Flaws of Economic Models: Differentiating Social Sciences from Natural Sciences

David Freedman of the Scientific American asks “Why Economic Models Are Always Wrong?”

He gives the answer,

The problem, of course, is that while these different versions of the model might all match the historical data, they would in general generate different predictions going forward--and sure enough, his calibrated model produced terrible predictions compared to the "reality" originally generated by the perfect model. Calibration--a standard procedure used by all modelers in all fields, including finance--had rendered a perfect model seriously flawed. Though taken aback, he continued his study, and found that having even tiny flaws in the model or the historical data made the situation far worse. "As far as I can tell, you'd have exactly the same situation with any model that has to be calibrated," says Carter.

That financial models are plagued by calibration problems is no surprise to Wilmott--he notes that it has become routine for modelers in finance to simply keep recalibrating their models over and over again as the models continue to turn out bad predictions. "When you have to keep recalibrating a model, something is wrong with it," he says. "If you had to readjust the constant in Newton's law of gravity every time you got out of bed in the morning in order for it to agree with your scale, it wouldn't be much of a law But in finance they just keep on recalibrating and pretending that the models work."

We can’t simplify, through mathematical models, what truly is a highly complex environment. Repeated “recalibrating their models” or “calibration problems” only exposes on these structural analytical errors.

The ultimate reason why economic models are always wrong is that investigations have been patterned after natural sciences. Yet analyzing natural sciences isn’t the same as social sciences. That’s what modelers and their disciples cannot seem to grasp.

The great Ludwig von Mises draws a clear distinction between the two sciences, (bold emphasis mine, italics original)

Since the elements of social cognition are abstract and not reducible to concrete images one would like to have metaphors. First there were biological metaphors, now mostly mechanistic ones. These are based in positivist view of social science that holds that social science should be built up by experimental method as ideally applied in Newtonian physics. Economics becomes experimental, mathematical and about measurement. This is all wrong:

1. Social sciences cannot be based on experience like the natural sciences. Social experience is of a complexity and cannot be experimented with

2. Therefore the social sciences can never use experience to verify their statements. Every fact and experience is open to multiple interpretations (but see Kuhn. KS)

3. The impossibility of experimenting implies the impossibility of measurement. In human behavior there are no invariable relations like there are between physical properties, which means that it is pointless to mathematize them in order to make predictions. Statistics merely studies history.

4. Mathematics does not deal with actual operations of human actions but with a fictitious concept, static equilibrium (tomorrow is like today, no uncertainty), that economists build up for instrumental purposes. But not only is this unrealistic, it is also inconsistent for lack of uncertainty and change implies lack of actions. The only purpose mathematics can have in economics is the study of the nature of relations between costs and prices and thereby of profits.

5. Mathematics cannot tell us how the market arrives at a static equilibrium.

6. Mathematicians are prone to consider the price either as measurement of value or as equivalent to the commodity. But prices are neither; they are simply the amount of money exchanged for a commodity and there is reversed valuation.

Economics deals with human action, not with objects (as physics does) such as commodities, economic quantities or prices. Therefore economists do not consider their subject matter from without, but from within, through our own understanding of what it is to be human and to act. What makes natural science possible is the power to experiment, what makes social science possible is the power to grasp the meaning of human action….

Social sciences have a distinct method, praxeology and verstehen, due to the special character of their objects, and owe their progress through it and do not have to and cannot use the method of the natural sciences.

Praxeological concepts refer exactly and with certainty to the reality of human action because both the science of human action and human action itself have their toot in human reason. The quantitative approach would not render them more exact.

Nobody denies that economics is not perfect yet, but:

1. the present unsatisfactory state of social and political affairs is not due to deficiencies in economic theory, but in policy. People just don’t use economic theory enough.

2. even if economics needs to be drastically reformed someday it cannot take the direction proposed by those who use the model of the natural sciences. This idea has been thoroughly refuted forever.

Again many people seem to find comfort in models, for many possible reasons such as social signaling, conversation, career, politics and others.

