Rethinking Economics: Let’s Get This Schism Started

Doctors of Doctrine

In the wake of the great crash of 2008 a lot of young people suddenly became interested in finance and signed up for university courses to learn about the detail of what went wrong. Instead, what they got was a load of nonsense dressed up as learning that bore little relation to the real world. Well, what did they expect, they chose to study economics?

Our of this was born the Rethinking Economics movement, an attempt to introduce new ideas and concepts into economic education, to adopt a pluralist approach. The problem with this, well meaning though it is, is that reforming economics is akin to Martin Luther nailing his theses to the door of All Saints' Church in Wittenberg: all it does is create a schism in a religious cult. Economics doesn't need re-thinking, it needs to be put out of its misery.

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SCOTUS Breaches the Efficient Frontier

Back to the Future

As you'll know the Psy-Fi Blog spends a lot of time pointing out to a (largely disinterested) audience of investors that there's a huge amount of psychological research out there that we can use to guide our investing behavior. In fact there are vast reams of the stuff, far too much for me to ever even summarize, let alone analyse. But as we saw in Behavioral Law and Disorder the Supreme Court, like most investors, has failed to take account of this by requiring investment professionals to benchmark themselves against the Efficient Markets Hypothesis, a failed meme if there ever was one.

Well, no more. The Supreme Court of the United States (aka SCOTUS) has donned kipper ties and white suits and boogied into the late-mid-twentieth century with a ruling that markets can no longer be regarded as entirely efficient. Somewhat surprisingly, however, the justices have based their findings not on the wealth of research that's accumulated over half a century but on an op-ed piece in the New York Times. It makes you wonder why you bother.

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Things Investors Should Hate 1/5: Models

I don’t hate all models. I’m quite keen on the tall, leggy ones.  It’s the mathematical ones that come trailing clouds of false precision that are the subject of my ire.  Yet the world is a complicated place, too complex for our complex brains, so assisting them with some expert mathematical models is entirely sensible. 

However, entirely replacing human analysis, intuition and commonsense with a computer program isn’t so much not sensible as unbelievably stupid.  People responsible for such behavior should be barred from any position of responsibility higher than a barista, and even then should be carefully watched when making anything more complicated than an Americano.  And do not, on any account, let them near a cash register.

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One Price To Rule Them All

Models. Behaving. Badly: Why Confusing Illusion With Reality Can Lead to Disaster, On Wall Street, And In Life by Emanuel Derman

"In physics there may one day be a Theory of Everything; in finance and the social sciences, you have to work hard to have a usable Theory of Anything."

The idea that the theories of physics are qualitatively different from the models of finance has been one of the longest running themes on the Psy-Fi Blog – all the way back to this post on Newton’s Financial Crisis. Now the polymathic ex-theoretical physicist, ex-quant for Goldman Sachs, Emanuel Derman, has written a book on this very subject; and very unusual it is too.

Being Derman this is no ordinary description of the problem, but one that roams widely across the realms of science, philosophy, autobiography and finance. At the heart of the discussion is a key idea, that the Law of One Price is a basis for most securities valuations and works not because it’s a deep law of nature but because it’s a simple rule of thumb that can’t be misused in any meaningful way.

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Grand Theft Auto: The Model of Modern Economics

John Kay is one of those rare economists whose views are both understandable and consistently worth listening to. He’s just published an article on the failings of modern economics, which is essential reading for anyone who is still wondering why economists don’t actually seem very useful in real life.  A few sound bites:

“It obviously cannot be inferred that policies that work in Grand Theft Auto are appropriate policies for governments and businesses.”

“Economists who assert that the only valid prescriptions in economic policy are logical deductions from complete axiomatic systems take prescriptions from doctors who often know little more about these medicines than that they appear to treat the disease.”

“There are, nevertheless, many well paid jobs for economists outside academia.  Not, any more, in industrial and commercial companies, which have mostly decided economists are of no use to them.  Business economists work in financial institutions, which principally use them to entertain their clients at lunch or advertise their banks in fillers on CNBC.”

“Economists – in government agencies as well as universities – were obsessively playing Grand Theft Auto while the world around them was falling apart.”

