Further reflections on Alchemists of Loss

Last week I presented a book review of Alchemists of Loss [1].  Now I seek to explore the errors of Modern Financial Theory in more detail and provide some insights based on an understanding of entrepreneurship that is missed by the authors.  I hope this will add to the impressive firepower mustered by Dowd and Hutchinson, and allow us debunk the myths of the Alchemists once and for all. The Alchemists’ ideas, like those of Keynes, have zombie-like quality; they return, undead, like something from a cheap cult Hammer House of Horror movie. I aim to provide another stake to impale them with.  The Great Austrian economist Dr Frank Shostak will also be deployed, as he placed an almighty stake into the Zombie in 2000, in an article I will reproduce in full. In an appendix to this article, I reproduce the names and profiles of the Alchemists of Loss.


Let us begin with an overview of Modern Financial Theory, as explained by Dowd and Hutchinson:

In essence, Modern Financial Theory can be summarized as the application of the theories of mathematical statistics to finance. The techniques involved soon became known colloquially as “rocket science”, although in fact real rocket science is a lot simpler. [2]

Nobel Prizes for its principal developers, including (among others) Harry Markowitz in 1990 and Robert Merton and Myron Scholes in 1997. By the mid-1990s it was said that there were more PhD physicists working as “quants” in the research departments of investment banks than were working in physics itself. [3]

A good starting point and one of the key pillars of Modern Finance is the 1958 Modigliani-Miller Theorem, developed at Carnegie-Mellon by Franco Modigliani and Merton Miller. This stated that under a set of hypothetical conditions – which included (i) no taxes, (ii) no difference between the rates at which individuals and corporations could borrow, (iii) zero transactions costs, and (iv) the complete absence of agency costs (no conflicts of interest) – then the value of a company is invariant as to its capital structure. In other words, capital structure, the balance between debt and equity, is irrelevant.

The Theorem was quickly extended to take account of the taxdeductibility of debt and the double taxation of equity dividends in the US system to show that, for a US company, the theoretically optimal level of leverage was infinite and the optimum dividend payout from earnings was zero. [4]

As with most of Modern Financial Theory, the flaws in Modigliani/Miller were primarily in the assumptions. [5]

Modern Financial Theory gave rise to Modern Portfolio Theory, which the authors describe as follows:

The underlying ideas were simple; the application of those ideas, however, was anything but straightforward. Imagine an enterprising trader who runs a market stall by an English seaside. Our trader can afford a certain outlay and has to choose which of two goods to sell, but is concerned about the day-to-day fluctuations in his income.

He begins by selling ice cream and sunglasses. This works really well when the sun is shining and everyone wants ice cream and protection against the sun: on such days, sales of both boom. However, this being England, there are many days when it rains, so on those days people want neither and he sells nothing. So he either makes a lot of money or he makes nothing, depending on the weather, and the English weather is very uncertain.

He then has the bright idea of switching from sunglasses to umbrellas.  When he does so, he then finds when the sun comes out, he does well on the ice cream but not on the umbrellas; and when it rains, he does badly on the ice cream but well on the umbrellas. His income is now much steadier, even though the weather is as unpredictable as it was before. Our trader has diversified his risks.

In the one case, the lines of business had a strong positive correlation, meaning that if one did well, the other was also likely to: both depended on the sun being out. In the second case, the two lines of business had a strong negative correlation: one did well if the sun came out, and the other did well if it rained.

The lesson is to search for lines of business that are negatively correlated and so have risks that offset each other [6]

As mentioned in my review, Hutchinson and Dowd show how these negative correlations are worked out statistically with sublimely beautiful maths. The probability of each standalone investment going bust is also worked out, and voila: a “black box” can objectively calculate your optimum portfolio strategy. Man has been removed from the investment process; the maths do the work of portfolio allocation. All man has to do is find clients to whom to sell the black box.

This faith in objective measures is rooted in the Efficient Market Hypothesis:

Its essence was the claim that market prices were “efficient” in the sense that they “fully reflected” all available information: markets are “efficient” because they get prices right. This hypothesis was the perfect embodiment of the notion of “rational economic man” that ruled the economics textbooks: efficient markets was rational economic man in the stock market.

