especially the stock market’s collective judgment, to overcome agency conflicts and costs.

Jensen, who taught at Harvard, used academic journals and popular outlets like the Harvard Business Review, The Wall Street Journal, and The New York Times to propound the idea of the market for corporate control. He argued that: “The takeover market provides a unique, powerful and impersonal mechanism to accomplish the major restructuring and redeployment of assets continually required by changes in technology and consumer preference.”⁸

Just as Friedman was the public face of its economics, Jensen and Miller were the public face of Chicago finance. Jensen combined his academic duties with a role at Monitor Group, a strategy consulting firm started by Michael Porter, another Harvard academic and consultant. Merton Miller served as a public director on the CBOT between 1983 and 1985 and the CME from 1990 until his death on June 3, 2000. They helped make the theory influential and relevant to banks and industrial corporations.

In his finance class, Miller once drew a vertical line on the blackboard, writing M & M on one side and T on the other side. Asked about the T, the combative Miller replied “them,” opponents of the new finance.⁹

Risk Taming

In a 2008 BBC documentary on finance, wispy clouds over central London shape a mathematical formula:

P_ce = S × N(d1) – K e ^-Rf.T × N(d2)

The clouds were the work of computer graphics; the equation is real. Just as every art movement has its defining work, the Black-Scholes option pricing model is the masterpiece of financial economics.

Options are insurance against rising and falling prices. Applied to stocks, a call option gives the buyer, in return for payment of a fee (the premium), any gain from increases above an agreed price (strike price) as at or by a certain date (expiry). A put option gives the buyer, in return for a fee, protection against falling prices. Options have skewed risks and rewards. The buyer of an option has limited loss (the premium) but potentially large gains (depending on how much the price of the underlying asset moves). Conversely, sellers of options risk large losses (the potentially large payout they must make if the price of the asset moves by a large amount) in return for a small gain (the premium received).

Insurance, with its long history, offered guidance on valuation. The insurer’s profit was the difference between statistical loss experience, based on historical knowledge of claims, and the premiums paid, plus investment income on the premiums. Applying insurance theory to options proved difficult.

Louis Bachelier applied random walk models to pricing options. Paul Cootner and Paul Samuleson worked on the problem. In their 1967 book Beat the Market, mathematicians Sheen Kassouf and Edward Thorp outlined the relationship between the price of an option and the price of the underlying stock. Thorp, whose interest was gambling and beating the casino at roulette and baccarat, developed a model, anticipating the Black-Scholes equation.

With a background in physics and mathematics, Fischer Black worked at Arthur D. Little, a financial consulting firm. While at the University of Chicago, Black collaborated with Myron Scholes, whose Ph.D. focused on using arbitrage to ensure that securities with similar risks offered similar returns. Black and Scholes built upon Kassouf and Thorp’s idea of hedging options using the underlying stock.

The value of the option must be determined by the value of the stock. As the stock price changes, so should the price of the option. If a stock price moves from $10 to $11, then the price of the call option should also increase. The relationship allows the setting up of risk-free portfolios where you buy a call option and at the same time short sell a share. If the stock price goes up then the value of the call option increases but you suffer a loss on the shares, as you have to buy them back at the higher price. By adjusting the ratio of options to the shares, you can construct a portfolio where the changes in the value of the options and shares exactly offset, at least, for small movements in the stock price.

Working as research assistant to Paul Samuelson, Robert Merton was also working on option pricing. Merton introduced an idea—continuous time mathematics. Black and Scholes assumed that the portfolio would be rebalanced to keep it free of risk by changing the number of shares held at discrete time intervals. Merton forced the time intervals into infinitely small fragments of time, effectively allowing continuous and instantaneous rebalancing. Although unrealistic and practically impossible, this allowed a mathematical solution using Ito’s (pronounced “Eto”) Lemma to solve the equation. Ito, an eccentric Japanese mathematician, later did not remember deriving the eponymous technique.

The Black-Scholes-Merton (BSM) option pricing model relied on the CAPM, itself reliant on the EMH, and arbitrage. While physicists searched, financial economists had arrived at their own version of the GUT (grand unifying theory).