But in terms of the predictive value, as the Scientific American article’s inquiry as indicated by the title, economic models have always been wrong.

Do Americans Buy Stuffs They Don’t Need?

One of the most outrageous obsessions by the mainstream is to substitute statistics for human action then apply political correctness when interpreting them.

This Wall Street Journal Blog should be an example (bold highlights mine)

As it turns out, quite a lot. A non-scientific study of Commerce Department data suggests that in February, U.S. consumers spent an annualized $1.2 trillion on non-essential stuff including pleasure boats, jewelry, booze, gambling and candy. That’s 11.2% of total consumer spending, up from 9.3% a decade earlier and only 4% in 1959, adjusted for inflation. In February, spending on non-essential stuff was up an inflation-adjusted 3.3% from a year earlier, compared to 2.4% for essential stuff such as food, housing and medicine.

To be sure, different people can have different ideas of what should be considered essential. Still, the estimate is probably low. It doesn’t, for example, account for the added cost of certain luxury items such as superfast cars and big houses.

Interestingly, people who spend more on luxuries have experienced less inflation. As of February, the weighted average price of non-essential goods and services was up only 0.2% from a year earlier and 82% from January 1959, according to the Commerce Department. By contrast, the cost of all consumer goods was up 1.6% from a year earlier and 520% from January 1959.

The sheer volume of non-essential spending offers fodder for various conclusions. For one, it could be seen as evidence of the triumph of modern capitalism in raising living standards. We enjoy so much leisure and consume so much extra stuff that even a deep depression wouldn’t – in aggregate — cut into the basics.

Alternately, it could be read as a sign that U.S. economic growth relies too heavily on stimulating demand for stuff people don’t really need, to the detriment of public goods such as health and education. By that logic, a consumption tax – like the value-added taxes common throughout Europe—could go a long way toward restoring balance.

It’s absurd to say that we buy stuff which we don’t need.

While the author does say “different people can have different ideas of what should be considered essential”, he seems confused on why people engage in trade at all.

Moreover, saying that Americans "buy stuffs they don’t need" translates to an ethical issue with political undertones: the author seems to suggest that Americans have wrong priorities or have distorted set of choices! Of course the implication is that only the government (and the author) knows what are the stuffs which people truly needs, thus his justification for a consumption tax! (Put up a strawman then knock them down!)

Although the author attempts to neutralize the political flavor of his article by adding an escape clause: that the stats signify as “evidence of the triumph of modern capitalism in raising living standards”.

People buy to express their demonstrated preference. Yet such preference gets screened from a set of given ordinal alternatives (e.g., 1st, 2nd, 3rd, etc..) from which the individual makes a choice or a decision. And that choice (buying) constitutes part of human action.

As Professor Ludwig von Mises explained, (bold highlights mine)

Action is an attempt to substitute a more satisfactory state of affairs for a less satisfactory one. We call such a willfully induced alteration an exchange. A less desirable condition is bartered for a more desirable. What gratifies less is abandoned in order to attain something that pleases more. That which is abandoned is called the price paid for the attainment of the end sought. The value of the price paid is called costs. Costs are equal to the value attached to the satisfaction which one must forego in order to attain the end aimed at.

If I buy beer (1^st order) at this moment at the cost of my other alternatives: a steak (2^nd order) or a chocolate (3^rd order), does my choice for beer represent stuff I don’t need?

The instance that I made a sacrifice (steak and chocolate) to make a choice (beer) makes my decision part of my act to fill a personal unease or a “need”.

It may not be your need, but it is mine. My actions reflect on my preference to solve my need.

The fact that beer is produced and sold implies that it is an economically valuable product (estimated at $325 billion industry for the world for 2008). Many people “needs” it and would pay (hard earned or otherwise) money for it.

The difference lies in the values ascribed to it by different consumers.

Some see beer as a way to socialize, or a way to get entertained or to get promoted or to close deals or as stress relief or a part of the ancillary rituals for other social activities or for many other reasons as health.