Large amounts of empirical evidence were soon being collected that seemed to support the Efficient Markets Hypothesis. Doubts and evidence against it were generally ignored and academics who opposed it were railroaded [7]

The Efficient Markets Hypothesis goes beyond the self-evident truth that, on average, you can’t expect to beat the market. It is one thing to say the market is hard to beat, as good investment gurus had maintained since at least Benjamin Graham, another to say that market prices are, somehow, “right.”

Yet the Efficient Markets Hypothesis was clearly inadequate. This was especially so for the “strong form” of the Efficient Markets Hypothesis, which maintained that prices fully reflect all information, both public and private. For one thing, how exactly does the information in one person’s head become instantaneously known to everyone else in the market? And, if the strong-form Efficient Markets Hypothesis does hold, then what incentive would anyone have to collect any more information? If prices fully reflected the information available, then it would be economically irrational to spend resources collecting it. In that case, the investment advice industry shouldn’t exist at all. But it did. There would be little reason to trade either.

There was also the awkward implication, usually glossed over by its proponents, that if markets were truly efficient, then why do market prices move so much? If markets are efficient, then changes in market prices must reflect new information becoming available to the market.

If so, what was the information that became available on October 19, 1987 that caused the New York Stock Exchange to fall by 23% that day? [8]

Dr Frank Shostak, an eminent economist and noted econometrician himself demolishes this point of view and provides us with the seeds of a correct and subjective view of portfolio allocation:

Diversification: An Austrian View

“According to the efficient markets hypothesis, stock market prices move in response to new, unexpected information. Since, by definition the unexpected cannot be known, it implies that an individual’s chances of anticipating the general direction of the market are as good as anyone else’s chances.

It is thus suggested that since the future direction of the stock market cannot be known and that the only way of earning above average returns is to assume greater risk. This is described by the modern portfolio theory (MPT). It is accepted by the practitioners of this theory that risk is associated with the degree of dispersion of returns around the average of returns.

A security whose returns are not expected to deviate significantly from its historical average is termed as a low risk. A security whose returns are volatile from year to year is regarded as risky. MPT assumes that investors are risk averse and they want high guaranteed returns. To comply with this assumption the MPT instructs investors how to combine stocks in their portfolios to give them the least possible risk consistent with the return they seek. MPT shows that if an investor wants to reduce investment risk he should practice diversification.

Consider the following simple example:

Activity A Activity B
Cold Weather 20% -10%
Warm weather -10% 20%

Let us assume that on average, half of the time the weather is cold and half of the time it is warm. According to the table, investment in activity A will yield a 20% return in cold weather and in warm weather will produce a loss of 10%. On average the return by investing in A will be 5%. The same outcome will be obtained with regard to investment in activity B.

The MPT then suggests that if the investor diversifies and invests one dollar in A and one dollar in B then he will be guaranteed a 5% return regardless of weather conditions. Thus in warm weather, one dollar invested in B will produce a 20% return, while one dollar invested in A will produce a 10% loss. Investors total return on two dollars invested in A and B will be 5%. Exactly the same result will be obtained for cold weather conditions. This example illustrates that through the magic of diversification regardless of weather conditions one can obtain risk free 5% return on investment.

This must be contrasted with the fact that the two investments A and B are highly risky, because the frequency of a cold or a warm season in a particular year cannot be always ascertained. All that we know is that on average, over a prolonged period of time, half of the time the weather is cold and half of the time it is warm. This however, doesn’t mean that every year this will be so. This example shows that as long as activities are affected differently by given factors there is a place for diversification, which will eliminate risk.

The basic idea of MPT is that portfolios of volatile stocks, i.e. risky stocks, can be combined together and this in turn will lead to the reduction of the overall risk. The guiding principle for combining stocks is that each stock represents activities that are affected by given factors differently. Once combined, these differences will cancel each other out, thereby reducing the total risk.

