In Adaptive Markets: Financial Evolution at the Speed of Thought, Andrew W. Lo challenges practitioners directly in his meatiest chapter, “Adaptive Markets in Action.” He asks and answers the question, “What are the practical implications of the adaptive markets hypothesis for the front lines of investing and portfolio management?” Lo writes beautifully, and the book reads quickly.
When Lo writes, investors should listen. He fits human investment behavior into a well-reasoned hypothesis he calls the adaptive markets hypothesis, which he considers an extension (rather than a contradiction) of the efficient market hypothesis (EMH). Lo creates a mindset that has strong applications to investors, financiers, and even regulators. He has worked on his hypothesis for more than a decade and quite intensively since he contracted with Princeton University Press in 2008, the year of the finance of fear and illiquidity. Lo’s thoughts on a financial crisis that exceeded anyone’s imagination necessitated additional research, so he delayed publication until 2017. The resulting book is powerful, ageless, memorable, and fun.
The author sets forth a behavioral basis for his concepts, describing the concept of survival of the fittest investors, and applies principles of biological and behavioral evolution to financial markets. Contrasting the application of physics to finance in the 1990s and early 2000s—and even earlier, with Paul Samuelson’s Foundations of Economic Analysis (Harvard University Press, 1947)—he clearly demonstrates that finance is not akin to physics, despite its so-called physics envy. Still, he reminds readers that the Black–Scholes–Merton option-pricing formula is comparable to the solution to the heat equation in thermodynamics (heat as the product of random motion).
Lo explains the contrasts between the rationalists and the behavioralists: His narratives of algorithms and their creators “freaking out” in 2007 flesh out the episode. Neuroscience and evolutionary biology, he suggests, confirm that rational expectations and the EMH capture only a portion of the full range of human investment behavior. Quants and their mathematical methods may have replaced more traditional investing for a time, but 2008 changed the face of quantitative finance and the EMH for good. “We need a new narrative to make sense of the wisdom of crowds, the madness of mobs, and evolution at the speed of thought,” he writes. Welcome to the world of behavioral biases, investigated in depth at a sophisticated level and with great humor.
Where is the meat for practitioners? It is set out in “A New Investment Paradigm,” appearing in Chapter 8, “Adaptive Markets in Action.” The five adaptive markets hypothesis principles address the following points:
Investment risk being subjected to extreme financial stress (resulting in irrationally concerted investment behavior)
The CAPM and related linear factor models being poor approximations in certain market environments
Portfolio optimization tools being useful only if matched to reality
Boundaries between asset classes becoming blurred, as macro factors and new financial institutions create links and contagion across previously unrelated assets—suggesting that managing risk through asset allocation is no longer as effective as it was during the Great Modulation (more on this later)
Realistic horizons being set proactively with risk management
Contrast these principles with the ones we grew up on that were spawned by the EMH—essentially,
the risk/reward trade-off (alpha, beta, and the CAPM),
portfolio optimization and passive investing,
asset allocation being mostly sufficient for managing the risk of an investment account, and
believing in stocks for the long run.
Lo asserts that holding a large number of assets will not eliminate systematic risk that is related to economy-wide factors, such as political instability, economic growth, unemployment, and inflation. He notes that alpha, after accounting for fees and poor benchmark performance, can easily be negative. Beta exists in passive portfolios, says Lo, by virtue of the implementation of Sharpe’s formula, which is public knowledge. He criticizes the “auto allocations” that are based on an investor’s age and/or risk tolerance. Finally, he states that holding stocks for the long run implies actually holding them for the very long periods required for the numbers to play out. This reminds me of a quote I have heard at many shareholder and analyst meetings: “If you had purchased our stock at its IPO, it would be worth thousands of times that value today.” My unvoiced response is always that I was neither born nor conscious at that point in time!
The Great Modulation that Lo refers to is the period of relatively stable financial markets and regulations from the mid-1930s to the early 2000s. During this period, the US equity market provided a reliable and steady source of investment return. Readers will easily understand how buy-and-hold strategies, asset allocation rules of thumb, and passive index funds suited most investors’ objectives during these seven decades of superior returns, even with a few short-lived blips. The Federal Reserve was “modulating” investment activity and containing volatility with its numerous changes in margin requirements, among other factors. This mindset hit a wall in 2008, following the bankruptcy of Lehman Brothers. Volatility spiked, along with trading volume and speed.
How can the adaptive markets hypothesis be applied to regulation? Dynamic margin requirements (i.e., capital requirements that vary automatically with credit and business cycles) provide an example of adaptive regulation. By using dynamic margin requirements, the Chicago Mercantile Exchange (CME) protects market participants and the CME from default attributable to extreme losses. Lo sagely suggests that such practices could be applied to the entire financial system as cruise control capital buffers. Noting that the CME’s Standard Portfolio Analysis of Risk (SPAN) has been in use for three decades, he recommends its adoption as an industry standard.
As I concluded reading this delightful book, I had three final thoughts.
First, adaptive markets can create a boom in demand for dynamic financial indexes, such as those created by robo-advisers. They are fully automatic and have no discretionary human intervention. This approach goes beyond the active allocation of passive indexes. A dynamic index can freely short or liquidate, as dictated by levels of risk tolerance and reward requirements.
Second, the book shows that disciplined investors practicing a unique asset selection/deselection based on fundamental research (not just trading indicators) can emerge as successful investors for any period of time that could be selected—not just the “long term.”
Finally, I have never before read a book with so many memorable quotes that enrich its content and meaning. To cite just one example, “Global financial markets contain enormous financial energy, and when detonated in an uncontrolled and irresponsible manner, you get bubbles, crashes, and years of nuclear fallout. But the analogy works both ways—it also implies that when we use these tools carefully and responsibly, we get virtually unlimited power for fueling innovation and economic growth.”