The full article >>  The Map is Not the Territory: An Essay on the State of Economics

Related articles: John Kay’s Obliquity, Breaking the Guild of Macroneconomists, When Muddled Modellers Model Muddles

Crash 2012: The Sunspots Are Coming …

Spurious Predictions

Next year (or, more probably, the year after) will, scientists predict, be the peak of the current sunspot cycle, when the Earth will be blasted by electromagnetic waves emanating from the Sun, disrupting communications networks, destabilising power grids and causing an almighty market crash as panicked investors head for the hills.  Although presumably they'll leave their battery powered cars at home.

Of course, if that turns out to be true I’ll claim I’m prescient and if it doesn’t, well, then it’s a deft attempt at irony. The effects of sunspots, however, are very real indeed and, for reasons we shall now analyse, the worst affected people appear to be economists.

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When Muddled Modellers Model Muddles

Hat, In, Cat, The

If a muddled modeller models a muddle do they end up with a muddle model or a model muddle?

Sadly Dr. Seuss’s most famous creation isn’t about to appear and rescue us from this conundrum. The problem is known as model risk and it’s what we often end up with when we start trying to model financial systems: a muddle rather than a model. Which helps explain how people who don't exist can get mortgages they can't pay.

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Economics & Psychology: Reconciliation?

Continued From Economics & Psychology: The Divorce …

By the early 1970’s, as the long bull market of the post war years collapsed in a welter of unforeseen problems, financial professionals confronted the real meaning of risk on a systemic basis. As markets crumbled in the face of economic uncertainty trading companies turned to economists in academia in the hope of finding a way through the mess, or at least some excuse to get people to buy stocks.

What they discovered was a way of measuring risk that appeared to offer the option of quantitatively managing investments in a rational way, rather than relying on the intuitions of individuals. This approach has come to dominate the securities industry ever since. At the same time, though, a small revolution was brewing in psychology. And it's been fermenting revolution ever since.

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Monte Carlo Simulation or Nuclear Bust

Escape from Berlin

It's 13th July 1938 and an elderly lady of Jewish extraction is boarding a train in Berlin for the Dutch border, barely escaping the grasp of the Nazi authorities. In one of the great switchback points in history the onrushing locomotive of destiny has found itself diverted down a path that will lead, circuitously, to the end of the Second World War, the triumph of the great liberal democracies and the rise of quantitative financial modelling.

Her name is Lise Meitner and now, as we leave her travelling her lonely path to exile in Sweden, she has just 10 marks in her purse. And the key to the atomic bomb in her head.

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Dying to Invest

Pain and Gain

Death, they say, is a great leveller and historically it’s the case that this has been true. Back in the early part of the twentieth century the richest man in the world, Nathan de Rothschild, couldn’t stop tuberculosis taking his life. Today, of course, fifty cents could have saved him. On the other hand Steve Job’s vast wealth has helped him survive pancreatic cancer via the best medical treatment on the planet. Maybe, in economic terms, death isn’t what it was.

No matter, the Grim Reaper will eventually take Apple's saviour from us as he will us all. Sex is designed to mix genes to help humanity in its never-ending fight against microbes seeking to destroy us: infinite longevity would come at the price of eventual human annihilation, always assuming we didn't do the job ourselves first. In economics as in science, however, we advance one death at a time and death has been one of the most important subjects for economics: no pain, no gain.

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Behavioral Law and Disorder

Mutual Fund Perfection

It’s may come as a surprise to you that the mutual fund industry, at least in the United States, is a shining record of fiduciary duty. It’s a fact that not once in twenty-five years did a shareholder in one of these funds manage to convince a court that their money has, in any way whatsoever, been mismanaged. This is despite a raft of allegations, originally stemming from the investigations of the ever-vigilant, if nocturnally over-enthusiastic, ex-Attorney General of New York, Elliot Spitzer, that many of the advisors running these funds had deliberately siphoned profits from long-term individual shareholders to institutional clients.

However, a recent judicial decision moves the goalposts – it’s no longer theoretically impossible for a private shareholder to successfully sue a mutual fund. No, now it’s actually impossible. The reason for this is grounded in an economic theory of the marketplace that is dying everywhere else: the idea that markets are perfect. It’s time for a little behavioural law and order.

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John Kay’s Obliquity

Oblique

Many of the great mistakes of history, including the problems financial markets have continualy re-experienced, have been caused by a basic error of judgement – the idea that it’s possible to define, plan and control the outcomes of the world around us despite the rampant uncertainty we daily take in our strides. So instead of relying on expert judgement and feeling our way carefully towards outcomes we’ve found ourselves traduced by people with tunnel vision and a strong but unjustified confidence in their ability to navigate unerringly to a correct solution, whatever that might be.

