Andrew Lo is a professor of finance at MIT’s Sloan School of Management. After taking his PhD at Harvard in 1984, he went off briefly to the Wharton School of Business, and, four years later, went back to Cambridge and then to MIT. A prolific author, he is known to the public for A Non-Random Walk Down Wall Street, and he is known to the professionals for his numerous articles in leading journals on finance and economics. He is rich in awards.

In Adaptive Markets he proposes to challenge the scientific basis for many theories in mainstream finance and economics. That mainstream, he argues, has been based on a static approach derived from theoretical physics. It is evolutionary biology that he champions in its place. Facile à dire, difficile à faire. The social sciences are always more complicated than the natural sciences. Controlled experiments are rarely possible. Human beings learn and they adapt. An experiment having been conducted, there is no guarantee that subsequent experiments will yield the same outcome. The wider environment of habits, rules, laws, and customs within which men live is always changing. Any initial economic state, to reprise the language of physics, is compatible with a huge number of evolutionary developments. Most of them are currently unknowable. They correspond to situations of uncertainty and not risk.

What to do?

The obvious answer is to simplify things by reducing the real world to a relevant model, one tractable enough to be calculable, and realistic enough to be useful. Within the close confines of an economic model, the environment is held constant, its markets, laws, and customs fixed from the first. The system that results is not completely determined because its parameters admit of stochastic variation, but these variations are derived from a known and constant probability distribution. There is risk, but no uncertainty.

If the real world were really so simple, consumers would share the same rational, model-consistent expectations, and these expectations would be efficiently reflected in asset prices. To within the margin of risk, borrowers and lenders know what is coming: borrowers have no reason to default, and lenders, no reason to despair. In some general equilibrium models, the agent never defaults. But if an agent never defaults on a contract, his note must always be acceptable in payment. If borrowers and lenders undertake their transactions at the riskless interest rate, there is no place in the model for a great financial crisis.

Whatever the model, in life this is too good to be true.

Some progress has been made in incorporating defaults into macroeconomic models, but none of the models yet encompasses bank failures. Mainstream macroeconomics has detached itself from such issues as illiquidity, insolvency, and bankruptcy; but they are crucial in finance. As a result, macroeconomics and finance, which used to overlap a few decades ago, have drifted further and further apart. They now have largely separate languages serving largely separate masters.

Live and Learn

Lo observes the current scene from his position as a finance economist; but where other finance economists see a stable and constant macroeconomic system, he sees one that is evolving, adaptive, and fluid. “Individuals and species adapt to their environment,” he writes. “If the environment changes, the heuristics of the old environment might not be suited to the new one.”1 Live and learn. In the time between living and learning, revisions in supposed rationality are bound to occur. Judged by the standards of an older environment, agents may seem to act irrationally, and vice versa. “[I]f the environment is constantly shifting, it’s entirely possible that, like a cat chasing its tail endlessly, individuals in those circumstances will never reach an optimal heuristic.”2 This, too, will look irrational. From this new perspective, it becomes plain that “[a]n efficient market is simply the steady-state limit of a market in an unchanging financial environment.” Such a market is unlikely ever to exist beyond the textbooks, but it is still a useful abstraction, one whose performance can be approximated under certain conditions.3

Most of us macroeconomists might well be tempted to remark that we knew it all along, and if not all along then at least since the publication of Judgment under Uncertainty by Daniel Kahneman, Paul Slovic, and Amos Tversky.4 Published in 1982, their work has been reinforced by the development of behavioral economics. Lo takes up most of the early chapters of his book with a review of this research and it is, of course, no surprise that the economic agent who emerges from this summary is only distantly related to homo economicus—the man of rational economic expectations. Lo writes well and these chapters are a good read.