However, the theory indicates that the risk of various stocks must be broken into two parts. The first part is associated with the tendency of returns on a stock to move in the same direction as the general market. The other part of the risk results from factors peculiar to a particular company. The first part of the risk is labeled systematic risk, the second part, unsystematic. Through diversification only unsystematic risk is eliminated, systematic risk cannot be removed through diversification. Consequently it is held that return on any stock or portfolio will be always related to the systematic risk, i.e. the higher the systematic risk the higher the return.

The systematic risk of stocks captures the reaction of individual stocks to general market movements. Some stocks tend to be sensitive to market movements while other stocks display less sensitivity. The relative sensitivity to market moves is estimated by means of statistical methods and is known as beta. In this regard beta is the numerical description of systematic risk. If a stock has a beta of 2 it means that on average it swings twice as much as the market. Thus if the market goes up 10% the stock tends to rise 20%. If however, the stock has a beta of 0.5% then it tends to be more stable than the market.

Does it make sense?

The MPT framework gives the impression that there is a difference between investing in the stock market and investing in a business. However, the stock market doesn’t have a “life of its own”. The success or failure of investment in stocks depends ultimately on the same factors that determine the success or failure of any business. Consequently an investment in stocks should be regarded as an investment in business as such and not in stocks. By becoming an investor in a business an individual has exercised an entrepreneurial activity. In other words he has committed his capital with a view to supply the most urgent needs of consumers.

For an entrepreneur the ultimate criterion for investing his capital is to employ it in those activities that will produce goods and services that are on the highest priority list of consumers. It is this striving to satisfy the most urgent needs of consumers, that produces profits and it is this alone that guides entrepreneurs. The entrepreneurs focus and main consideration while investing his capital is to secure the highest possible profits, not to minimise risk as the MPT suggests. If entrepreneurs strived after what they considered to be the safest investment while neglecting consumers wishes they would render the entire investment unsafe.

The size of an entrepreneur’s return on their investment is determined not by how much risk they assume, but whether they comply with consumers’ wishes. The fact that entrepreneurs appear to be practicing diversification by investing in various businesses over time doesn’t necessarily mean that they do so in order to reduce their investment risk, they may diversify in order to boost their chances of earning profits.

The moment the primary consideration of investment becomes the reduction of risk rather than the attainment of the highest possible profit, then all kind of strange decisions may emerge. For instance, strictly following MPT, one may deliberately invest in an asset that offers a negative return in order to reduce the overall portfolio risk. However, no sane investor deliberately chooses a badly performing investment. It is only the emergence of conditions not properly forecasted by the investor that leads to a bad investment.

According to Mises:

A capitalist never chooses that investment in which, according to his understanding of the future, the danger of losing his input is smallest. He chooses that investment in which he expects to make the highest possible profits. [9]

Furthermore, in an attempt to minimize risk, practitioners of MPT tend to institute a high degree of diversification. However, having a large number of stocks in a portfolio might leave little time to analyse the stocks and understand their fundamentals. This could raise the likelihood of putting too much money in bad investments. This way of conducting business would not be an entrepreneurial investment but rather gambling.

One of the world’s most successful stock market investors, Warren Buffett, argues that investor’s financial success is in direct proportion to the degree to which he understands investment. This understanding, according to Buffett, is what separates investors with a business consideration from gamblers who merely buy stocks. Buffett says that investors are better served if they concentrate on locating a few spectacular investments rather than jumping from one mediocre idea to another. John Maynard Keynes expressed a similar view:

As time goes on, I become more convinced that the right method of investments is to put fairly large sums into enterprises which one thinks one knows something about and in management of which one thoroughly believes. It is a mistake to think that one limits one’s risk by spreading too much between enterprises about which one knows little.

Proponents of modern portfolio theory argue that diversification is the key to the creation of the best possible consistent returns. We argue that one must focus on the profitability of the investments in a portfolio, before one considers their contribution to the portfolio’s diversification. Consequently, whilst we agree with the general principle of diversification, we believe that the profitability of an individual investment should be the primary consideration for the investor.