Some may not like beer at all!

The point is people consume or don’t consume beer for different reasons. As an economic good, beer is just part of the ordinal alternatives for people to choose from, aside from chocolate or steak or other goods or services.

Suggesting that beer isn’t a stuff we need, as a beer consumer, severely underappreciates the way we live as humans.

Abraham Maslow proposed that human needs come in the form of a pyramid. He breaks them down into 5, namely physiological, safety, social (love and belonging), esteem and self-actualization.

As the Wikipedia explains, (bold highlight mine)

Maslow's hierarchy of needs is often portrayed in the shape of a pyramid, with the largest and most fundamental levels of needs at the bottom, and the need for self-actualization at the top.

The most fundamental and basic four layers of the pyramid contain what Maslow called "deficiency needs" or "d-needs": esteem , friendship and love, security, and physical needs. With the exception of the most fundamental (physiological) needs, if these "deficiency needs" are not met, the body gives no physical indication but the individual feels anxious and tense. Maslow's theory suggests that the most basic level of needs must be met before the individual will strongly desire (or focus motivation upon) the secondary or higher level needs. Maslow also coined the term Metamotivation to describe the motivation of people who go beyond the scope of the basic needs and strive for constant betterment Metamotivated people are driven by B-needs (Being Needs), instead of deficiency needs (D-Needs).

From my example, my choice for a beer may not signify a physiological (basic need), yet they could reflect on my other deficiency needs (emotion or esteem or social needs).

In other words, B (being)-needs may not be D (deficiency)-needs but they still represent as human ‘intangible’ NEEDS.

Bottom line:

Statistics, which accounts for mainstream’s obsessions, fails to incorporate the intangible or non-material aspects of human nature.

It is, thus, misleading to make the impression that by reducing people’s activities to quantitative equations, or to dollar and cents, patterned after physical sciences, governments can manage society efficiently.

As the great Friedrich von Hayek admonished, in his Nobel lecture “the Pretence of Knowledge” (bold highlights mine)

While in the physical sciences it is generally assumed, probably with good reason, that any important factor which determines the observed events will itself be directly observable and measurable, in the study of such complex phenomena as the market, which depend on the actions of many individuals, all the circumstances which will determine the outcome of a process, for reasons which I shall explain later, will hardly ever be fully known or measurable. And while in the physical sciences the investigator will be able to measure what, on the basis of a prima facie theory, he thinks important, in the social sciences often that is treated as important which happens to be accessible to measurement. This is sometimes carried to the point where it is demanded that our theories must be formulated in such terms that they refer only to measurable magnitudes.

Lastly the notion that people don’t know of their priorities seems plain silly and downright sanctimonious.

Besides, governments compose of people too which makes the whole quantitative statistical exercise as self-contradictory.

Beware Of Economists Bearing Predictions From Models

Max Borders explains (emphasis added)

"So what do all these macroeconomic models have in common?

-They’re rendered either in impenetrable math or with sophisticated computers, requiring a lot of popular (and political) faith.

-Politicians and policy wizards hide behind this impenetrability, both to evade public scrutiny and to secure their status as elites.

-Models vaguely resemble the real-world phenomena they’re meant to explain but often fail to track with reality when the evidence comes in.

-They’re meant to model complex systems, but such systems resist modeling. Complexity makes things inherently hard to predict and forecast.

-They’re used by people who fancy themselves planners—not just predictors or describers—of complex phenomena."

The point is, according to Richard Ebeling, ``The inability of the economics profession to grasp the mainsprings of human action has resulted from the adoption of economic models totally outside of reality. In the models put forth as explanations of market phenomena, equilibrium — that point at which all market activities come to rest and all market participants possess perfect knowledge with unchanging tastes and preferences — has become the cornerstone of most economic theory."

Yet many people stubbornly refuse to learn from the lessons of the last crisis.

The mainstream hardly saw the last crisis from ever occurring:

This is why the Queen of England in 2009 censured the profession's failure to anticipate the crisis.