However much Lo’s economic agent may be adapting himself, he is still acting alone. As a solitary actor, he is not always interested in optimal solutions. Lo offers himself as an example. In choosing his wardrobe on any given day, he faces a choice of 2,016,000 unique outfits. Contemplation is required. The simple business of optimally matching his silk undershorts to his tie and pocket handkerchief would require almost twenty-four days. “The choice of clothes I settle on each day,” he adds sensibly, “may not be optimal, but it’s good enough.”5

Me? I ask my wife. That usually settles the matter.

We respond to events, Lo is right to observe, by constructing plausible narratives; and intelligence, he is right to add, is nothing less than, “the ability to construct good narratives [emphasis added].”6 The good narratives are those that make for “accurate cause-and-effect descriptions of reality.” But no one acting alone has the time or the energy to construct narratives about most phenomena, and only specialists are able to derive their narratives from first principles. We learn and accept such narratives from others. In the social sciences, opinions matter; there are fashions in economics as in clothing.

Evasive Abstractions

Mainstream economic models ignore the poorly understood process of learning. Such models may still be useful. Carefully hedged mainstream predictions are better than no predictions at all. The assumption that, given perfect information, economic agents are apt to converge rapidly onto rational expectations is most nearly justified when models assess the impact of current events on short-run, high-frequency developments in asset prices. So long as the time is short, the overall structure of the system stays constant. But even this commonsensical conclusion fails when a crisis occurs: a terrorist attack, the unexpected fall of a great financial house, the outbreak of war. In economic life, the structure of the economy changes, and the relationship between temporal volatility and asset return turns negative. Asset prices tend to vary much more than is consistent with their dividend path because economic agents are uncertain about the structural changes taking place all around them.

It is in this context that Lo sets out principles of investment management from the perspective of adaptive markets. “When the population of investors is dominated by individuals facing extreme financial threats,” he writes, “they can act in concert and irrationally, in which case risk will be punished. These periods can last for months or, in extreme cases, for decades.”7 Capital asset pricing models and similar linear factor models, he observes, “are useful inputs for portfolio management, but they rely on several key economic and statistical assumptions that may be poor approximations in certain market environments.”8 It is better to know “the environment and population dynamics of market participants” than the single factors that figure in capital asset pricing models.

These remarks are certainly commonsensical. So, too, are Lo’s observations about portfolio optimization techniques. They are useful, Lo writes, “only if the assumptions of statistical stationarity and rationality are good approximations to reality.”9 Passive investors take note. “The notion of passive investing is changing due to technological advances, and risk management should be a higher priority, even for passive index funds.” Lo designates the years between 1940 and 2000 as the “great modulation.”10 It was during these sixty years that investors managed their risk by means of asset allocation. With the happy times receding into the past, “[t]he boundaries between asset classes are becoming blurred, as macro factors and new financial institutions create links and contagion across previously unrelated assets.” With a figurative hand placed on the reader’s shoulder, Lo remarks that, yes, “equities do offer attractive returns over the very long run.” He adds at once that, “few investors can afford to wait it out,” if only because, as John Maynard Keynes noted, in the long run we are all dead.11

My own concern as a macroeconomist is more related to dynamic, stochastic general equilibrium (DSGE) models than finance. These models are used in forecasting developments in the national economy. How useful are such forecasts for central bankers? It is a natural question, and one that Rochelle Edge and Refet Gürkaynak addressed in 2011. They concluded that DSGE models were not very useful at all.12 This is not so surprising. Such models assume that the economy is structurally stable, and during periods when this is true, the economy will resemble a dynamical system subject to transient shocks—technological innovations, sudden shifts in productivity. Shocks are unpredictable. Trend-following methods will be equally good in predicting how much things are changing, and equally bad in predicting how fast they are doing so.

Problems arise when the underlying structure of the economy changes, as it did during and after the great financial crisis of 2008. DSGE models characteristically treat a shock as a one-off, and predict a reversion to a prior equilibrium. This was a recipe for failure in 2008. When DSGE forecasts go wrong, they go wrong in a big way, but what is worse, their errors will typically be autocorrelated. It is seldom easy to distinguish quickly between a transient shock to a system and a structural change in the system.

