Tulips, Bitcoin, and Complex Adaptive Systems

by | Jan 4, 2021


As I read this morning how Bitcoin returned over 300% in 2020 and is now up to $34,000 when it traded for about $10,000 in October, I felt a desire to jump on the Bitcoin train again (having exited it when it hit $20,000). Is Bitcoin worth $34,000? Who knows – it has no reference value. It only has worth because others think it does.

Bitcoin’s remarkable rise in value reminded me of the Dutch Tulip Craze of the 1600s. As you may be aware, I’m writing a book on investment mental models and in one of the chapters I discuss the Dutch Tulip Craze and Black Monday in 1987 and how these two events can inform how we view markets. Here’s the relevant excerpt from the chapter:

Complex Adaptive Systems Can Generate Extreme Results

When humans interact with each other in social systems, extreme outcomes occur that defy prediction. A prime example of this is the “Dutch Tulip Craze” of the 1600s.

The tulip was introduced to Europe in the sixteenth century from the Ottoman Empire. They became wildly popular in the Netherlands and were considered a luxury item and a status symbol. By the 1620s, many types of tulips were highly prized and shot up in value. A single tulip bulb could cost 1,000 Dutch florins (the average laborer at the time made 150 florins/year). By the 1630s, the price of tulip bulbs had reached even more extraordinary levels and continued to increase rapidly.

When humans interact with each other in social systems,
extreme outcomes occur that defy prediction.

Tulip bulbs traded on stock exchanges, and people accepted bulbs in exchange for livestock and land. Common people without financial sophistication speculated in tulips as their prices skyrocketed. Enterprising financiers invented futures contracts for tulips and they were among the first derivatives to trade on an exchange. By late 1636, a single tulip bulb sold for as much as 15,000 florins. Tulip-mania is an example of the “greater fool theory.” Tulip prices didn’t skyrocket because of the tulips’ intrinsic value. Instead, the high prices reflected that everyone thought everyone else would be silly enough to buy them for an even higher price in the future.

The bubble burst in February 1637, as tulip traders could no longer realize inflated prices for their bulbs and began to sell to lock in their gains. People began to suspect that the demand for tulips could not last, and as this notion spread, a panic developed. Some were left holding contracts to purchase tulips at prices ten times greater than those on the open market, while others found themselves in possession of bulbs now worth a fraction of the price they had paid. Prices crashed, and thousands suffered financial ruin.

Tulip-mania is an excellent example of a complex adaptive system in action. People in the Netherlands watched each other watching each other buy tulips. The rising price of tulips was a positive feedback loop that caused more people to want to buy tulips, which further increased the cost. The extreme result of some tulip bulbs being worth more than a lifetime of a laborer‘s wages occurred due to people’s complex interaction and the feedback loops created by those interactions.

Another example closer in time occurred on October 19, 1987. Known as Black Monday, the Dow Jones Industrial Average plunged nearly 23 percent on that single day. Before Black Monday, such a massive drop in the market wasn’t considered possible because statistics put such a decline at an impossibly rare twenty-two standard deviation event. Even during the stock market crash of the 1920s, the largest single-day decline was 13 percent, a large drop but way shy of 23 percent. Thus, Black Monday was unthinkable before it happened.

What caused the drop? While there’s no one clear answer and economists and investment strategists still debate the underpinnings of Black Monday, one widely acknowledged cause is the popularity of a derivative strategy known as portfolio insurance. The scheme involved using options to hedge a portfolio to enjoy gains when stocks went up but would limit losses when stocks went down. It was dynamic trade, meaning that portfolio managers adjusted the hedges as the market gained or lost. In the years leading up to Black Monday, the use of portfolio insurance spread among Wall Street firms that used the strategy on tens of billions of dollars of investments.

On an individual basis, it made sense to implement portfolio insurance. Who wouldn‘t want the ability to enjoy stock market gains while also limiting losses? However, on a system-wide basis, having tens of billions of dollars using the same strategy was disastrous.

As market volatility increased in the days leading up to Black Monday, the portfolio insurance strategy led to investment managers simultaneously selling to raise money to increase their hedges. This selling generated losses, which caused the portfolio insurance algorithms to require the sale of even more assets to place more hedges. This feedback loop of losses that caused more selling, creating more losses and then more selling, and so on, was a vital component of the 1987 crash.

In his book, A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation, Richard Bookstaber—head of risk management at Morgan Stanley in 1987—analogized it as follows: “If one small portfolio uses this sort of strategy, liquidity will not be an issue. If everyone in the market is trying to do it, it can become a nightmare, a little like everyone on a cruise ship trying to pile into a single lifeboat: it won‘t float.”

What hoarding toilet paper, Dutch tulip-mania, and the 1987 stock market crash teach us is that the complex system of human interaction can create feedback loops that result in extreme outcomes. Who would have predicted at the beginning of 2020 that if a global pandemic hit that the thing Americans would hoard would be toilet paper? Likewise, the atmospheric rise of tulip prices was unpredictable before it happened, as was a 23 percent stock market drop happening during a single day.

. . . the complex system of human interaction can create
feedback loops that result in extreme outcomes.

Why It’s Important to Understand That the Market Is a Complex Adaptive System

The fact that the stock market operates as a complex adaptive system is an essential concept to have in our toolbox of mental models. It means that many effects we see in the markets have no readily discernible cause. There’s simply too much complexity among the various agents that interact and generating feedback loops. As explained by author and investment strategist Michael Mauboussin in his excellent book More Than You Know: Finding Financial Wisdom in Unconventional Places, “Investors who insist on understanding the causes for the market’s moves risk focusing on faulty causality or inappropriately anchoring on false explanations. Many of the big moves in the market are not easy to explain.” We‘ll look at causation in more detail in chapter four.

Here’s my Forbes article about the stock market being a complex adaptive system: Why The Stock Market Doesn’t Make Any Sense


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