Why Do Financial Models Break?

Lauren Reiff
Dec 30, 2019 · 7 min read

For years, economics has had to fight against being defined by scientific dogma and has had to put up with being distilled down to mathematical equations. That such efforts have been attempted is, on the surface, not surprising per se, for so much of our world has been helpfully explained by the elegant laws of physics.

Yet economics defies these attempts to imprint precision at many a frustrating turn. It might conform for a bit, play along nicely for a little while, but before long, things start to twist and bend.

Those employed in high finance drive themselves mad with these discrepancies — the ways in which their models continually trot off the main path, forking off in another direction. The way it was supposed to be, the more math that structured the financial system, the more cleanly and predictably it was supposed to act. As the thinking went, the more we clambered after efficiency and implemented computer trading, the more smart and stable the system would be, right?

The logic sounds reasonable enough but in reality, nothing the finance industry concocts is unbreakable or entirely predictable, which is a continual frustration for the algebraic-addled minds that populate high finance. Many models that are employed at some point become brittle and thus, snap and cease to function.

It’s a confounding problem but one that few in the industry really take the time to consider (mostly because everyone has an incentive to keep the show running at all costs). But because economics is not some esoteric discipline but instead, is the very beating heart of human society, this is an issue that frankly, should concern us all.

The EMH has gone awry

Since quantitative finance blossomed in the 1980s, building math directly into the markets became increasingly de rigueur. Statistics swarmed the field as a new crop of brainiacs entered finance ready to unleash their wizardry. This was during a time when many people had become infatuated with the Efficient Market Hypothesis, the intellectual creation of the famous Eugene Fama, which posited that the markets were always rational.

The implications were that not only would it be impossible to “beat the market” but also that the market itself was to be seen, at all times, as an untouchably accurate representation of real value. But did this hold true? Was it a correct assumption to make? Was the market actually rational?

After all, one didn’t have to think for too long how this theory easily tripped upon itself. The EMH depicts an environment of information symmetry, of mechanistic thought-processes, of human beings cooly focused on profit maximization and stripped of their human irrationalities.

That this is the dominant theory that has undergirded many of the models created in the ensuing years is staunchly important. Because, baked into the models was the assumption that the current value of, say, a stock price, was correct — that it could be nothing but correct. Following this logic, however, asset bubbles would be obsolete. And that certainly wasn’t true.

But here’s the thing: Regardless of how much trading is dumped into the waiting, technologically able hands of computer algorithms, finance is inseparable from people, for whom it serves and provides (usually!) necessary functions.

As such, financial bubbles are created by people. Just as financial models are created by people. And it is people who ultimately decide to use them.

Now, financial models are alluring because they appear to remove the messy fluctuations of human behavior. They have a deceptively faultless sheen to them, appearing to be emotionless entities. They are like beautiful pieces of architecture that their creators look on in awe. But do they stay this way? In a lot of cases, no.

Usually, something goes wrong and the model is blamed. Perhaps the verdict is that something needs to be tweaked or that something was otherwise incorrect to begin with. Interestingly, when crises occur, there is mass head-scratching and panic over the numerical intricacies, but maybe it is the supremacy of models themselves that lies at the heart of a larger issue of a potential systemic threat to the financial system as a whole.

The Four Flaws

There are several shortcomings of financial models that reveal their hazardous inflexibility — their clumsy handling of what you might call ‘the human question’.

  1. Models are not perfect representations of reality. Models are abstract simulations braced by certain chosen parameters. They are probabilistically-close approximations of reality, but trouble ensues when too many people put their faith in those models bearing the gold-at-the-end-of-the-rainbow Ultimate Truth.
  2. A flood of computer scientists, mathematicians, and physicists into the field of high finance have helped embed, to an even greater extent, an overarching idea that people behave in an atomistic manner and that their economic choices are independent and contained, so to speak. This stands in contrast to a more accurate assessment of there being inherent complexity and interrelatedness above all else.
  3. Human beings cannot be depended upon to behave rationally to the degree that their behavior can be sufficiently squared away in an equation. Again, despite the huge volume of technological trading (and those algorithms’ obsession with predicting the behavior of other models), human behavior can never be eradicated from the financial arena.
  4. Perhaps the biggest danger of all is this: In this environment overrun by models, the math becomes the markets, rather than the actual actions of traders themselves. As in, much of the activity taking place is a blindingly fast-paced slew of predictions of what the rest of the market will choose to do.

The Deductive & Predictive Fall Flat

Personally, I’d come back to this fundamental realization: The foremost responsibility of free markets is authentic price discovery. The predictive quality that defines so many of the financial algorithms that populate the financial arena today is culpable for distorting this basic function that our collective economic livelihood depends upon.

It all stems from a profit imperative, naturally enough, as well as an equally strong self-soothing belief that the markets can be tamed by math, governed by immutable laws, and elaborately forecasted (so as to be profited from).

This grandiosely deterministic philosophy is actually really important to note. It’s the essential idea that underpins all the faith in models and that has come to characterize our current financial system. Everyone wants to believe that financial markets can be defined by the pleasantly neat and tidy criterion of mathematics instead of being something completely beyond the scope of humans to govern and control in a top-down manner.

People want to believe that the markets can be pinned down and predicted — it’s like playing God, in a way. In reality, the markets are an ineffably complex project of collective proportions. In reality, financial markets are probably better off left to in-the-moment negotiation between a marketplace of buyers and sellers.

Finance can never be bludgeoned into being a hard science because it’s incapable of abiding by the mechanistic laws that the physical sciences are built upon. The capriciousness of the human influence contained therein, as well as the inherent interrelatedness/complexity that makes conceiving of the markets as the mere sum of ‘x amount of agents’ impossible, both permanently prevent the field from ever behaving in a way that those in high finance dearly wish it would.

In any case, the really concerning the reality of models is that they cannot escape the influence of the human hand. People are still the ones that create them and the ones that choose them.

The price of playing by the same rules

And financial models have their own popularity contests and standards of fashionability. In most cases, financial players will converge to play by the same models. After all, this helps their odds of predicting what the rest of the market will do which, of course, carries with it the tantalizing possibility of bigger returns. Cooperation and homogeneity actually win in this case. But it’s a kind of cooperation that can be fatal.

This bandwagon effect results in everyone piling onto the same models. When things go awry (as they will, because no model is a perfect representation of reality), the result is not a run-of-the-will liquidation of a trade, for example. If everyone is mimicking the steps of everyone else, then naturally the edifice crumbles like a row of dominos. This is how the risk potential for modern financial crises has amplified. In any case, when this occurs, the models can cease to function.

In the end, it is human behavior that topples the whole game. A game that was supposed to be impervious to anything but elegant mathematics.

There is something profoundly humbling and sobering in these kinds of analyses. We build things like financial models that sparkle and shine but in the end, they sometimes result in crudely exposing our own wrong-headed confidence in our ability to play God with a system that no one of us can ever hope to vanquish by abstract means.

Data Driven Investor

from confusion to clarity, not insanity

Lauren Reiff

Written by

writer of economics, psychology, and lots in between. forever in love with words — laurennreiff@gmail.com

Data Driven Investor

from confusion to clarity, not insanity

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