The BSM model and its hedging logic profoundly influenced risk management. Infinite varieties of derivative instruments could be created. In awarding Scholes and Merton the 1997 Nobel prize in Economics, the Swedish Academy noted that “their valuation methodology has paved the way for...new...financial instruments and facilitated more efficient risk management.”¹⁰

Option pricing proved pivotal because many transactions could be deconstructed into options or, more generally, derivatives. Shares, bonds, mortgage prepayment rights, and various contracts could be unbundled, evaluated, and priced. Keen to solve every financial problem, Merton led the way with his newfound Lemma gun. Derivatives were a “universal financial device,”¹¹ which could be used in endless ways to manage risk, create packages of risk, generate income, or simply speculate on certain outcomes.

In April 1973, shortly after publication of the Black Scholes paper, the Chicago Board of Option Exchange (CBOE), coincidentally, began trading stock options in its converted smoking lounge. The advent of handheld calculators, made by Texas Instruments and Hewlett-Packard, which could be programmed to calculate option prices using the model, appeared. Texas Instruments advertised in The Wall Street Journal: “you can find the Black-Scholes value using our...calculator.”¹²

Black went on to a long career at Goldman Sachs. Scholes moved to Salomon Brothers and Long Term Capital Management (LTCM), where Merton joined him. As the Nobel prize in Economics is not awarded posthumously, Fischer Black’s death in 1995 robbed him of a share of the award that went to Scholes and Merton for the development of option pricing models. During his life, when Black appeared infrequently on the floor of the CBOE, trading would halt momentarily and a loud cheer and clapping would break out. The Economist obituary described Black as “one of the most productive minds of [the] century.”

In practice, it is the hedging or replication aspect of the model (the Merton perspective) that became important. Traders could hedge the risk of options by trading in the underlying asset, allowing them to make markets and trade in options and derivatives generally. Black remained equivocal about the replication approach: “Merton’s derivation relies on stricter assumptions, so I don’t think it’s really robust.”¹³

Slow and Quick Money

Initially, the ideas did not find acceptance among practitioners. The professors could point to no practical experience or track record. Baron Rothschild once observed that only three people understood the meaning of money and none had very much of it.¹⁴

Diversification to reduce risk was contrary to the ethos of stock picking. While successfully managing the portfolios of an insurance company and the King’s College endowment, Keynes insisted that diversification was flawed:

To suppose that safety...consists in having a small gamble in a large number of different [stocks] where I have no information...as compared with a substantial stake in a company where one’s information is adequate, strikes me as a travesty of investment policy.¹⁵

Mark Twain’s Pudd’nhead Wilson agreed: “Put all your eggs in one basket, and watch that basket.”

Academic James Lorie arrogantly advised money managers to “give up conventional security analysis. Its occasional triumphs are offset by its occasional disasters and on the average nothing valuable is produced.”¹⁶ One convert argued what fund managers were doing was “150,000 percent bullshit.”¹⁷ Burton Malkiel wryly remarked that proponents were “greeted in...Wall Street...with as much enthusiasm as Saddam Hussein addressing a meeting of the B’nai B’rith.” Wells Fargo’s James Vertin, whose bank was trying to commercialize the theories, understood the problem: “most practitioners feel themselves to be objects of academic ridicule, and most feel bound to resist this assault.”¹⁸

The 1974 U.S. Employee Retirement Income Security Act required investment managers and trustees to meet the “prudent man” test in discharging fiduciary duties. As EMH and CAPM enjoyed academic acceptance, trustees and managers were forced to follow the theory to avoid failing the test by differing too radically from its prescriptions.

As money was farmed out to external specialist fund managers, investors needed to measure investment performance. Asset consultants Mercers, Frank Russell, Towers-Perrin, Watson-Wyatt, BARRA, and others, who were employed to analyze portfolios, returns, and investment practices, were well versed in the theory, helping popularize them.

Benchmarking returns and monitoring investment performance regularly became accepted wisdom. It influenced how money was allocated between different investments such as cash, bonds, and equity. Portfolio returns were compared to benchmarks such as the S&P 500. Investment return was separated into β (beta), or the return on the broad market, and α (alpha), the fund manager’s outperformance.

Giving up trying to beat the market, index funds purchased all the stocks in the index to match the market return. Jack Bogle’s Vanguard Group and Barclays Global Investors (BGI, now owned by Blackstone) built large businesses on the mantra of low cost indexation.

