On December 17, 2014, then-President Barack Obama announced that the United States would restore its international relations with Cuba. In addition to many expected diplomatic consequences, the decision had an odd effect: boosting the popularity of a small, closed-end fund that trades as CUBA.
Despite its name, CUBA doesn’t invest in any stocks of any value in Cuba.* There was no rational financial explanation for why investors would buy up this fund—which nearly doubled in price—on this particular day.
The investments in CUBA are a reminder that the market isn’t always “efficient”: Investors don’t always make rational decisions and go with whatever gives them the greatest risk-adjusted return. Yet stock prices often are relatively predictable based on rational, mathematical models. CUBAs in the stock market don’t happen all the time, nor do unexpected dips and crashes.
In a complex system like the stock market, rationality and irrationality coexist. Determining what strategies make the most sense depends on the constantly changing conditions, says Andrew Lo, a professor at MIT’s business school.
To better understand complex financial markets, a growing number of economists are looking beyond math and physics, the roots of the field’s historic models, to what might seem an unrelated discipline: evolutionary biology. Much like a biological organism living in an ecosystem, the stock market is a network. As cells do within a human body, or as bacteria do in their colony, investors and companies interact with, influence, and compete with each other—and they need to adapt for survival.
Proponents of this so-called adaptive-markets approach, sometimes known as “evolutionary economics,” believe it has big implications for investment strategies, from how to make financial systems more stable to understanding innovation, growth, and inequality.
The theory reconciles behavioral economics—which examines psychological factors in economic decision-making—with traditional efficient-markets economics by taking into account that things change over time, says Lo, who has written a book called Adaptive Markets. Neither alone is the complete story, he says.
One of the hallmarks of networks is that properties emerge from them that you can’t predict from a one-on-one relationship, says Laura Reed, a biologist at the University of Alabama in Tuscaloosa who doesn’t study economics. These emergent properties can be beneficial, such as when some innovative solution to a problem is generated from crowdsourcing. But they can also spiral the other direction, like a virus that jumps species to infect humans.
Think of investor panic leading to a run on a bank, or more commonly these days, the stock market. The panic incited by a plummeting Dow Jones average is driven because the behaviors of other players in the system are intertwined, namely that each can see that others are selling in the market and react to it. Even though crashes occur regularly, the spark that sets them off catches most financial wizards off-guard each time precisely because the crashes are properties that emerge from this complex system.
Predicting instability in a complex system is difficult, and therefore probably the biggest limitation of the evolutionary-economics approach. But to improve the understanding of what the outcomes will be, you need to understand the structure of the system, says Reed, who studies the biological system of how fruit flies react to poor diets and a lack of exercise. “If you want the complete story, you have to think about it as a whole,” she says.
In evolutionary thinking, understanding the history is important, too, she says. Appreciating the pressures that shape the organism into the way it is could help predict what will happen in the future. Like a virus that morphs to become resistant to an antibiotic, long-term survival—financial payoff, in the case of investing—may require adapting to the current or future environment rather than using one static strategy.
As Reed notes, evolution is less about survival of the fittest than it is about survival of the “good enough”: An organism may survive one crisis because it has enough of the right characteristics to do this, but it still may be at risk in the future. This makes it all the more important to understand the entire network to figure out who is best set up to succeed under different types of duress, or to influence systemic forces to better serve the individuals in the network. Understanding the network also can help identify critical vulnerabilities, the pieces of the system that, if perturbed, can lead to collapse of the whole.
Historically, economists have been drawn to the idea of equilibrium that physics offered, which allowed tidy algorithms of supply and demand, says Eric Beinhocker, the executive director of the Institute for New Economic Thinking, or INET, at the University of Oxford. But the stock market isn’t about achieving and maintaining balance; it’s really about disequilibrium, more like a ship lurching from one crisis to another, trying to right itself.
Real-world economic crises are great laboratories for learning, Beinhocker says. Lehman Brothers, an investment bank that collapsed spectacularly and suddenly in 2008, was hugely connected to others in the industry, and its failure set off a chain reaction. There’s a biological concept at play here known as pleiotropy, which means that one particular aspect of a system has multiple functions. It can’t be fiddled with in isolation; it will cause ripple effects.
Beinhocker and his group are working on a new generation of models to map out the conditions and interactions involving real human behavior to show when markets might crash and contagion might spread. Their goal is to help develop policies that can restructure those networks so one player isn’t so dependent on another that the system spirals out of control. “The problem is not so much too big to fail, but too connected to fail,” Beinhocker says.
One study spearheaded by Beinhocker’s colleague at INET, J. Doyne Farmer, looked at the 2008 financial meltdown to model what would happen if banks today followed something like the regulations that existed at that time. They found that over time, under rules that controlled the amount of capital versus debt banks could hold, a “boom and bust” cycle always emerged. Attempts to actively manage how much capital companies could borrow depending on how volatile or stable the market is didn’t help. But when companies were required to maintain a particular amount of borrowed capital regardless of circumstance, the market didn’t exhibit such dramatic swings. This suggests that a policy change, while perhaps not erasing the risk of any crash, could minimize it.
At this big-picture level, adaptive-markets ideas can help policy makers improve regulatory laws to stabilize the system. By modeling the system as a system, one can see what properties emerge—or fail to emerge, such as feedback warning a system that bad things are happening—and try to implement the missing piece in order to reduce the volatility of the market cycles, says MIT’s Lo.
On an individual level, a more accurate model of financial-market dynamics would allow workers to develop more effective retirement plans, Lo says. Or, taken to an extreme, he notes, the adaptive-markets theory allows one to imagine “roboadvisors,” or software that could customize and manage portfolios to achieve people’s short- and long-term financial goals, or alert us when some of those goals simply aren’t achievable given the current circumstances.
Think of different funds and investments as new species on a never-before-explored island, says Lo. Who adapts better or worse to that environment? And what’s the goal for approaching the ecosystem? “If I’m trying to protect the new island and keep the ecology relatively stable, what kinds of policies or interventions do I consider to maintain stability?” he says. “If the goal is to enhance growth and innovations, there are specific policies one wants to undertake.”
* This article previously stated that CUBA invests in no stocks related to Cuba. We regret the error.
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