This also why US investment banks became an extinct species in 2008 as remnant banks were converted into holding companies, as losses strained the industry's balance sheets, which forced these banks into the government's arms.

This also why Ponzi artist Bernard Madoff gypped, not only gullible wealthy individuals but importantly a slew of international financial companies.

And this is also why contrarian John Paulson was able to capitalize on shorting the housing bubble via Goldman Sachs, which became a recent controversy, because the other side of the trade had been 'sophisticated' financial companies.

In retrospect, not only was the mainstream composed of highly specialized institutions, which were not only model oriented, but had an organization composed of an army of experts that have not seen, anticipated, predicted or expected these adverse events.

It is also important to point out that not only are the models unrealistic but those making these models are people with the same frailties whom they attempt to model. These people are also subject to the same biases that helped skew the models, which they try to oversimplify or see constancy in a dynamic world. They are also subject to Groupthink and the influences of Dopamine in their decision making process.

Adds Mr. Borders, (emphasis added)

``What does this mean for economics as a discipline? I think it’s time we admit many economists are just soothsayers. They keep their jobs for a host of reasons that have less to do with accuracy and more to do with politics and obscurantism. Indeed, where do you find them but in bureaucracies—those great shelters from reality’s storms? Governments and universities are places where big brains go to be grand and weave speculative webs for the benefit of the few.

``And yet “ideas have consequences.” Bureaucracies are power centers. So we have a big job ahead of us. We’ve got to do a seemingly contradictory thing and make the very idea of complex systems simple. How best to say it? Economists aren’t oracles? Soothsaying is not science? Ecosystems can’t be designed?

“The very term ‘model’ is a pretentious borrowing of the architect’s or engineer’s replica, down-to-scale of something physical,” says Barron’s economics editor, Gene Epstein. “These are not models at all, but just equations that link various numbers, maybe occasionally shedding light, but often not.”

Bottom line: Incentives and stakeholdings largely determine the mainstream's fixation to models.

Many are driven by ego (desire to be seen as superior to the rest), others are driven by politics (use math models to justify securing the interests of particular groups), some by groupthink (the need to be seen in the comfort of crowds), some because of personal benefits (defense of political or academic career, stakeholdings in institutions or markets or businesses) and possibly others just for the plain obsession to mathematical formalism.

At the end of the day, logic and sound reasoning prevails.

Should We Follow Wall Street?

``There are two requirements for success in Wall Street. One, you have to think correctly; and secondly, you have to think independently." - Benjamin Graham

For some, there is the impression that the workings of Wall Street have to be piously followed by the letter.

The general notion is that Wall Street has devoted unremitting years of research on the subjects of risk management, portfolio allocation and asset pricing or valuations such that these need to be incorporated into conventional analysis.

That is the reason why guild like certification standard as the Chartered Finance Analyst (CFA) has emerged.

Hence, should we follow what Wall Street does?

While technically Wall Street can be identified as a symbolic location for the operating platforms of the various asset markets, it has been generally been associated with the investment community.

However, Wall Street, for me, is a broad, vague and complex issue.

Wall Street Models Are For Convenience, Myth of Blue Chip Investing

From the recent crisis, we learned that Wall Street has been ground zero for the financial alchemy crisis of turning the proverbial stones into bread via the traditional models of mortgage credit risk management of “originate and hold” into the latest model of “originate and distribute”. Where risks had been passed like a hot potato the impact has been contagion-globalized crisis.

It has been likewise the birthplace of the Shadow Banking System, which encompassed the circumvention of regulations and signified as the gaming of the system (regulatory arbitrage) in cahoots with credit ratings agencies, whose mandated revenue model had been derived from the issuers- than from the risks buyers-from whose interests it protected (Agency Problem), and regulators (regulatory capture)-who refused to take the proverbial punch bowl away.

Wall Street has most importantly played a critical role in the transmission of the US Federal Reserve policies in overextending the credit system intermediation…globally.

