Literally all the time. Everyone sees it. But they sure keep trying.

Quantitative models have traditionally been fashionable in representing complex processes and realities.

In economics, it's often said that any given model is true until proven wrong. In time, new models are built, and additional variables added to “fit the formula to the reality.”

Economists are absolutely obsessed with attaching numbers to things, and their peers judge the legitimacy of the model by the beauty of its mathematics, not necessarily the usefulness of its application.

That’s some cool math Bro, but does it actually work?

These models are poor predictors of market events because they can't possibly account for the nonlinear and adaptive nature of our economy and its emergent properties.

Our market operates in an open system, and because we have no benchmark in which return anomalies can be compared, we use models (written by ancient academics) with assumed variables and supporting hypotheses. Any statistical test of such a model faces a joint hypothesis problem, making it impossible to validate.

Increasing levels of global market noise, inappropriate risk assumptions, tailored statistics, and misaligned political incentives saturate the market and distract investor attention from underlying realities.

Legions of PhDs armed with supercomputers hard wired directly into the exchanges with fiber lines cannot predict market events with any consistency.

A better approach is to put the math aside for a moment to view the economy through a different lens.

Let’s recognize that our economy is an emergent system much like any other biological ecosystem.

Such complex adaptive systems don’t lend themselves to predictive linear models, but are subject to feedback loops, both positive and negative.

Positive feedback enters the system when one set of behaviors causes more of the same. Imagine investors buying because prices are increasing or selling because prices are falling. Such is the case in any boom/bust cycle (think tulips, dot.com, real estate, or fringe crypto projects).

Positive feedback creates distortions, while negative feedback is the corrective mechanism.

Short lived negative feedback loops are healthy for the system, they bring pricing back toward a more consensus ‘true value.’

The market is unpredictable, but there are some clues to lookout for when positive feedback loops enter the system.

• Large subsets of the population become suddenly wealthy on paper with little effort. Think of all the dot.com startups issuing IPOs without a product or revenue; or investors flipping homes for double what they paid 12 months prior, or how about an NFT of a rock selling for six-figures? FOMO breeds distortion; always has.

• Market participants come to a consensus. When too many investors agree on any particular model, risk measure, or pricing method, cognitive biases begin to solidify, and structural risks are either ignored or blindly justified.

• Investors become excited, terrified, or impulsive. Aggregate investor emotion fuels positive feedback loops throughout the entirety of the boom/bust cycle. Remember, prudent investing should be long term, low cost, and painfully boring. The moment an investment theme gains its own reality TV show, something ain’t right here.

Warren Buffett said it best to be “fearful when others are greedy and greedy when others are fearful.” This is often true but really only addresses those periodic (temporary) cycles when positive feedback takes hold.

During more ‘normal’ market conditions, when healthy doses of negative feedback keep prices more stabilized, an appropriately diversified, low-cost portfolio just might allow the average investor to capture (and compound) long term growth as innovative companies solve problems and create value.

But what do we know, surely this time is totally different : )