Others used a core-satellite approach—the bulk of funds were invested to match the index but a small portion was used to pick winners seeking outperformance (generate alpha). As fund management evolved into a professionally managed business, increasing costs, especially of attracting investors and compliance, forced economies of scale. UBS Asset Management and Blackstone now manage trillions of dollars. As size made it difficult to enter and exit markets quickly without affecting prices, indexation or core-satellite approaches grew.

Diversification encouraged new asset classes—emerging markets, currencies, commodities, infrastructure, insurance risk, and even fine arts. As long as the investment offered returns and did not move together with other asset classes held by the investor, adding them to a portfolio improved return with lower risk. The success of the unorthodox investment philosophy of David Swensen and the Yale Endowment showed the potential of hedge funds and private equity to generate alpha. Investment managers used derivatives to manage risk or create structured products. Portfolio insurance or constant proportion portfolio insurance was used to limit investor’s risk of loss from a sharp fall in prices.

Acceptance was not wholehearted: “An awful lot of material is coming in...sitting on people’s desk...getting talked about...But the number of people...actually using the new investment technology is still limited...Everybody is trying to look au courant.”¹⁹ But engagement gradually deepened.

Corporate Practice

Chicago graduate Joel Stern’s Stern Stewart & Co. (a consulting firm), Marakon Consulting (which evolved out of Wells Fargo’s Management Sciences Division) and Alcar (created by Professor Alfred Rappaport) spruiked shareholder value. Stern, a skilful self-promoter, wrote influential articles in The Wall Street Journal, a column in the Financial Times and, like Bill Gross, became a regular on Wall Street Week.

The Stern Stewart pitch was pure Modigliani and Miller, arguing that companies’ share prices reflected ‘expected cash flow...above and beyond the anticipated investment requirement of the business.”²⁰ Shareholder value required maximizing cash flow and returns above the company’s cost of capital.

Stern Stewart published an annual Billboard Top 100 Chart that analyzed corporations’ EVA™—economic value added or estimate of economic profit. The idea gained traction when executive remuneration was linked to EVA and companies awarded stock or stock options to executives to align the interests of shareholders and management.

Shareholder value fueled financial innovation, new debt and equity securities, allowing companies to raise cheap equity or increase borrowing. Companies repurchased the firm’s shares to boost share prices.

Traditionally, companies favored modest borrowing and high credit ratings. Now, they increased debt to reduce their cost of capital. Debt was cheaper than equity and interest was tax-deductible, allowing companies to increase shareholder return through additional leverage. By 2005 the number of companies rated AAA (the highest credit quality) declined to 6 from 32 in the early 1980s.²¹

As the volatility of currencies, interest rates and equity prices increased, banks and exchanges introduced derivative instruments. At the CME, Leo Melamed, concerned about the drop in volumes in the onion and egg futures contracts, perspicaciously steered the exchange into financial derivatives, with the support of Friedman and Miller, with immediate success. Corporations and investors increasingly needed to use available derivatives to hedge risks to be considered prudent.

The BSM model and Markowitz’s work evolved into risk quantification modes, such as value at risk (VAR) models. VAR signifies the maximum amount that you can lose, statistically, as a result of market moves for a given probability over a fixed time.

If you own shares over a year, then most of the time the share price moves up or down a small amount. On some days you may get a large or very large price change. VAR ranks the price changes from largest fall to largest rise. Assuming that prices follow a random walk and price changes fit a normal distribution, you can calculate the probability of a particular size price change. You can answer questions like what is the likely maximum price change and loss on your holding at a specific probability level, say 99 percent, which equates to 1 day out of 100 days. A VAR figure of $50 million at 99 percent over a 10-day holding period means that the bank has a 99 percent probability that it will not suffer a loss of more than $50 million over a 10-day period.

VAR became accepted best practice, enshrined in bank regulations. Risk, the unknown unknown, was now a known unknown or even a known known. Former risk manager Barry Schacter offered an alternative definition of VAR: “a number invented by purveyors of panaceas for pecuniary peril intended to mislead senior management and regulators into false confidence that market risk is adequately understood and controlled.”²²

Everything Is Just Noise

The new theories required that everyone has rational expectations, that is, the population is correct on average even if no individual person is. Behavioral economist Amos Tversky summed it up: “When we talk about individuals, especially policy makers, they all make errors in their decisions. But in aggregate, they all get it right?”²³

Exceptions and anomalies increasingly undermined the theory of efficient markets. There was the turn-of-the-year effect, where stocks seemed to rise in January each year. Small-size firms outperformed large stocks—the small-firm effect. In the loser effect, stocks that had fallen significantly outperformed stocks that performed well in previous periods. There was little relationship between beta (risk) and return.