Here we quote Prudent Bear’s Doug Noland, ``to create Trillions of instruments (chiefly Treasuries, agency debt, MBS, and “Repos”) perceived as safe and liquid by our foreign trading partners that accommodated our massive current account deficits (and attendant domestic and international imbalances). It was contemporary risk intermediation at the heart of a historic mispricing of finance for, in particular, mortgages and U.S. international borrowings. And it was the potent interplay of contemporary risk intermediation and contemporary monetary management/central banking (i.e. “pegged” interest rates, liquidity assurances, and asymmetrical policy responses) that cultivated unprecedented financial sector and speculator leveraging.” (emphasis added)

It had likewise operated on the psychology predicated on the Greenspan or Bernanke Put or the principle of Moral Hazard that has emboldened speculation or expanded risk taking capacity.

In sum, Wall Street has been THE EPICENTER and THE EPITOME of bubble dynamics- where the rigors of long term discipline has been exchanged with short term profit and fun at the expense of the US and global economy.

The great value investor Benjamin Graham, Warren Buffet’s mentor once sardonically remarked on the same shortsightedness, ``That concerns me, doesn't it concern you?... I was shocked by what I heard at this meeting. I could not comprehend how the management of money by institutions had degenerated from the standpoint of sound investment to this rat race of trying to get the highest possible return in the shortest period of time. Those men gave me the impression of being prisoners to their own operations rather than controlling them... They are promising performance on the upside and the downside that is not practical to achieve.” (emphasis mine)

So how effective has Wall Street been to predict and respond to the crisis?

From Bruce Bartlett, author and former US Treasury department economist in a recent Forbes article, ``Economists were slow to see a recession coming and often didn't see one at all until we were already well into it.”

From Robert Samuelson, economist and contributing editor of Newsweek in his latest article Economist Out Of Lunch (bold highlights mine), ``Well, if you de-emphasize financial markets and financial markets are decisive, you're out to lunch. Financial markets pumped up the real estate bubble; greater housing and stock market wealth inspired a consumer spending boom; losses on "subprime" mortgage securities triggered a collapse of confidence. Some economists have grudgingly, if obscurely, conceded error. A study by the International Monetary Fund called "Initial Lessons of the Crisis" admits: There "was an under-appreciation of systemic risks coming from . . . financial sector feedbacks onto the real economy." That's an understatement.

``Overshadowing the misunderstanding of finance is a larger mistake: ignoring history. By and large, most economists don't care much about history. Introductory college textbooks spend little, if any, time exploring business cycles of the 19th century. The emphasis is on "principles of economics" (the title of many basic texts), as if most endure forever. Economists focused on constructing elegant, mathematical models.”

Or how about from one of my favorite contrarians Black Swan author Nassim Taleb with Mark Spitznagel in an article at the Financial Times “Time to tackle the real evil: too much debt”, ``Relying on standard models to build policies makes us all fragile and overconfident. Asking the economics establishment for guidance (particularly after its failure to see the risk in the economy) is akin to asking to be led by the blind – instead we need to rebuild the world to make it resistant to the economist’s mystifications.”

Considering the vast armies of financial experts (accountants, CFAs, economists, quant risks modelers and managers, statisticians, actuarial, research and security analysts and etc…) employed in the banking and financial industry, common sense inference suggest that we wouldn’t have seen the disappearance of the US Investment Banking Sector (bankruptcy of Lehman Bros, the acquisition of Bear Stearns and Merrill Lynch and the conversion to Bank holding companies of Goldman Sachs and Morgan Stanley) and the government takeover of AIG-once largest insurance company in the US and the 18^th largest in the world, had these models or theories worked.

The fact that the crisis occurred and heavily impacted the US financial system occurred demonstrates that as the experts quoted above, Wall Street failed!

This also utterly demolishes the MYTH of BLUE CHIP investing- where the public have been ingrained to believe that investing in blue chips are safe and sound and least subjected to risks.