The Missing Link in Portfolio Management : The Entrepreneur and the (boring but smart) Bank Manager

The Neoclassical School of thought (anything other than the Austrian School) seems to absent the entrepreneur from all of economics. To them, economics is about the dispassionate, efficient allocation of resources, rather than a dynamic (ongoing) and creative process of discovery and capital allocation. This is why such otherworldly investment strategies can gain traction with otherwise very intelligent men, with such horrendous consequences for our economy.

The Three Stages of Entrepreneurship

Our starting point is the initial discovery of information. For example, the simple observation I have in my business that people want their meat and fish delivered, cut to a bespoke specification, packaged in a certain way, and sold with local provenance that floats their boat. Each of these factors makes the consumer want to buy from me rather than my competition.

Then, by my actions, I seek to train highly skilled people to do the bespoke cutting, go to inventors of packaging and get modifications here and modifications there, and source meat and fish from the desired locations: the farms and seas nearest to me. I signal out to other entrepreneurs that there are opportunities to provide me with these things to help me stratify my ex novo idea.

Other entrepreneurs then respond and supply me in the knowledge that things they might not have thought of as being valuable are now actually valuable.

Note that the co-ordination of my ex novo thought or idea then causes a trickle through the economy of further co-ordination to others and a new alignment of resources is propagated.

Others may well have the same idea and different patterns of co-ordination arise, which causes capital to be realigned to the new set of needs. This is a constant and ongoing process of creation, discovery, and co-ordination. IT CAN NEVER BE SUMMED UP BY EQUATIONS AND MODELLED BY ANY MATHEMATICS. It is a process driven totally by subjective decisions.

The role of a fund manager is to understand what entrepreneurs are tailoring their business to and why. They do not have to have the initial creative idea; the fact that they are employed as a fund manager probably indicates that they do not have this skill. Their skill is to understand the opportunity the entrepreneur has seen, then understand how he is seeking to meet this need. Is the opportunity real? Is the plan realistic? As I said in the original book review,

if a portfolio manager is so dull that he can’t look for companies with at least some of the following traits, then perhaps he is in the wrong business:

  1. Strong balance sheet with good equity and retained profits for a rainy day,
  2. Ongoing investment,
  3. Inspiring entrepreneurial leadership,
  4. Good executive management,
  5. Sensational, functional product,
  6. High or strong barriers to entry

The unique and economically vital role of the fund manager is not to look at a black box, “quantifiable” system and deploy “objective” mathematical equations to determine what stocks he should pick and who he should allocate capital across his portfolio to, but instead to exercise some judgement about the merits of the entrepreneur’s own creative talents and his ability to actually make what he sees happen in a solid and profitable way.

This, of course, is what the bank manager used to do. This elusive member of our species certainly did exist when I started business, but by the early 90’s he was replaced by “relationship managers” who were never there for more than 18 months at a time.  His care was to facilitate this co-ordination process of matching capital with successful entrepreneurs to help facilitate the ongoing, creative and dynamic co-ordination of the market. This should be the purpose of portfolio management.

It is little wonder that the whole house of cards has fallen down if capital has been allocated over the last 30 plus years less under the subjective guidance of entrepreneurship and more via objective Modern Financial Theory.

Appendix: The Alchemists of Loss Named

Courtesy of Dowd and Hutchinson, here are the names and profiles of the Alchemists of Loss:

Appendix 1: Some Leading Financial Alchemists …

Louis Bachelier (France, 1870–1946)

Grandfather of modern financial alchemy. A mediocre student at the Sorbonne, his 1900 PhD thesis “Theorie de la Speculation” laid out a mathematical description of Brownian motion (the motion visible under a microscope through vibrating molecules randomly bashing dust particles), but applied it to stock market prices rather than particle physics, as would have been natural. In spite of patronage by his instructor Henri Poincaré, France’s leading mathematician, the thesis only drew the grade of “honorable.” Albert Einstein later improved the Brownian motion mathematics, publishing it in 1905 with its proper physical application. Bachelier later had an academic career that might be politely described as peripatetic, interrupted by service as a private on the Western Front.