Behavioural economists, such as Tversky, Daniel Kahneman and Richard Thaler, argued that efficient financial markets were rife with cognitive biases and errors in reasoning and information processing, including overconfidence, overreaction, representative bias, information bias, and the use of linear reasoning. Cliff Asness, a student of Fama and founder of hedge fund AQR Capital Management, exploited these anomalies. Hearing complaints that his strategies were not working, Asness’ wife asked him incredulously: “Let me get this straight. I thought you said you make your money because people aren’t completely rational. Yet now you’re mad because they’re too irrational.”²⁴

Risk management assumes that price changes are normally distributed. The mathematician Benoit Mandelbrot demonstrated that normal distributions do not exist in practice. In Fooled by Randomness and Black Swan, Nicholas Nassim Taleb argued against the application of statistical methods in finance, especially the normal distribution curve to measure risk.

Taleb drew on John Stuart Mill, himself rephrasing a problem posed by Scottish philosopher David Hume: “no amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.” Unpredictable extremes of price movement, known as fat tails, were more common than theory implied. Extreme price moves were perfectly intelligible in hindsight but are not predictable—just as the existence of black swans was only discovered in Australia in the eighteenth century.

In August 2007, David Viniar, Goldman Sachs’ CFO, commented that: “We were seeing things that were 25-standard-deviation moves, several days in a row.” In October 2008, the Dow Jones Industrial Average moved more than 10 percent on 2 days. Using a normal distribution, economists Paul De Grauwe, Leonardo Iania and Pablo Rovira Kaltwasser estimated that the moves should occur only every 73 to 603 trillion billion years. “Since our universe...exists a mere 20 billion years we, finance theorists, would have had to wait for another trillion universes before one such change could be observed.... A truly miraculous event.”²⁵

But nobody wanted to accept that their models were incorrect. Confronted with quantum theory, Albert Einstein refused to believe that God played dice with the universe. But as Stephen Hawking remarked: “Not only does God play dice, but...he sometimes throws them where they cannot be seen.”²⁶

In his 1986 presidential address to the American Finance Association, Fischer Black distinguished between noise and information. In traditional communication, noise is the disruption in the passage of information through unintended addition to the signal between transmission and reception. It makes it difficult to decode the intended signal accurately. People increasingly mistook noise for information. Traders bought and sold on rumor or misinformation. Noise occurs at every step, making observation imperfect and preventing prices from getting to real values.

Noise led to oversimplified relationships, making it impossible to determine cause and effect. Black argued:

No matter how many variables we include...there are always...potentially important variables that we have omitted, possibly because they too are unobservable.... In the end, a theory is accepted not because it is confirmed by conventional empirical tests, but because researchers persuade one another that the theory is correct and relevant.²⁷

Traders traded, fooled by noise. Even people with accurate price sensitive information could not be certain whether they were trading on information or noise, making much of the modeling meaningless.

Keynes, too, had warned about the risk in making any estimate because of incomplete knowledge:

Professional investment may be likened to those newspaper competitions in which the competitors have to pick out the six prettiest faces from a hundred photographs...each competitor has to pick, not the faces which he himself finds the prettiest, but those which he thinks likeliest to catch the fancy of the other competitors.

He anticipated modern finance:

We have reached the third degree when we devote our intelligences to anticipating what average opinion expects the average opinion to be. And there are some, I believe, who practice the fourth, fifth and higher degrees.²⁸

Perfect Worlds

In modern economics and finance, until something is said mathematically, it isn’t said at all. Soviet economists had access to the latest Western economics publications because “the Party ruled that these were mathematical works...purely technical, devoid of ideological content.”²⁹

Models were increasingly the product of data mining, trawling through historical data to find a relationship and prove or reject hypotheses. More data and improved statistical methods overwhelmed common sense. Albert Einstein knew the problem: “As far as the laws of mathematics refer to reality, they are not certain, and as far as they are certain, they do not refer to reality.”