In bubble cycles, particularly with growing relevance today [see last week’s Worth Doing: Inflation Analytics Over Traditional Fundamentalism!], industries exposed to the extremities of misallocation due to policy based distortion are all subjected to heightened risks regardless of their stature.

The oldest of the 30 elite members of Dow Jones Industrials (Answers.com) are the Proctor and Gamble (1932), United Technologies (1939), Exxon Mobil (1928) and Du Pont (1935). All the rest have been included in since 1959 and above, and where the 30 member composite index has undergone several changes- 49 alterations according to wikipedia.org (since May 26,1896).

This means that in the US, blue chips aren’t exactly “blue” in the sense that they been exposed to the variable changes in technology or management or bubbles or other factors which prompted for the restructuring of the blue chip index.

So what has been the problem with Wall Street?

As noted in the past in How Math Models Can Lead To Disaster and in the above, “elegant” mathematical and or scientific models that has reinforced the public’s tendencies to rely on heuristics or mental shortcuts.

Like government policies, the theoretically or math constructed models served as intellectual justification or cover to advance on their biases.

Essentially it isn’t about what works or not, it is about what needs to be believed that counts. In short, it has all been about convenience.

For instance, in a bullmarket you need an excuse to push up stocks, instead of relying on gossip based information, the public embraced Wall Street’s models.

Value investor Ben Graham in his 1949 classic the Intelligent Investor castigated on the industry’s inclination towards this, when he wrote, ``security analysts today find themselves compelled to become most mathematical and 'scientific' in the very situations which lend themselves least auspiciously to exact treatment." (bold highlight mine)

Stocks For The Long Run?

One of the hardcore or popular beliefs in Wall Street is that investing in stocks would have led to an outperformance of a portfolio relative to Treasury Bonds, Bills or Gold as shown in Figure 1.

Figure 1: Jeremy Siegel: Stocks For The Long Run

Recently at a Wall Street Journal article Mr. Jason Zweig wrote to challenge the conventional Wall Street conviction which has relied on Siegel’s chart. He stated that the data used during the earlier days had been cherry-picked (or data mined) and were therefore NOT accurate.

``There is just one problem with tracing stock performance all the way back to 1802: It isn't really valid,” wrote Mr. Zweig, ``What, then, are the odds that stocks will continue to lag behind bonds for the long run? The sad truth is that history can't tell us the answer. The 1802-to-1870 stock indexes are rotten with methodological flaws. So we have only the period since then, or four distinct and complete 30-year stretches of stock returns, to base our long-term investment decisions on. Another emperor of the late bull market, it seems, has turned out to have no clothes.” (emphasis added)

If Mr. Zweig is correct then imprecise data alone could shatter the very foundations of Wall Street’s most consecrated canon.

And this doesn’t end here.

Contrarian investor Rob Arnott has also confronted the alleged supremacy of the returns of stocks over bonds in the long run, see figure 2

Figure 2: IndexUniverse.com: Stocks Lacks Real Appreciation

In the Journal of Portfolio Management (published by indexuniverse.com), Mr. Arnott, wrote, ``Stocks for the long run? L-o-n-g run, indeed! A mere 20 percent additional drop from February 2009 levels would suffice to push the real level of the S&P 500 back down to 1968 levels. A decline of 45 percent from February 2009 levels— heaven forfend!—would actually bring us back to 1929 levels, in real inflation-adjusted terms.”

In short, stocks could invariably underperform bonds if it continues to fall in real adjusted terms.

If Wall Street icon and guru, Fidelity Investment Peter Lynch once said that, ``In stocks you've got the company's growth on your side. You're a partner in a prosperous and expanding business. In bonds, you're nothing more than the nearest source of spare change. When you lend money to somebody, the best you can hope for is to get it back, plus interest,” on the contrary, Mr. Arnott concludes, (all bold highlights mine), ``Many cherished myths drive our industry’s equity-centric worldview. The events of 2008 are shining a spotlight, for professionals and retail investors alike, on the folly of relying on false dogma.