By applying a perfectly good physics model to finance, and assuming Gaussian randomness of prices and zero net expectation in markets, Bachelier was inadvertently the creator of a new discipline that ultimately led to multi-trillion dollar losses more than a century later. His work was obscure but not entirely forgotten, influencing the Soviet mathematician Andrei Kolmogorov (1903–87) and then becoming a source for the work of Benoit Mandelbrot and Franco Modigliani, both of whom studied in Paris in their youth.

Fischer Black (1938–95)

A third of the Black-Scholes-Merton options valuation equation. Degree and PhD in applied mathematics from Harvard, after which he spent some time at Arthur D. Little. Joined the University of Chicago in 1971, where he did some interesting work on the theory of moneyless monetary systems and propounded the remarkable theory that monetary policy was irrelevant to the economy’s movements.

His most famous work, with Myron Scholes, the eponymous option valuation equation, was published in a paper “The pricing of options and corporate liabilities” in January 1973. This was just in time for the explosion in options trading, and enabled him to move to a lucrative position with Goldman Sachs in 1984. He was unlucky enough to die of throat cancer two years before the work leading to his equation was honored with the 1997 Nobel Prize.

Robert F. Engle (1942–)

Inventor of Autoregressive Conditional Heteroskedasticity (ARCH) methods of volatility forecasting, initially applied to the UK inflation rate; this subsequently generalized by his former student Tim Bollerslev to create the GARCH model, which was then widely applied to financial volatilities. Co-founder of the Society for Financial Econometrics. BS in Physics (Williams, MS in Physics, PhD in Economics (Cornell). Professor at UC San Diego, 1975–2003. The co-inventor, with Clive Granger, of the theory of cointegration, which models equilibrium relationships between trended variables.

Eugene Fama (1939–)

Propounded the Efficient Market Hypothesis in his 1965 doctoral thesis “The behavior of stock market prices.” PhD in Economics from University of Chicago, with Benoit Mandelbrot as doctoral supervisor.

He also played a leading role in the development of the Capital Asset Pricing Model, before putting it out of its misery a generation later in a landmark paper with Kenneth French. Apart from Black, Bachelier, and Gauss (all dead), he is the only leading Financial Alchemist not to get the Nobel. It is unclear why he has been snubbed in this way; it’s not as if any of the other financial alchemists’ theories worked either.

Carl Friedrich Gauss (1777–1855)

Son of a gardener in Brunswick, Germany, he is ranked in the “objective scoring system” of Charles Murray’s Human Accomplishments as the fourth greatest mathematician of all time. He was reputedly the last mathematician to know all of the mathematics of his day. Inventor of the famous Gaussian probability distribution, often mislabeled the “normal distribution,” which is a lot less normal than its proponents normally suggest. Also invented modular arithmetic and proved the fundamental theorem of algebra showing that any polynomial in a single variable has at least one root. His calculations enabled astronomers to identify Ceres, the first identified asteroid. Identified a method for representing the unit of magnetism (the Gauss). Disliked teaching and was not a prolific writer.

Gauss married twice and had six children, but maintained poor relations with his two sons, whom he would not allow to become mathematicians “for fear of sullying the family name.”

Gauss would have rejoiced in the practical and lucrative uses to which his probability distribution has since been put. However, as a highly rigorous yet intuitive mathematician, he would undoubtedly have spotted the fundamental flaws underlying Modern Financial Theory the moment he saw it.

Harry Markowitz (1927–)

Inventor of Modern Portfolio Theory, published in the Journal of Finance in 1952. PhD, University of Chicago in Economics. Developed the Markowitz frontier, under which the risk/expected return function of all securities in an optimal portfolio lies on a single Markowitz Efficient Frontier curve, which helped pave the way for the later Capital Asset Pricing Model. Worked at Rand Corporation, founded CACI International, pioneer in computer simulation and a well-read and open-minded polymath. Professor at UC San Diego. Awarded Nobel Prize in 1990 for development of portfolio theory, along with Merton Miller and William F. Sharpe.