The Evolved Economist

If imitation is the sincerest form of flattery, economists in the twenty-first century must ask themselves whether they intend to flatter the physicists or the biologists. If it is to be the first, then economists must concentrate their attention on the properties of static, stable, stochastic systems; if the second, then on the properties of systems undergoing evolutionary change. Lo wants us to shift from the first to the second:

The Adaptive Markets Hypothesis is based on the insight that investors and financial markets behave more like biology than physics, comprising a population of living organisms competing to survive, not a collection of inanimate objects subject to immutable laws of motion.13

He is not alone. “It has become part of the accepted wisdom,” Freeman Dyson argued, “that the twentieth century was the century of physics and the twenty-first century will be the century of biology.”14 I am sympathetic with this view, but I wonder whether the social sciences have yet matured sufficiently to benefit from biology. What insights into finance or macroeconomics do Lo’s adaptive markets offer?

In one important respect, I believe that Lo makes his program sound simpler than it is. Throughout his book, he generally describes the environment as a given, set externally by independent but changeable forces, to which economic agents then adapt themselves. But in practice, the environment is man-made, notably in the structure of markets, the legal system, customary modes of behavior. Our technology is man-made, and many technological advances are derived from adaptations to man-made pressures—war, the moon landings, climate change. As those dealing with financial regulation know, crises in financial markets lead to a regulatory reaction. Often it is peremptory, a kneejerk response both to the currently dominating narrative of what went wrong and to the resulting public outcry that what went wrong must never go wrong again. Regulations, if they are not superfluous, most be coercive, thus affording agents in financial markets every incentive to get around them. The combination of ever-changing behavior, new technologies, and unexpected events can lead to another financial crisis. Finance is an everlasting dance between agents seeking to exploit the existing structure and regulators trying desperately to patch it up.

Lo simplifies in another respect, one that I have already noted. His economic agent is far too much of a solitary character, someone adapting individually to his environment. Adaptation is a joint effort. My own analysis of current policies and analysis owes far more to the Financial Times than to any individual research that I have undertaken.

The failure properly to appreciate that the communal bath, which all of us inhabit, has a distorting effect on Lo’s analysis. In reverting to his incarnation as an investment advisor, Lo commends a portfolio with a “dynamic volatility management strategy.”15 It works quite simply.

If the estimated volatility of the index at a given date exceeds a pre-specified threshold, it invests a portion of the fund in cash. On the other hand, if the volatility falls below that threshold, it invests more than 100 percent of the fund in the index: in other words, it leverages the fund. That’s it.16

Lo then reports that an investor who uses such a “cruise-controlled fund” does much better than an investor who buys and holds. This is not, Lo urges, a more sophisticated version of portfolio insurance.17 But surely he protests too much. Bad news is much more striking than good news. Surges in volatility will normally be related to news causing asset prices to drop sharply. A volatility-adjusted fund, if widely adopted, would increase the amplitude of fluctuations both in the level, and the volatility, of equity prices. What might be a good strategy for Lo may worsen the outcome for everyone adopting a similar strategy. Forcing all banks to adopt the same portfolio as the best run bank may weaken the system as a whole. For an ecological system, diversity is the safest long-term policy.

So, too, for a portfolio.

Clash of the Titans

So far as Lo is concerned, it is AMM versus EMH: the adaptive market mechanism and the efficient market hypothesis. Lo is quite confident that it will be the AMM that emerges victorious during financial crashes, when volatility rises and equity prices tank:

Sudden increases in equity volatility cause a significant portion of investors to rapidly reduce their holdings through a fight-or-flight response, better known in financial contexts as “freaking out.” This panic selling puts downward pressure on equity prices, and upward pressure on the prices of safer assets, which are now in higher demand. The price changes created by investors freaking out cause the normally positive association between risk and reward to be temporarily violated. Once these emotional responses subside, the madness of mobs is replaced by the wisdom of crowds, and the usual risk/reward relation is restored.18

But not so fast. A word of caution is necessary. There is an asymmetry in economic relationships. Losers in any transaction must restore their position immediately. Winners have time to adjust. There are forced sales in markets, but hardly any forced purchases. In international economics, it is the debtors who fear kneecapping, not the creditors. I do not doubt that panic selling can occur, but I have no idea of the balance between panic selling and forced sales during market crashes.