-For the long-term investor, stocks are supposed to add 5 percent per year over bonds. They don’t. Indeed, for 10 years, 20 years, even 40 years, ordinary long-term Treasury bonds have outpaced the broad stock market.

-For the long-term investor, stock markets are supposed to give us steady gains, interrupted by periodic bear markets and occasional jolts like 1987 or 2008. The opposite—long periods of disappointment, interrupted by some wonderful gains—appears to be more accurate.

-For the long-term investor, mainstream bonds are supposed to reduce our risk and provide useful diversification, which can improve our long-term risk-adjusted returns. While they clearly reduce our risk, there are far more powerful ways to achieve true diversification—and many of them are out-of mainstream.”

So NO, there isn’t any universally accepted Wall Street wisdom, instead they seem to be conditional (cycles) and subject to time referenced debate. This also means that despite the years of drudging research, such insights risks being defective or if not outmoded.

The fact that they are constantly vulnerable to policy induced business cycles exemplifies such shortcomings.

Bluntly put, Holy Grail investing is a delusion even in Wall Street standards.

The Significance Of Policy Based Or Inflation Analytics

Today’s crisis has provoked rapid policy responses among governments.

This entails a material shift in the operating economic and financial environment which should impact the underlying drivers to the risk reward tradeoffs or to the asset pricing mechanics of diverse financial markets.

And any analysis that foregoes these changes and sticks to the old paradigms will likely misinterpret the risk return environment see Figure 3.

Figure 3: CSFI: The Road To Long Finance

Take for instance this splendid observation from Centre for the Study of Financial Innovation CSFI’s Michael Mainelli and Bob Giffords ``Unexpected regulatory intervention may destroy a long-short market neutral hedge, as when short selling was suddenly restricted in several jurisdictions in late 2008. The efficiency of a diverse portfolio may similarly morph into something quite different in a bear market panic. At night all cats are black, no matter how colourful and distinctive they may appear in the daylight.” (emphasis added)

The problem is that risk managers frequently respond only to ex-post (after the fact) events rather than preparing for the ex-ante (before the event).

From the same authors, ``Most risk managers deal with the bottom loop of reacting to accidents and danger. "A one-sided concern for reducing accidents without considering the opportunity costs of so doing fosters excessive risk aversion – worthwhile activities with very small risks are inhibited or banned. Conversely, the pursuit of the rewards of risk to the neglect of social and environmental ‘externalities’ can also produce undesirable outcomes," wrote Adams [John Adams “Risks”, UCL Press London 1995]. This illustrates how easy it is for risk management to yield unexpected consequences.” (emphasis added)

In other words, risk managers like any human being tend to get swayed by emotions that foster extreme pendulum swings of fear and greed.

And why the difficulties in analyzing the dynamics of government policies? Because of the complexities derived from the interweaving feedback loops of human interactions from the web of regulations.

According to Jeffrey Friedman in his paper, A Crisis of Politics and Not Economics: Complexity, Ignorance and Policy Failure, ``The task of researching such interactions, however, illustrates the practical difficulties of minimizing the disasters to which they might lead. Just as a major problem that regulators face is their ignorance of the effects of their actions, especially in conjunction with past regulatory actions, the main problem scholars of regulation may face is that there are so many regulations, and so many historical circumstances explaining them—and so many theories about their effects—that inevitably, the scholars will, here as everywhere, be compelled to overspecialize. The predictable cost is that most scholars will overlook interactions between the rules in which they specialize and the rules studied by specialists in a different subfield—even if they are deliberately attempting (like the super-systemic regulator) to keep the big picture in mind.” (bold highlight mine)

That’s why any risk return analysis from today’s rapidly evolving conditions should always take into the account the evolving policy dynamics.

And policy dynamics tend to differ from country to country, which implies that the impact to markets or the economy could be expected to be dissimilar.

Another favorite iconoclast, Jim Rogers, hits the nail on the head in an interview at the Economic Times, ``I don't pay any attention to things like emerging markets premium. You talk about it on TV, but every market is different. Why can't I just go out and buy emerging markets when it is likely to go broke. Every market is different, every country is different, every economy is different and every sector of the economies is different. Just because you are in an emerging country does not mean you are going to make money if you get the wrong sector.”