This is the argument presented in a new addition to the popular literature on human decision making by the economist John Kay. At the heart of this book, Obliquity, are many arguments readers here will find familiar, but perhaps the most notable is the idea that those economists who argue that people are irrational are wrong. It’s the economists who misunderstand the nature of human decision making, not their subjects,

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Basel, Faulty?

Containment, Not Cure

The international banking regulations known as the Basel II Accord have come in for some stick, given the fallout from the banking crisis of 2008. This is, on the face of it, a bit unfair given that Basel II hasn’t yet been fully implemented in most countries and anyway was designed to try to head off some of the problems that have occurred.

Still, most observers reckon that Basel II wouldn’t have prevented the crisis and the tendency of regulators, like generals, to fight the last war means that proposed changes won’t help. Whatever causes the next crisis it won’t be the same as the last one and while regulators are busily building a Maginot Line to stop one kind of problem they’re unlikely to notice that they’re also incentivising banks to invade Belgium, or at least find a way to go around the new regulations. We need a new kind of regulation, one that recognises we can’t stop the disease, but that it can be contained if we act quickly enough.

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A Sideways Look At … Economic Models

Economic Models on the Psy-Fi Blog

Almost inevitably economic models keep on appearing on this blog, an annoying but inevitable occurrence. These models underpin so much of what happens in finance that it’s impossible to ignore them. In fact they’re now so important that the models themselves can change markets, although usually only because they’ve screwed everything up again.

All models are simplifications of the real-world – a model that described everything would have to be bigger than the universe, so until we can figure out how to start consuming other dimensions we’ll have to make do with approximations. Of course, when we do break out of this cosmos we’ll end up with a bunch of do-gooders complaining about the effects of dimensional change as vast chunks of the space-time continuum break off and strand the pan-dimensional equivalent of the polar bear.

Anyway, here’s a brief summary of the Psy-Fi Blog’s thoughts on economic models …

Newton’s Financial Crisis: The Limits of Quantification

It all started nearly three centuries ago when a bewigged philosopher-scholar named Issac Newton went and invented calculus in order to model planetary motion. Soon if you weren’t using maths to do your modelling other physicists were calling you a sissy. It wasn’t long before generating mathematical models became the gold standard for everyone and that’s where the trouble started. The next thing you know we have the Efficient Markets Hypothesis:

“The Efficient Market Hypothesis is one of the great blights of modern investment analysis. What it says is that markets price efficiently, all the time. So all known information is already in the price of stocks or bonds or whatever which means that unless you know something that’s not in the public domain you can’t, on average, profit by trading. The corollary of this is that you can develop mathematical models to describe – and predict – market movements.

Charlie Munger, the octogenarian billionaire Vice-Chair of Berkshire Hathaway has a name for the Efficient Market Hypothesis. He calls it “bonkers”.” >> Read More

Markowitz’s Portfolio Theory and the Efficient Frontier

In investment circles math was, for a very long time, something done by rocket scientists. In the wake of the Second World War the climb and climb of the stockmarket led many to think that there was something inevitable about the perpetual rise of stocks. Then the seventies hit, the Arabs decided that the oil in their countries was, well, theirs and the markets went into a dizzying tailspin.

Belatedly recognising the existence of risk, and casting about for some way of managing it, fund managers happened upon some twenty year old research which equated risk with volatility and meant that everything could be boiled down to a few simple numbers. The Efficient Market Hypothesis and models that go with it were born.

“What Markowitz did was put a number on risk to allow it to be managed. The first danger for investors is in not understanding the importance of Portfolio Theory for risk management of stockmarket investments. The second is in believing that it can explain everything. People don’t get programmed linearly – we come with randomness built in, not as an optional extra. Thank goodness.” >> Read More

Of course, no one understood the downside of what rapidly turned into a quest for spurious precision.

Alpha and Beta – Beware Gift Bearing Greeks

Markowitz’s ideas led eventually to something called the Capital Asset Pricing Model (CAPM). CAPM assumes that returns from a market lie on a Bell Curve or, in the jargon, are distributed normally. Sometimes you get very bad returns, sometimes you get very good returns but mainly you get something in the middle. Only this turns out to be dead wrong.

