The Precision Problem: Actually, Economists Can Predict Financial Crises

We often appear captive to the illusion that more information means more certainty. We expect our yearnings to be converted into predictions and those predictions to rendered with ever more precision.

And we are, of course, deluding ourselves. The presence of data does not necessarily cause the absence of ignorance as all too many financial speculators have learned, sometimes repeatedly, over the past five years.

The biggest issue, as the following article explains, is that our demand for ever greater certainty about what will happen interferes with our understanding of what might happen.

The phrase 'the perfect is the enemy of the good,' variously ascribed to von Clausewitz, Voltaire and Joseph Stalin, captures this rather, well, precisely. That so many notable thinkers and doers of diverse interests and attitude are credited with saying it reinforces the universality of this truth.

The fact is that applying intellectual constructs from other disciplines may provide greater understanding is not new. In one notable example we are finding, particularly of late, that our efforts to hire only the most specifically qualified applicants is causing enterprises to lose both needed contributors - and the benefits of potential serendipity.

As we seek to gobble up ever greater amounts of data in hopes of converting it into knowledge, it is worth remembering that what we dont know will always exceed what we do - and that we can use that lack of precision to our advantage. JL

Mark Buchanan comments in Bloomberg:

Predictions can be useful without being precise. Scientists make valuable predictions all the time that have little to do with foreseeing the future, but develop our understanding of cause and effect
Economists have long argued that they shouldn’t be expected to predict crises, such as the one that almost sank the global economy five years ago.
That depends on how you define the word “predict.”
In recent years, an inconsistency has emerged in the economics profession. Many, including some Nobel Prize winners, maintain that crises are by their very nature unpredictable. At the same time, others -- aided by engineers, physicists, ecologists and computer scientists -- are developing ways to detect and quantify systemic risks, including measures that regulators could use to identify imbalances or vulnerabilities that might result in a crisis. If prediction is impossible, what’s the point of all the activity? After all, there’s an obvious logic to how forecasts can subvert themselves. If, for example, someone could have convinced investors in 2005 that a financial crisis was coming in two years, markets would have reacted immediately, setting off the spiral of deleveraging and financial contraction that in reality came later.
A biologist might predict that knocking out a gene in a mouse will lead to obesity. If experiments bear that out, then we learn something about the genetic control of metabolism, and might get some hints about ways to help combat obesity in people.
On a more mundane level, we know by experience, as well as from engineering theory, that failing to keep oil in a car’s engine will inevitably lead to trouble. No one can say precisely when or where, or even what will go wrong. That doesn’t prevent us from recognizing the wisdom of regular auto maintenance.
In this sense of a causal relationship, quite a few people did foretell the financial crisis. Consider the 1999 book “Debt and Delusion,” in which financier Peter Warburton warned that the decades-long orgy of rising corporate and individual debt would inevitably fuel asset bubbles and cause financial instability. This was another “if X, then Y” prediction: If we continue along current lines, then trouble will follow. Many others made similar predictions, to which very few paid attention.
The challenge for economists is to find those indicators that can provide regulators with reliable early warnings of trouble. It’s a complicated task. Can we construct measures of asset bubbles, or devise ways to identify “too big to fail” or “too interconnected to fail” institutions? Can we identify the architectural features of financial networks that make them prone to cascades of distress? Can we strike the right balance between the transparency needed to make risks evident, and the privacy required for markets to function?
Work is racing ahead. In the U.S., the newly formed Office of Financial Research has published various papers on topics such as stress tests and data gaps -- including one that reviews a list of some 31 proposed systemic-risk measures. The economists John Geanakoplos and Lasse Pedersen have offered specific proposals on measuring the extent to which markets are driven by leverage, which tends to make the whole system more fragile.

‘Physics Envy’

One problem has been “physics envy” -- a longing for certainty and for beautiful, timeless equations that can wrap up economic reality in some final way. Economics is actually more like biology, with perpetual change and evolution at its core. This means we’ll have to go on discovering new ways to identify useful clues about emerging problems as finance changes and investors jump into new products and strategies. Perpetual adaptation is part of living in a complex world.
The efforts to develop measures of systemic risk are encouraging. They suggest that the idea that “markets are always right,” which inspired much of the financial deregulation of the 1990s and early 2000s, has lost some of its credence. Increasingly, mainstream economists accept that markets routinely get out of balance, and that they require continual oversight.
Now all they need to do is get a lot better at prediction before the stage is set for the next crisis.