Complex systems are all around us.
By one definition, a complex system is any system that features a large number of interacting components (agents, processes, etc.) whose aggregate activity is nonlinear (not derivable from the summations of the activity of individual components) and typically exhibits hierarchical self-organization under selective pressures.
In today’s infographic from Meraglim we use accumulating snow and an impending avalanche as an example of a complex system – but really, such systems can be found everywhere. Weather is another complex system, and ebb and flow of populations is another example.
At the 2021 SALT New York conference, which was held earlier this week, one of the panels on the main stage discussed the best macro shifts coming out of the pandemic and investing in value amid distress. The panel featured: Todd Lemkin, the chief investment officer of Canyon Partners; Peter Wallach, the managing director and Read More
Markets are Complex Systems
Just like in the avalanche example, where various factors at the top of a mountain (accumulating volumes of snow, weather, temperature, geology, gravity, etc.) make up a complex system that is difficult to predict, markets are similarly complex.
In fact, markets meet all the properties of complex systems, as outlined by scientists:
System actors have different points of view. (i.e. bullish, bearish, long, short, leveraged, non-leveraged, etc.)
Capital markets are over-connected, and information spreads fast. (i.e. chat rooms, phone calls, emails, Thomson Reuters, Dow Jones, Bloomberg, trading systems, order entry systems, etc.)
Trillions of dollars of securities are exchanged in transactions every day (i.e. stocks, bonds, currencies, derivatives, etc.)
4. Adaptive Behavior
Actors change their behavior based on the signals they are getting (i.e. making or losing money, etc.)
And like the avalanche example, where a single snowflake can trigger a much bigger event, there are increasing signs that the complexity behind the stock market has also reached a critical state.
Markets in a Critical State
Here are just some examples that show how the market has entered into an increasingly critical state:
The VIX, an index that aims to measure the volatility of the market, hit all-time lows this summer.
Bull Market Length
Meanwhile, the current bull market (2009-present) is the second-longest bull market in modern history at 3,109 days. The only bull market that was longer went from the 1987 crash to the Dot-com bust.
Valuations at Highs
Stock valuations, based on Robert Schiller’s CAPE ratio (which looks at cyclically-adjusted price-to-earnings), are approaching all-time highs as well. Right now, it sits 83.3% higher than the historical mean of 16.8. It was only higher in 1929 and 2000, right before big crashes occurred.
Market Goes Up
Investor overconfidence leads investors to believe the market only goes up, and never goes down. Indeed, in this bull market, markets have gone up 67 of the months (an average gain of 3.3%), and have gone down only 34 months (average drop of -2.6%).
Here are some additional signs of systemic risk that make complex markets less stable:
- A densely connected network of bank obligations and liabilities
- Over $70 trillion in debt added since Financial Crisis
- Over $1 quadrillion in notional value of derivatives
- Non-bank shadow finance through hedge funds and securitization make risk impossible to measure
- Increased leverage of banks in some markets
- Greater concentration of financial assets in fewer companies
In other words, there are legitimate reasons to be concerned about “snow” accumulation – and any such “snowflake” could trigger the avalanche.
In complex dynamic systems that reach the critical state, the most catastrophic event that can occur is an exponential function of scale. This means that if you double the system, you do not double the risk; you increase it by a factor of five or 10
– Jim Rickards, author of Road to Ruin
The Next Snowflake
What could trigger the next avalanche? It could be anything, including the failure of a major bank, a natural disaster, war, a cyber-financial attack, or any other significant event.
Such “snowflakes” come around every few years:
1987: Black Monday
The Dow fell 508 points (-22.6%) in one day.
1994-95: The Mexican peso crisis
Systemic collapse narrowly avoided when the U.S. government bailed out Mexico using the controversial $20 billion “Exchange Stabilization Fund”.
1997: Asian financial crisis
East Asian currencies fell in value by as much as -38%, and international stocks by as much as -60%.
1998: Long Term Capital Management
Hedge fund LTCM was in extreme distress, and within hours of shutting down every market in the world.
2000: The Dotcom crash
Nasdaq fell -78% in 30 months after early Dotcom companies crashed and burned.
2008: Lehman Brothers bankruptcy
Morgan Stanley, Goldman Sachs, Bank of America, and J.P. Morgan were days away from same fate until government stepped in.
Shelter from the Avalanche
The Fed and mainstream economists use equilibrium theory, regressions, and correlations to quantify the markets. And while they pay lip-service to black swans, they don’t have a good way of forecasting them or predicting them.
Markets are complex – and only complexity theory and predictive analytics can help to shed light on their next move.
Alternatively, investors can seek shelter from the storm by investing in assets that cannot be digitally frozen (bank accounts, brokerage accounts, etc.) or have their value inflated away (cash, fixed-income). Such assets include land, precious metals, fine art, and private equity.
Article by Jeff Desjardins, Visual Capitalist