“The markets can behave normally for long periods of time but when they go wrong it can be spectacular. Long Term Capital Management (LTCM) a hedge fund run by Nobel laureates found this out to their cost in 1998 when their normally distributed model collapsed when they were unable to sell assets at any price due to the collapse in the Russian bond market. Only concerted government intervention prevented a massive financial crisis.” >> Read More

The search for spurious mathematical precision was to lead to all sorts of problems.

Holes in Black Scholes

The LTCM Noble Laureates had made the basic assumption that the world they knew was the only world that there was to know and constructed their models accordingly. Unfortunately a human lifetime isn’t time enough to get to know even a fraction of the possibilities. What’s odd, not to say worrying, is that the inefficient Black-Scholes option pricing model that underpinned LTCM (which itself depends on the distribution of returns on a Bell Curve, aka the Gaussian distribution) is still in use today.

“Peer under the covers of Black-Scholes and you find our old friend, the Gaussian distribution, assuming that extreme events are impossible instead of just rather unlikely. The unlikely happens all the time in markets, usually because of human behavioural biases which kick in at extreme moments and lead to sustained overshoots in valuations and liquidity.” >> Read More

Of course, any hope that the lessons of the past would be learned by the financiers of the future was forlorn.

Risky Bankers Need Swiss Cheese Not VaR

Underpinning many of the risk models being used by financial institutions is something called Value at Risk (VaR) which attempts to measure the likelihood of an unlikely event under everyday conditions. Unfortunately, like many models, it’s open to abuse if the people overseeing it don’t know what they’re doing or are too distracted by large bonuses to bother. Guess what?

“To summarise a vast range of problems in simple terms, the people running the banks, the credit rating agencies and the regulatory bodies didn’t have a clue about the limitations of the risk management models they were all using. They were all looking at the same data and using the same models. And all drawing the same conclusions. Which were wrong. >> Read More

Which eventually led to the almost inevitable problems the world started reluctantly facing up to in late 2007.

Quibbles with Quants

The rise of all of these quantitative models, based on the spurious precision accompanying analogies with Isaac Newton’s models of gravitation, have resulted in continual market failures culminating in the crash of 2007-2008, which was by far the most spectacular implosion of math based financial models yet.

“The sheer nuttiness of the credit rating agencies changing their risk models purely because a quantitative model existed that indicated that the risk of these securities collapsing like dominoes in the event of isolated defaults was remote is still hard to believe. It’s not that the models didn’t indicate exactly that. … You’ve got to ask – did none of the overpaid executives running the world’s financial corporations and regulators actually stop to wonder whether someone might have, just possibly, failed to predict everything that might happen in the real world? Did none of them look at the collapse of LTCM and wonder?” >> Read More

The Death of Homo economicus

Dig hard enough into these models and you’ll uncover the idea of the perfectly rational human being, weighing up decisions in the light of perfect information. Yet the brain is, at best, an imperfect rationalising machine making all sorts of shortcuts in an increasingly desperate attempt to make sense of our information saturated world. In 1979 Kahneman and Tversky came up with a different model, behavioural finance, based on human psychology which suggest that …

“…investors – were more risk adverse when it came to protecting a profit than they were in trying to recover a loss.

So, in effect, if something went wrong with a stock they were holding the theory stated that they would be more likely to sell it if they were in profit than if they were making a loss. This is, indeed, illogical since it’s the same company with the same prospects. If investors were truly rational they would decide whether to sell or not based on the current information – stock history is irrelevant to whether a stock is currently a good investment or not. Yet the evidence suggests that this decision is, in fact, heavily biased by their personal history and, therefore, that the decision is not really a rational one.” >> Read More

Exit homo economicus, leaving a mathematical vacuum just dying to be filled.

The Special Theory of Behavioural Economics

The death of Homo economicus is required by behavioural finance, which looks at how intelligent human beings behave irrationally for the most rational of reasons. Increasingly it looks like the Efficient Markets Hypothesis is just what happens when we don’t all have some particular bee in our collective bonnets.

“A hypothesis, then, is that behavioural biases effect investors all of the time but while there’s a reasonable balance between different types of investors in the market any deviation in valuation is corrected, leading to a market that exhibits the hallmarks of a standard efficient model. However, this is only correct at the gross level – look under the covers and you’ll find a whole bunch of behavioural biases twitching away but doing so fairly randomly, cancelling each other out.” >> Read More

To Infinity and Beyond

If rational man is dead and the rational models they go with him are similarly extinct you’d hope that these adventures in stupidity are over. Unfortunately the quest for spurious precision through mathematical models that can be programmed to make institutions easy money isn’t likely to end any time soon. Whether any of these models can successfully integrate human behaviour is questionable but, on the other hand, perhaps we should hope that they don’t.