Researchers “saw” patterns in data. But strong correlation does not prove causality. In the late 1940s, before the invention of the polio vaccine, American public health experts thought they had discovered a correlation between polio cases and increased consumption of ice cream and soft drinks. In reality, polio outbreaks were simply more common in the hot summer months and not caused by eating ice cream. Eugen Slutsky cautioned that “it is always possible to detect, even in the multitude of individual peculiarities...uniformities and regularities.”³⁰ David Weinberger, in his mathematics Ph.D. dissertation involved in arbitrage, argued that investors and traders “believe there’s structure or you don’t .... It’s...a religious question.”³¹

The models relied on over-simplified assumptions. Friedman argued that economic theory was “an engine” not “a camera”; it could analyze the world but it was not complete as it was impossible to incorporate all details. The theory would have inaccurate or unrealistic assumptions, but this was irrelevant. A theory should be judged not on its assumptions, but on the power of its conclusions. Philosopher Karl Popper argued similarly that factual evidence could never prove a theory but could only fail to disprove it.³²

Frank Knight remained sceptical: “mathematical economists have commonly been mathematicians first and economists afterwards, disposed to simplify the data and underestimate the divergence between their premises and the facts of life.”³³ Economic research increasingly resembled the view of Poo-Bah in The Mikado: “corroborative details intended to give artistic verisimilitude to an otherwise bald and unconvincing narrative.”³⁴

Theories were the work of economists who did not know any finance and a handful of financiers who learned finance from these same economists. The statisticians, mathematicians, and scientists knew neither economics nor finance. None had much actual practice, actively resisting contamination and preferring to deal with finance on a theoretical level. Much of the literature reflected the Gordon-Liebhafsky theorem that: “provided that it achieves a certain threshold of intelligibility, the greater the obscurity of a piece written by an economist, the greater is the likelihood that it will be recognized as a classic or seminal work.”³⁵

For financiers, theory and models were secondary to profit. Merton’s dynamic hedging or replication approach allowed the creation of derivatives and their risk management, helping banks to trade a bewildering variety of instruments. In the 1950s, two economists, Kenneth Arrow and Gerard Debreu, showed that attaining the nirvana of economic equilibrium required state securities, contracts to buy or sell everything at any time period in every place until infinity or the end of the world, whichever was first. This theoretically perfect world now justified any and every type of derivative and financial product.

Financial Fundamentalism

In the eighteenth century, Western societies shifted from medieval systems of aristocratic and religious authority to models of reason, scientific method, rational discourse, personal liberty, and individual responsibility. A central tenet of the new faith was the ability to control the environment. In 1965, President Johnson’s Council of Economic Advisers, led by Walter Heller, declared: “Tools of economic policy are becoming more refined, more effective, and increasingly freed from inhibitions imposed by traditions, misunderstanding, and doctrinaire polemics.” The Council declared that economic policymakers could “foresee and shape future development.”³⁶

In Against The Gods: The Remarkable History of Risk, Peter Bernstein argued that capitalism would be impossible without quantification and hedging of risk. Investors and businesses could now undertake long-term investments with confidence, secure in the knowledge that, if necessary risk could be hedged away at a price. In 1999 Time featured Alan Greenspan, Robert Rubin, and Lawrence Summers as the new vaudeville act, “The Committee to Save the World.” No financial risk was so great that it could not be managed.

In 130 AD, Ptolemy, a Greek mathematician, astronomer, geographer, and astrologer, developed an astronomical system. The Ptolemaic system fitted the accepted view of philosophers and the Church that the Earth was at the center of the universe and all stellar bodies moved with perfect uniform circular motion. When Galileo observed the actual movements of heavenly objects and tested Ptolemy’s theories against the evidence, the system collapsed. Modern economics resembles a Ptolemaic system where deeply held philosophical and political beliefs shaped models of economies and markets.

As the Russians learned, economics is ideology. Alan Blinder, a former vice-chairman of the Fed, observed: “Some...economists are extremely ideological. If you [give] them evidence counter to their world view, they say you are wrong.”³⁷

The dominant theme is free and efficient markets, which have to be left alone for the common good. Thomas Frank in One Market, One God captured this spirit:

Efficient markets theory holds that stock markets process economic data quickly and flawlessly...commentators...believe that stock markets perform pretty much the same operation with general will, endlessly adjusting and modifying themselves in conformity with the vast and enigmatic popular mind.³⁸

American Nobel-prize-winning economist Joseph Stiglitz dissented: “[Adam Smith’s] invisible hand often seems invisible [because] it is often not there.”³⁹

Nineteenth-century Danish philosopher Søren Kierkegaard differentiated between objective truths and subjective truths. Objective truths are filtered and altered by our subjective truths. Financial economics, in its prioritization of evidence and its models, converted objective truths into subjective truths consistent with the Chicago Interpretation. Daniel Miller, an anthropologist, argued that “economics has the authority to transform the world into its image.”⁴⁰ That image was money and the speculation economy.