Robert F. Merton (1944–)

Generalized the Black-Scholes options valuation equation and then produced an inter-temporal version of the Capital Asset Pricing Model.

Hailed by Paul Samuelson as the Isaac Newton of Modern Finance, which, since Newton was a keen alchemist, is only appropriate. Professor at MIT Sloan School of Management, 1970–88, Harvard Business School, 1998–. Spectacularly eventful consulting career. With Myron T. Scholes, was a Director of Long-Term Capital Management, which slid famously into collapse in 1998, causing a major crisis and triggering a panicked bailout led by the Federal Reserve. Chief Science Officer of Trinsum Group, a financial advisory firm that filed for bankruptcy protection in January 2009. Awarded Nobel Prize jointly with Myron T. Scholes for their work on options valuation.

Merton Miller (1923–2000)

Co-author with Franco Modigliani of Modigliani-Miller theorem, which proposed the irrelevance of debt-equity structure. PhD in Economics from Johns Hopkins University, 1952. At Carnegie-Mellon in 1958, jointly authored paper “The cost of capital corporate finance and the Theory of Investment” propounding the Modigliani-Miller theorem, which was sometimes used to give a spurious respectability for grossly excessive leverage in US financial and corporate systems, and among US consumers. A leading advocate of the benefits of financial derivatives – he often claimed that financial derivatives made the world a safer place rather than a more dangerous one – and free financial markets.

Professor, University of Chicago, 1961–93. Nobel Prize, 1990 with Harry Markowitz and William F. Sharpe.

Franco Modigliani (Italy/US, 1918–2003)

Primarily a macro-economist. Co-author with Merton Miller of Modigliani-Miller theorem, allegedly after he and Miller had been assigned to teach corporate finance at Carnegie-Mellon to business school students, and as good economists determined that the existing texts were internally contradictory. Modigliani also propounded the life-cycle theory of saving in the economy, in parallel to Milton Friedman’s “permanent income” theory, which supposed that consumers would aim for a stable level of consumption through their lifetime, saving in early years to fund their retirement. Left Italy in 1939 for France, then came to US in 1942. D.Soc.Sci., New School for Social Research. Professor, Carnegie-Mellon, then MIT 1962–2003. Nobel Prize, 1985.

Myron T. Scholes (Canada/US, 1941–)

Co-author with Fischer Black of Black-Scholes option valuation model. BA Economics McMaster University, PhD/MBA, University of Chicago. Professor, MIT 1968–73, University of Chicago, 1973–81, Stanford, 1981–96. Director of Long-Term Capital Management with Robert F. Merton, which collapsed in spectacular fashion in 1998. From 1999, Chairman of Platinum Grove Asset Management, $5 billion hedge fund which was forced to suspend withdrawals in October 2008 after a 38% loss, then lost another 11% in March 2009 and by October 2009 was in a Special Rebalancing Situation, bankrupt in all but name. Nobel Prize 1997 with Robert F. Merton for the European call option valuation model.

William F. Sharpe (1934–)

Devised the Capital Asset Pricing Model, published as “Capital asset prices – a theory of market equilibrium under conditions of risk” in the Journal of Finance in 1964. MA, PhD in Economics from UCLA. Professor, Stanford, 1970–89. Also devised the Sharpe ratio measuring the return of a security in relation to its risk. One of his doctoral students Howard Sosin founded AIG Financial Products, whose activities in the CDS market were a major contributor to the recent financial crisis.

Co-founder of the consulting firm Financial Engines, which encourages its clients to save more for the retirement that, thanks to Modern Finance, many of them will never live to see. Nobel Prize, 1990 with Harry Markowitz and Merton Miller

Appendix 2: … And Some Non-alchemists

Augustin Louis, Baron Cauchy (France, 1789–1857)

Father of the Cauchy distribution, the ultimate long-tailed risk. Born into a Royalist family and spent his first five years hiding from the French revolutionaries deep in the countryside. Educated at the new Bonapartist École Polytechnique, where he objected to the military discipline, then became an engineer. After a few years of engineering, he returned to Paris in 1812 and switched to mathematics. Three years later, when Napoleon fell and the Bourbons were restored, as a well known Royalist he was appointed a professor at the reorganized École Polytechnique in December 1815 and a member of the Academie des Sciences the following year.