At least we can be reasonably sure that people will continue to do exactly what these models expect until they don’t. That’s the thing about people, we’re unpredictable. Which, fortunately, is still about the only predictable thing about us.

Quibbles With Quants

Models are not Reality

When we model the world we simplify it, because we have to: to model the real-world accurately we’d need to have all the atoms in the universe and a few more. So, the globe of the world revolving gently on my desk afore me, is a highly simplified representation of the planet most of us live on, leaving out what many of us would consider to be most of the important details – stuff like people, beer, football, lively teenage daughters and the latest episode of House. You fill in your own details.

Quants, the purveyors of quantitative investment analysis, also model the world, although in a much more complex, mathematical way. Yet despite the sophistication and elegance of the mathematics they too make simplifications because they have to, and these matter to us all because they’ve brought the world to the point of financial meltdown thrice within a few years.

Models are Metaphors

What quants bring to investment analysis is a set of metaphors about the way the world actually works, rather than any kind of accurate description of what’s really happening. In fact the world is run by such approximations – models of average life expectancy, mean time to failure of aerospace components, the probability of Michael Jackson’s chimp contesting his will – and, in general, these inaccuracies haven’t mattered much to humanity en-masse. However, we’re now in a world dominated by mass computing power and intricately interconnected such that small perturbations in one area can have dramatic ramifications elsewhere.

From the 1980’s, when Portfolio Insurance turned out to be no such thing, to the 1990’s, when Long Term Capital Management became a glorious oxymoron, through to the 2000’s, when the securitised mortgage market turned into a series of financial time bombs in a greater game of pass the ticking parcel, the quantitative models that mathematically illiterate managements have relied on have given no warning of what was about to occur. Yet these models were created by some of the cleverest minds in the world and supported by some of the most serious back-testing that has ever been carried out.

Yet still they failed.

People Are Uncorrelated, Until They Aren’t

What the models failed to capture was that humans don’t behave in simple, predictable and uncorrelated ways. It’s impossible to overstate the importance of the way these models cope with correlation of peoples’ psychology. To sum it up: they don’t. Let me know if that’s too complex an analysis for the mathematical masters of the universe.

Anyone who’s ever been to a nightclub, a football game or even a very loud party will know that there are situations where we don’t act as individuals, buzzing about doing our own thing. These are occasions when we all suddenly stop being individuals and start doing the same thing – usually involving large quantities of drugs and some very bad singing. Although these sorts of events are specifically designed to trigger this behaviour – which is probably a deep evolutionary adaptation to sponsor group behaviour, useful when it comes to running down tasty antelope and dealing with giant, carnivorous sabre toothed beavers – it can also happen in other situations. Most stockmarket booms and busts are generated by similar group effects.

In general, people behave in an uncorrelated fashion right up until the point they don’t. Then we all suddenly do the same thing together. We stop taking flights in the wake of 9/11, we stop letting our children play in the streets because of a single, heavily reported abduction in another country and we start selling our shares because everyone else is. Fear is an awfully big motivator.

If Something Can’t Continue, It Won’t

Quantitative models don’t handle this sudden polarisation of human behaviour very well. Every so often something surprising happens that causes us all to scurry into the nearest hole and the models promptly fall over, usually accompanied by some whizz-kid earnestly explaining that this was a one in a million year event and that it was just bad luck it happened after three years and can he please have his bonus anyway?

Think about the booms and busts in stockmarkets compared to the relatively stable periods in between. In the stable times it’s possible to roughly model the way that people are going to invest, on average, making certain assumptions about the companies comprising the markets and general economic conditions. While these assumptions hold so do the models and this can go on for a long time, long enough to convince people that stability is the norm.

Only stability is not the norm. When the markets enter one of their periodic manic-depressive phases these general models break down – people start to cluster together in fear or greed and do the same thing. Quantitative models work on the basis that the stable times will last forever, but the reality is that they don’t and when they end they do so in highly unexpected and unpredictable ways. Worse still, if any model becomes too popular it will start to influence the real world, to swing the pendulum one way. The trouble with pendulums is that eventually they swing back.