Fata Morgana

It was a mirage, a fata morgana: “poetic crap, in short.”⁴¹ The world was not structured, comprehensible or controllable. Theories were narrative fallacies where unconnected data was rearranged into a plausible story after the fact to give it an identifiable explanation.

Robert Merton’s first published paper—“The ‘motionless’ motion of Swift’s flying island”—was about Jonathan Swift’s Gulliver’s Travels. Educated people, fond of mathematics and astronomy, populated Laputa, Swift’s fictional island. The Laputians were impractical, unable to construct buildings or make clothing, and insisted on taking measurements with compasses and quadrants where measuring tapes were appropriate. La puta means “whore” in Spanish profanity, alluding to Martin Luther’s famous phrase: “that great whore, reason.” Swift’s allegory was a satire on rational thinking. Much of the liturgy of finance and economics was Laputian in nature.

In 1936 sociologist Robert K. Merton, Robert Merton’s father, introduced the concept of unintended consequences. The world’s complexity, self-deception, hubris, and biases had unintended consequences. There is ignorance—the impossibility of anticipating everything leading to incomplete analysis; the likelihood of error—the incorrect analysis of the problem or inappropriate application to a situation; immediate interest—which may override long-term interests; basic values—requiring or prohibiting certain actions; and the problem of the self-defeating prophecy—fear driving people to find solutions before the problem occurs. The financial theories and models had serious unintended consequenses and were deeply flawed.

Quantification of risk is difficult. The illusion that risk can be measured or managed has unintended consequences, encouraging risk taking or lulling regulators and policy makers into assuming that something is less risky than it is. Managing risk itself creates instability and increases risk. Financial markets are artificial man-made creations, not objects found in nature. As Taleb noted: “Our activities may invalidate our measurements.”⁴²

Keynes had modest hopes for economics: “If economists could manage to get themselves thought of as humble, competent people on a level with dentists, that would be splendid!”⁴³ Believing that they were able to control markets and economies, ambitious modern policy makers and economists embraced reason and scientific method, ignoring the law of unintended consequences. Ignorance, error, and immediate interest were crucial subtexts in finance.

In business to manufacture financial products and sell them to clients for profit, financiers were oblivious to the theoretical nuances and debates. Financial economics relies heavily on data collected by Chicago’s Center for Research in Security Prices (CRSP), funded by Merrill Lynch. The sponsorship was not driven by altruistic interest in research and knowledge. In 1960 Louis Engle, Merrill’s head of advertising and marketing, discovered that the Securities and Exchange Commission (SEC) would not allow the broker to advertise stocks as an appropriate investment without evidence. Engle funded CRSP to undertake a long-term study of returns on shares to justify Merrill selling stocks to ordinary investors. As Confucius observed: “The superior man understands what is right; the inferior man understands what will sell.”

Theory and practice have always been occasional bedfellows on Wall Street. Financiers borrow and steal whatever bits and pieces of any cosmological theory and Sufi philosophy within reach.

Subtle theological debates about finance did not interest bankers. Was The Physical Impossibility of Death in the Mind of Someone Living really art or amateur taxidermy? Traders did not care, only being interested in whether clients would pay good money for it. They took the advice of art critic Peter Schjedahl: “Art tells you things you don’t know you need to know until you know them.”⁴⁴

The new economics allowed politicians to implement their ideological agendas. The new finance provided the moral and intellectual basis for financial conquest, plunder, and pillage. The historian Bernal Díaz del Castillo, companion of Hernán Cortés de Monroy y Pizarro, the Spanish conquistador who led the Spanish colonisation of the Americas, understood this very well: “We came here (to America) to serve God and the King, and also to get rich.”

The professors may have been after some elusive truth but ended up as the piano players in the whorehouse. The smart ones cut themselves a share of the action. Financiers did what they had always done—whatever it takes to make money, only now they did it with financial alchemy.