Chaos Finance

“The real trouble with this world of ours is not that it is an unreasonable world, nor even that it is a reasonable one. The commonest kind of trouble is that it is nearly reasonable, but not quite. Life is not an illogicality; yet it is a trap for logicians. It looks just a little more mathematical and regular than it is; its exactitude is obvious, but its inexactitude is hidden; its wildness lies in wait.”

― G.K. Chesterton, Orthodoxy

The big Wall Street bankers are always on the lookout for the ‘idiot’ savants that will return the impossible and read the market in ways unlike anyone else. The problem with this task isn’t that brilliant people aren’t found, on the contrary, the Wall Street reporters are constantly praising their shaman prophet gurus that unravel the future of money and the secrets of the stock market. The problem isn’t that the gurus aren’t found, the problem is that making money is a long, long game. An idiot savant one day could be just another idiot the next. The requirement in the financial game isn’t to be brilliant for a day, a week or even a decade, the requirement is to be brilliant until the day you retire. Unfortunately, our golden gurus tend to only perform for a while. When they perform, they can really perform, usually selling more books than J.K. Rowling for a given year, openly divulging to the masses how they too can outperform the masses. But, if and when their reign ends, it can end hard. The guru is forgotten and supplemented with the next guru that can keep the lie going, the lie being, that a guru can understand the market and tell its future.

The prevalent alternative impulse to accruing savants to predict and understand the market is to subscribe to the belief that markets are unpredictable, this assumption is of course false. Just ask any leading economist on the government payroll and they will assure you that we will be seeing year on year growth, without fail, “probably for the next decade”… and of course we always will be seeing year on year growth for the next decade. It doesn’t matter how good things are now, “the future will be better”. That is until it isn’t. That is until the following year is worse and the path to prosperity is not well lit. At which point the experts will tell us that their models were wrong because they didn’t factor in that one variable that they excluded and once that variable is refactored, the model will work and they can go back to their highly paid, highly technical job of assuring everyone that everything will be alright. Unlike the gurus of Wall Street, the economists are never ousted, they didn’t fail, their models did, just as the weatherman didn’t fail, the computer model failed.

The human race, to which so many of my readers belong, has been playing at children’s games from the beginning, and will probably do it till the end, which is a nuisance for the few people who grow up. And one of the games to which it is most attached is called “Keep to–morrow dark,” and which is also named (by the rustics in Shropshire, I have no doubt) “Cheat the Prophet.” The players listen very carefully and respectfully to all that the clever men have to say about what is to happen in the next generation. The players then wait until all the clever men are dead, and bury them nicely. They then go and do something else. That is all. For a race of simple tastes, however, it is great fun. – G.K. Chesterton, Prophets

Butterflies & Hurricanes

Chaos Theory is one of the most unfortunate names ever given to a theory inasmuch as the name doesn’t properly describe the field at all. Chaos, lexically speaking is synonymous with the word ‘random’, in relation to the vast field of Chaos Theory, nothing could be further from the truth. Chaos, shouldn’t imply random as if it did it could not be a field that could be studied. It wouldn’t only fall outside the realm of science that relies on causality, if there was a natural phenomenon that was truly random, then it would lead to the death of science as an endeavour to understand the world. In contrast what is meant by the term Chaos Theory, is better described as ‘near unpredictability theory’ with the unpredictable nature of whatever is being studied, not originating from break down in causality, but rather an inability of the observer to fully factor in and understand all relevant variables, with small variations in initial parameters able to create vastly different non-linear outcomes. The Butterfly Effect was the term given to this phenomenon of a system’s extreme sensitivity to initial parameters originally presented in a talk given by Lorenz (1972).

The impact of Lorenz’s 1972 talk titled “Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?”, had wide-reaching and profound impacts both in popular culture and in the scientific community. Both of which is important. From a previously growing deterministic outlook on science and the natural world, the Butterfly Theory meant that within complex systems, the ‘error’ component or the really small variables that seem inconsequential, considered ‘noise’, can have massive unpredictable, non-linear outcomes on the end result. This realisation, especially in the world of statistics, was equivalent to a KO to all sages and modern gurus who made a living off predicting the future. The term the “butterfly theory” was endowed due to the fantastical illustration which proposes that a butterfly flapping its wings could one day cause or add to a chain of events that results in a hurricane forming in another part of the planet. This event was set to be possible, or rather plausible, through a process known as positive and negative feedback loops which state that the amplitude of an event would be growing non-linearly. The implications are, that the task of determining the critical from the superfluous variables becomes practically an impossible task. Small seemingly unimportant data could have potentially huge cascading effects over time. However, despite this “discovery” of the truly complex nature of certain systems, the study into these systems didn’t stop, no climatologist hung up their hat and said that the task was too difficult. Rather the discovery leads to new fields of study and new light being shed on what was knowable and what wasn’t.