Peter Bernstein on Risk and Risk Managment

McKinsey Quarterly presents Peter Bernstein [my apologies I erroneously placed Richard Bernstein earlier]

``The celebrated author of Against the Gods: The Remarkable Story of Risk explores the history of risk and how it works in real-world markets and in our lives.

``Risk doesn’t mean danger—it just means not knowing what the future holds. That insight resides at the core of risk management for companies, whether in managing the potential downside of an investment or putting a value on the option of waiting when making irreversible decisions. In this video Peter L. Bernstein also explains why in the real world the most sophisticated mathematical models can sometimes fail."

Peter Bernstein in this video deals with Risk, Risk Control and Management, Options and Option pricing, and mathematical models that can't input world dynamics.

"It's how you deal with it when it happens"

Update: Learned today June 10th, that risk guru Peter Bernstein has recently passed away at age 90 (Bloomberg). Sad to lose such an inspirational icon, he will surely be missed.

How Math Models Can Lead To Disaster

The crash of Wall Street had been aggravated by people looking for rationales to confirm their beliefs. And there was no better source of inspiration than one modeled after a seemingly impervious mathematical formula.

An article from Wired.com on “Recipe for Disaster: The Formula That Killed Wall Street” by Felix Salmon, gives a splendid narrative.

Some excerpts (all bold highlights mine),

``For five years, Li's formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.

``His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored….

``It was a brilliant simplification of an intractable problem. And Li didn't just radically dumb down the difficulty of working out correlations; he decided not to even bother trying to map and calculate all the nearly infinite relationships between the various loans that made up a pool. What happens when the number of pool members increases or when you mix negative correlations with positive ones? Never mind all that, he said. The only thing that matters is the final correlation number—one clean, simple, all-sufficient figure that sums up everything.

``The effect on the securitization market was electric. Armed with Li's formula, Wall Street's quants saw a new world of possibilities. And the first thing they did was start creating a huge number of brand-new triple-A securities. Using Li's copula approach meant that ratings agencies like Moody's—or anybody wanting to model the risk of a tranche—no longer needed to puzzle over the underlying securities. All they needed was that correlation number, and out would come a rating telling them how safe or risky the tranche was…

``As a result, just about anything could be bundled and turned into a triple-A bond—corporate bonds, bank loans, mortgage-backed securities, whatever you liked.

``In the world of finance, too many quants see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years' worth of data and come up with probabilities for things that may happen only once every 10,000 years. Then people invest on the basis of those probabilities, without stopping to wonder whether the numbers make any sense at all.

``As Li himself said of his own model: "The most dangerous part is when people believe everything coming out of it."

Some observations:

One. This is another example of people how people (including the majority of experts and professionals) fall prey to cognitive biases, such as the confirmation bias, in order to buttress their beliefs.

Two. This also shows of people’s penchant to trustingly espouse mathematical models to address the concerns of social structures especially in markets.

The illustrious Friedrich A. Hayek (1899–1992) in his Nobel Prize speech The Pretence of Knowledge warned of this, ``A theory of essentially complex phenomena must refer to a large number of particular facts; and to derive a prediction from it, or to test it, we have to ascertain all these particular facts. Once we succeeded in this there should be no particular difficulty about deriving testable predictions — with the help of modern computers it should be easy enough to insert these data into the appropriate blanks of the theoretical formulae and to derive a prediction. The real difficulty, to the solution of which science has little to contribute, and which is sometimes indeed insoluble, consists in the ascertainment of the particular facts.”

For pundits, math models elicit intellectual attraction of a system that can be deliberately controlled. However, the sad reality is that they hardly capture all variables required out of the complexity of the environment. And over reliance thereof may lead to disastrous consequences, similar to the LTCM fiasco.

And this applies not only to Wall Street but to the general economics or even to the environment.