As a professor, he was not entirely successful, since he took his students through higher mathematics at a brisk, rigorous trot that baffled all but the best of even the École Polytechnique’s elite. He designed the Cauchy stress tensor, central to the theory of elasticity, and Cauchy’s integral theorem, which led to the development of the theory of complex functions and his proof of Taylor’s theorem, central to calculus. In mathematical papers produced, he was second only to Leonhard Euler.

Then in 1830, disaster struck. The reactionary Charles X was overthrown and Cauchy went into exile, refusing to swear an oath of allegiance to the new regime of Louis Philippe. In exile he was for five years tutor to the legitimist heir Henri d’Artois, Duke of Bordeaux, an exercise that left the Duke with a lifelong hatred of mathematics and Cauchy with a legitimist (and therefore, alas, legally unofficial) barony.

He was only readmitted to the École Polytechnique after Louis Philippe was himself overthrown.

Cauchy was an eccentric reactionary, but a very great mathematician; he ranks eighteenth all-time among mathematicians (above Fibonacci and Archimedes) in Charles Murray’s Human Accomplishments.

Benoit Mandelbrot (France/US 1924–)

Should be thought of as the Robert Boyle or Antoine Lavoisier, who began to move the world of finance beyond alchemy. PhD, Mathematical Sciences, Paris. Centre National de la Recherche Scientifique, 1949–57; Institute of Advanced Study, Princeton, 1953–54. Moved to US in 1958. Fellow, IBM Research Centre, 1958–90. Also taught as Visiting Professor at Harvard and Yale. Mandelbrot discovered as early as 1962 that financial market prices did not follow a Gaussian distribution: cotton prices in fact followed a Levy stable distribution with constant of 1.7 instead of 2 as in a Gaussian.

Mandelbrot invented fractal geometry, which he named in 1975, publishing The Fractal Geometry of Nature in 1982, Chapter 37 of which is “Scaling and price change in Economics.” His 1997 book Fractals and Scaling in Finance and his 2004 book The (Mis-) Behaviour of Markets exploded many of the axioms of Modern Finance, without posing a wholly satisfactory alternative paradigm.

The only possible excuse the Nobel people have for not having awarded him one or two Nobel Prizes is the lack of a Nobel Prize for Mathematics. Even so, he is a gap in the Economics Nobel line-up.

Alternatively, it might be more appropriate if the Sveriges Riksbank would end the Economics Nobel Prize as a failure: strictly, it is isn’t a true Nobel at all; it was not part of Alfred Nobel’s legacy, but a much later add-on to pander to the economics profession’s vain pretensions of scientific respectability. If we judge a science by the hallmark of predictability, then the predictions of economists are no better than those of ancient Roman augurs or modern taxi drivers; alternatively, we can judge it by its contribution to “scientific” knowledge, in which case the contribution that financial economics has made makes us wonder if the agricultural alchemist Lysenko shouldn’t have got a Nobel himself; or we can judge it by its contribution to the welfare of society at large, in which case the undermining of the capitalist system, the repeated disasters of the last twenty years, the immiseration of millions of innocent workers and savers, and the trillion dollar losses of recent years surely speak for themselves. [10]

[1] Alchemists of Loss (AofL) How Modern Finance and Government Intervention Crashed the Finance System by Kevin Dowd and Martin Hutchinson, Published by Wiley 2010

[2]AofL page 65

[3] AofL page 66

[4] AofL page 66

[5] AofL page 67

[6] AofL page 67-68

[7] AofL page 72

[8] AofL page 72-73

[9] Mises (1963) Human Action , Chicago IL. P890

[10] AofL page 80-86

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