Risk & Known Risk

Value at Risk or VAR for short is a method used in the financial world for quantifying risk. Stemming from the Markowitz’s work in 1959, VAR grew to become a standard tool in the banking world in the early 80’s due to advancements in computational power. VAR “signifies the maximum amount that you can lose, statistically, as a result of market movers for a given probability over a fixed period of time … Assuming that prices follow a random walk and price changes fit a normal distribution, VAR meant that you can calculate the probability of a particular size price change” … with the result being that “Risk, the unknown unknown, was now a known unknown or even a known known.” Das (2011). This powerful tool combined an endless supply of financial instruments meant that our financial rulers could become gurus of risk and hence the true priests of Wallstreet were born and the great financialisation of the west commenced giving birth to the God of this age, the God of science, probability and maths that could be continually appeased by the genius mathematicians, econometricians and financial analysts. That was the God of endless wealth through financial manipulation. All of which was hinging on the likelihood of a normal distribution of price changes. This gave birth to the golden age of the Great Financialisation.

The VaR models worked when they worked meaning that as long as the predictions held true, and the god of finance was appeased, the priests were employed and grew in power and prominence, as John Kenneth Galbraith noted “The specious association of money with intelligence”. The models worked when they worked until they simply didn’t. 2007 drew blows to the VaR modeling with multiple standard deviation changes in the period of days that some economists theorised would occur “only every 73 to 603 trillion billion years”, an event so unlikely that it takes many universes in time to ever occur again given our own universe is predicted to be roughly 20 billion years old, David Einhorn debated in 2008 that VaR worked as “an airbag that works all the time, except when you have a car accident”. The other alternative being that our probability models were wrong and the ‘impossible event’ wasn’t only impossible to happen again in our lifetimes but rather, probable. Why? Because financial markets, like all complex systems, are subject to chaos, no one could include all relevant variables in their modelling much less distinguish between important and unimportant variables.

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

It doesn’t matter how many times markets, stocks, economics, housing prices have fluctuated within standard deviations in the past, it doesn’t mean that they will continue to act within those parameters in the future.

“no amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion” – David Hume

Just as a butterfly can cause a hurricane, a shout can cause an avalanche, so the unknown unknowns can plague financial outcomes in ways completely unpredictable and possibly devastating ways.

If A Butterfly Could Cause A Hurricane, What Could A Tsunami Do?

Progress made in understanding the field of Chaos Theory demonstrates how small variables can have massive impacts on complex systems over time. This couldn’t be more clearly demonstrated than in the US 2007 sub-prime mortgage housing crisis. Seemingly small decisions by politicians to deregulate the market so to promote sub-prime lending, decisions by banking and lending boards to promote and sell junk loans, decisions by investment banks to obfuscate risk in repackaged loans, decisions by ratings agencies to overlook and not properly assess the risk in these securities, decisions by investors to purchase deliberately complex securities, and above all else, decisions by mortgage holders to purchase these loans all added to a positive feedback loop which amplified the impact of each subsequent decision that ultimately lead to a world wide financial crisis. A financial crisis so severe that it could have potentially lead to the complete collapse of the worldwide financial system and the end of the US economy. All of which was caused by small moral and intellectual failings by many parties across the economic landscape from the poor borrowers “the butterflies”, to the financial, political and regulatory elite.

The problem wasn’t that one person defaulted on a loan that they should have never had been dealt, it’s that a positive correlation of defaults occurred between these subprime mortgages that lead to the collapse of… almost everything. The reasons why the loans weren’t meant to create a systematic risk was because they were relatively small when compared to the overall economy and defaults were uncorrelated. There wasn’t much reason to think that someone defaulting on their loan in California would correlate with people defaulting on their loans in Texas.

Once again the what occurred in the financial world is best described by something that occurs in the natural world.