Credit Rating Madness

We can trace the collapse of the banks over the past couple of years to the excessive risks they were taking in holding Collateralised Debt Obligations on mortgage securities, the idea being that if you took a lot of very safe mortgage debt and bundled it up with a bit of really unsafe mortgage debt you’d end up with a safe investment. Whereas, in fact, you ended up with a lot of really unsafe lending to people who were never going to be able to repay their mortgages.

Yet at the time the models investment analysts were relying on simply didn’t identify these risks. Indeed the models of the credit rating agencies were explicitly changed to take into account the quantitative models showing that such securities weren’t risky.

The sheer nuttiness of the credit rating agencies changing their risk models purely because a quantitative model existed that indicated that the risk of these securities collapsing like dominoes in the event of isolated defaults was remote is still hard to believe. It’s not that the models didn’t indicate exactly that. Nor is it that the mathematics behind the models was particularly stupid. Nor was it that the analysts who dreamt up the models were doing anything obviously wrong.

No, it’s none of those things. It’s the fact that a bunch of smart people can possibly believe that any computer model can accurately reflect the real risks in a world dominated by stubborn, irrational, fearful humankind. You’ve got to ask – did none of the overpaid executives running the world’s financial corporations and regulators actually stop to wonder whether someone might have, just possibly, failed to predict everything that might happen in the real world? Did none of them look at the collapse of LTCM and wonder?

We Need Bridge Builders, Not Quants

Well, oddly enough, some of them did. Back in 2006 when the CEO of Citigroup was still dancing in the last chance sub-prime disco the risk management team at Goldman Sachs got together and solemnly inspected their VaR models which told them that risk levels were still low. Then they inspected their brains – and bailed out. Whether that was luck or judgement is still to be decided.

The great Victorian engineers built bridges that endure to this day because they couldn’t exactly model the risks. They built with a margin of safety not with a bonus on margins in mind. Remember this because the quants are not dead, they’re out there yet. They will rise again.


Related Articles: Risky Bankers Need Swiss Cheese, Not VaR, Hedge Funds Ate My Shorts, Black Swans, Tsunamis and Cardiac Arrests

Markowitz’s Portfolio Theory and the Efficient Frontier

Managing Risk

You’d have thought that the management of risk in respect of stockmarket investment would have a long and reputable history. After all, the very idea of a share is all about allowing individuals to spread their capital and risks across multiple, partial investments.

This is not so, stockmarket risk management only really started with Harry Markowitz’s seminal paper Portfolio Selection in 1952. Typically the industry then ignored his ideas for twenty years before belatedly getting around to using them for, well, everything. And then some, leading eventually to the invention of the index tracker.

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Risky Bankers Need Swiss Cheese Not VaR

Financial Risk Management is Too Risky

The failures by banking institutions across the world over the last couple of years would have been remarkable in almost any industry. However, when they’re taking place in institutions that are fundamentally all about risk management, closely overseen by phalanxes of regulators, they’re quite extraordinary. Sadly the banking industry was too focused on profits to remember the basic rule of investment.

If pharmaceutical companies or airlines suffered from the same type of risk management failures as the banks we’d all be dying of aspirin overdoses and ducking for cover as airliners crashed in our back gardens. These other industries have more nuanced models of managing risk, relying on combinations of methods. It’s about time the banks learned about the Swiss Cheese Model of Risk.

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Alpha and Beta – Beware Gift Bearing Greeks

A Land of Giants and Dwarves

Anyone involved in the stockmarket for any length of time will eventually come up against the concepts of Alpha and Beta. The terms are freely bandied about as though they can explain the mysteries of the investing universe without, unfortunately, any corresponding explanation of actually what they are.

The best way to think of Alpha and Beta is to imagine a world populated by an inordinate number of very tall and very short people. Everywhere you go you’re either tripping over them or being stood on and squished. As usual the securities industry has latched onto a useful tool and started to use it in an automated, mindless and value destroying way. Of course, the value being destroyed isn’t theirs, it’s ours.

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Newton’s Financial Crisis: the Limits of Quantification

Issac Newton's Nail

“To the man with only a hammer, every problem looks pretty much like a nail”

The blame for the crisis in today’s financial markets lies, in my opinion, with one man. Isaac Newton.

The observant among you may spot a potential flaw in this theory. The great English scientist has been dead for two hundred and eighty two years. But let’s not let a matter of such insignificant detail delay us, the genesis of the credit crunch can clearly be traced back to the man whose balls swing so freely on many a stressed executive’s desk.

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