Andrew Lo is a professor of finance and the director of the Laboratory for Financial Engineering at MIT’s Sloan School of Management.
His research spans a wide range of topics, including the empirical validation and implementation of financial asset pricing models; the pricing of options and other derivative securities; financial engineering and risk management; trading technologies and market microstructure; computer algorithms and numerical methods; financial visualization; statistics, econometrics and stochastic processes; nonlinear models of stock and bond returns; and hedge fund risk-and-return dynamics and risk transparency.
Lo is a co-author of “The Econometrics of Financial Markets,” “A Non-Random Walk Down Wall Street,” “The Heretics of Finance,” “The Evolution of Technical Analysis,” and the author of “Hedge Funds: An Analytic Perspective.”
He is also currently an associate editor of the Financial Analysts Journal, the Journal of Portfolio Management, Quantitative Finance and the Journal of Computational Finance, as well as a co-editor of the Annual Review of Financial Economics.
That’s quite an impressive résumé. Now Lo has turned his attention to evolutionary and neurobiological models of individual risk preferences and financial markets—the subject of his new book, “Adaptive Markets: Financial Evolution at the Speed of Thought.”
The Biology Of Markets
Lo writes: “The Adaptive Markets Hypothesis is based on the insight that investors and financial markets behave more like biology and physics, comprising a population of living organisms competing to survive, not a collection of inanimate objects subject to immutable laws of motion.”
The adaptive markets hypothesis differs from the efficient markets hypothesis in that “it implies that market prices need not always reflect all available information, but can deviate from rational pricing relations from time to time because of strong emotional reactions like fear and greed.”
It further implies that “market risk isn’t always rewarded by market returns” and that “the wisdom of crowds is sometimes overwhelmed by the madness of mobs.”
However, Lo adds, “the madness of mobs eventually subsides and is replaced by the wisdom of crowds—at least until the next shock disrupts the status quo. From the adaptive markets perspective the efficient markets hypothesis isn’t wrong—it’s just incomplete.”
In fact, it’s long been known that individuals aren’t always completely rational, and thus we should not be surprised that markets aren’t always efficient. Because, to replace a theory, you need a better one, Lo developed the adaptive markets hypothesis.
Lo explains: “Psychologists and behavioral economists agree that sustained emotional stress impairs our ability to make rational decisions. Fear leads us to double down on our mistakes rather than cutting our losses, to sell at the bottom and buy back at the top, and to fall into many other well-known traps that have confounded most small investors—and not a few financial professionals. Our fear makes us vulnerable in the marketplace.”
Lo shows that biofeedback measures now enable us to study human behavior. And thanks to new technological developments like MRI, we can now watch how the human brain functions in real time as we make decisions, leading to the development of the field of neuroeconomics.
Collections Of Quirks
Lo demonstrates that “we aren’t rational actors with a few quirks in our behavior—instead our brains are collections of quirks. We’re not a system with bugs; we’re a system of bugs. Working together, under certain conditions, these quirks often produce behavior that an economist would call ‘rational.’ But under other conditions they produce behavior an economist would consider wildly irrational.”
He continues: “These quirks aren’t accidental, ad hoc or unsystematic; they’re the products of brain structures whose main purpose isn’t economic rationality, but survival. Our neuroanatomy has been shaped by the long process of evolution, changing only slowly over millions of generations. Our behaviors are shaped by our brains. Some of our behaviors are evolutionarily old and very powerful.”
He summarizes the adaptive markets hypothesis with these five key principles:
1. We are neither always rational nor irrational, but we are biological entities whose features and behaviors are shaped by the forces of evolution.
2. We display behavioral biases and make apparently suboptimal decisions, but we can learn from past experience and revise our heuristics in response to negative feedback.
3. We have the capacity for abstract thinking, specifically forward-looking what-if analysis, predictions about the future based on past experience and preparation for changes in our environment. This is evolution at the speed of thought, which is different but related to biological evolution.
4. Financial market dynamics are driven by our interactions as we behave, learn and adapt to each other and to the social, cultural, political and economic environments in which we live.
5. Survival is the ultimate force driving competition, innovation and adaptation.
Lo notes that, under the adaptive markets hypothesis, we never know for certain if our current heuristic (a “rule of thumb” that guides our decisions) is good enough. We can only conclude whether it is through trial and error. Over time, heuristics will eventually adapt to yield approximately optimal solutions.
The adaptive markets hypothesis provides us with a predictive framework for making sense of behavioral biases. Not only can we understand how they arise, we can predict when they’re likely to arise and what their effects will be on markets.
A Hypothesis At Work
Lo also offers many examples of his adaptive markets hypothesis at work.
For example, he provides a brief history of David Shaw, one of the first quants and founder of the hedge fund D. E. Shaw & Co.—whose spectacular success clearly demonstrated that markets weren’t perfectly efficient. Shaw built an organization of top mathematicians who would detect and exploit even the smallest of market anomalies.
Lo relates how, along the way, Shaw noticed market dynamics were persistently changing over time, and changing in ways that required his firm to work harder for profits. As Lo writes, “effects tended to disappear over time.”
He goes on to quote Shaw, who explained: “Anomalies that had previously generated significant profits stopped making money, and you had to discover other, more complex effects that people hadn’t found. The market is never completely efficient, but it certainly has a tendency to become more efficient over time.”
Shaw admitted that quantitative trading was becoming more challenging every year as more and more quants pursued careers in finance and the level of competition kept rising. This point is one of the main themes in my recent book, co-authored with Andrew Berkin, “The Incredible Shrinking Alpha.”
Another example of the evolutionary nature of adaptive markets involves high-frequency trading. At first, high-frequency traders made windfall profits because humans were inefficient in comparison.
However, as time passed, the high-frequency traders were mainly competing against each other, and to be successful, they had to engage in an “arms race” by investing in faster and more expensive hardware. Eventually they will be pushing against evolutionary limits.
In addition, a new stock exchange, the IEX (the Investors Exchange), was launched. It has electronic speed limits that prevent high-frequency traders from participating. This is the adaptive markets hypothesis at work.
Lo’s new book is a fascinating exploration of the evolution of financial innovation. It’s filled with fascinating stories of what drives human behavior and, ultimately, markets.
Reading “Adaptive Markets” will provide you with an understanding of the reasons—based in biology and human nature—that the market is not perfectly efficient. That said, active management isn’t easy, because as Lo points out, competition and arbitrage work to make the markets ever-more efficient.
If you are interested in learning more about Lo’s book, you can read a more detailed review by economist Laurence Siegel.
This commentary originally appeared August 9 on ETF.com
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