The Ising Model

While interest rates were low, repayments could be met and the defaults remained low. The problem of course with a fed created bubble of cheap money is that as soon as the Fed stops blowing, the bubble starts going down. Ultimately the GFC was created through poor performing loans made possible through the Feds expansionary monetary policy introduced in response to the Dotcom bubble. In order to avoid the painful corrections that were due because of poor speculative tech investment of the 1990s, the Fed flooded the market with cheap cash, combined with weakening regulation on mortgage-backed securities 2001 to 2007 lead to a glut of non-performing loans. The breakdown of which lead once again to the fed flooding the market with even cheaper cash.

While the economy was hot and the Fed kept up the supply of cheap cash, default correlation was nonexistent. That was until the Fed started to up interest rates from the low levels of the post Dotcom bubble recovery. The Ising Model portrays a simplified model of a thermodynamic phase transition, put simply, while temperatures are high, magnetic moments are uncorrelated but once temperature drops to a critical point, the magnetic moments spontaneously become correlated. While the economy was hot, money flowed throughout the economy, the debt chain worked, and mortgage defaults were uncorrelated. Due to the massive weight of interest only loan repayments on variable interest rates, on an overleveraged lower class, once the cost of maintaining debt climbed to a critical point, the default correlation coefficient shot up and created a default positive feedback loop which simultaneously triggered a negative feedback loop on housing prices.

The problems experienced in the 2007-2008 GFC were created by millions of butterflies in the form of bad debtors, suddenly flapping their wings in unison as economic conditions changed.

The Tsunami

To tackle the unravelling economy, governments around the world started pumping more and more money back into economies, lowering interest rates to virtually zero, encouraging lending, borrowing and debt. In effect, governments around the world desperately tried to fix the debt glut of the early 2000’s, with more debt. All of which resulted in skyrocketing public debt, alongside huge private and corporate debt. The cards that were stacked against the world’s economy in 2007 haven’t been removed and the stack is now higher than ever.

As of 2016 there were over $1 trillion euros in non performing loans. US public debt was tipping over $20 trillion and the emerging markets weren’t looking any better. With central banks around the globe now working to dial back their expansionary monetary policy and lower their check books, the question arises, with this much debt, will we experience a similar effect to what happened the last time the US Fed raised interest rates?

The percentage of the US tax receipts that will go into servicing their public debt alone will create excess stress on an already underperforming economy not to mention the increased pressure it places on private borrowers.

The Three Body Problem

Economies are rational, even when they’re described as “irrational” frenzies, all economies are in fact rational and ruled by the underlying goal of self-interest. However, the effects of having such large masses in the form of cheap money printed in mass to prop up struggling markets, along with never before seen levels of public debt, mean that the unconventional situation creates unconventional economies.

The Three Body Problem is an example of a problem that has no algorithmic solution. Using the analogy of planetary motion, imagine we had a solar system including 3 planetary bodies, in this instance it could be 3 stars in relatively close proximity to each other, if you had the place, direction, speed and the knowledge of how each mass’s gravity is influencing the other’s movement, there would still be no predictive algorithm that could map out exactly where the celestial bodies will be in the future. The movement of the planets is subject to a chaotic system, with the only way of knowing where they will be in the future, being through brute calculative force offered by computers.

Just as a third star’s gravity will turn a basic predictable solar movement into a chaotic and complex system, so the gravity created by effectively zero and even negative interest rates combined with enormous government debt, create the gravity needed to completely throw off and predictable nature of where the economy is heading. And while our economic solar system has become accustomed to these extra gravitational pulls, there is no way of knowing what trajectories will be as governments and central banks around the world try to reverse the situation.

More than that, the complexity incorporated in an economy contains far more than 3 important determinant variables. The number of important variables is finite, the future is predictable given perfect knowledge of what variables are important and how, but the reality is there is no way of knowing this. Just as the complexity of a gravitational system explodes exponentially going from 2 celestial bodies to 3, so the future of economic growth and stability have been thrown into the unknown to a much greater level than what was offered to it through the unknown unknowns that always plague our society, and what we have now is a future economy where many of the knowns are now unknowns in an unusually complex system in which the gurus have no better idea of what is going to happen than the tarot card reader at your local hippie fate.

But of course, don’t take my word for it, below is a relatively short discussion at the World Economic Forum from January 2018 highlighting the issues with funny money and unpredictability.