And neither does Lo.

Hyperbolic Hyperbole

Psychology and the neurosciences very often take pride of place in Lo’s analysis. This is as it should be. He is making a case. But whether the case quite gets made is a different matter. Economists have known for more than three centuries that an asset’s value is time sensitive. The future imposes a discount on what something is worth. What if asset prices are discounted inconsistently?

But most Homo sapiens are inconsistent about future value. If offered the choice, most of us will take a $100 bill now versus $200 in a month. However, it offered a choice between $100 a year from now or $200 in thirteen months, most of us would take the $200 in thirteen months. Economists call this behaviour hyperbolic discounting, … Compared to the perfectly rational Homo economicus, we’re more impulsive over the short term and more logical over the long term, or as a spoiled eight-year-old once said at a birthday party I chaperoned, “I want it all and I want it now!”19

Such, Lo supposes, are the facts; and they do not surprise him.

Neuroscience has shown that human brain processes value over different time periods inconsistently. We are short-term demanding and long-term inattentive by nature. From the economist’s perspective, it’s astonishing that there are systems in the brain that explain this phenomenon at all.20

The economist Eugene Fama has apparently managed to circumvent his natural tendency toward hyperbolic discounting by means of a simple rule. It is to Fama’s rule that Lo wishes to draw to the reader’s attention.

When he’s invited to give a talk or engage in some business activity, he has a simple rule for deciding whether to accept: no matter how far in the future it’s scheduled, he asks himself whether it’s something he would want to do if the event were next week; if the answer is yes, he accepts, otherwise, he politely declines. This simple rule of thumb ensures that he uses the same discount rate across all decision horizons.21

Were I to follow this rule myself, I would never agree to take on commitments that are beneficial in the long term, but painful or arduous in the short term. Visits to the dentist would go unmade. Accepting a longer-term engagement can be a useful commitment device, precisely because our short-run discount rate can be excessively high, and lead us to refuse near-future engagements that we should accept.

In his struggles with the mainstream, my sympathies, but not my allegiances, are with Lo. The social sciences may individually give the impression of disorder, but collectively they are like an immense ocean liner, something difficult to turn. I think that Lo understates the problems turning the social sciences toward evolutionary biology. Structural change in economic systems are the result of human interactions. They lie beyond the reach of current theory. The agent-based models pioneered by Doyne Farmer are going in the right direction.22 But they are still far from the dance between events and structural change that we see in reality.

The outcome of the conflict between the AMM and the EMH is for the moment unknowable. In the essentially static world of the EMH and various DSGE models, there is no room for history. In that world, expectations are rational and, even if shocked, the system settles down by settling back. Mainstream economics has ignored and downgraded economic history. But until evolutionary biology can lead us forward, our history is all that we have.

  1. Andrew Lo, Adaptive Markets: Financial Evolution at the Speed of Thought (Princeton, NJ: Princeton University Press, 2017), 189. 
  2. Ibid. 
  3. Ibid., 221. 
  4. Daniel Kahneman, Paul Slovic, and Amos Tversky, eds., Judgment Under Uncertainty: Heuristics and Biases (New York: Cambridge University Press, 1982). 
  5. Andrew Lo, Adaptive Markets: Financial Evolution at the Speed of Thought (Princeton, NJ: Princeton University Press, 2017), 180. 
  6. Ibid., 128. 
  7. Ibid., 282. 
  8. Ibid. 
  9. Ibid. 
  10. Ibid., 254. 
  11. Ibid., 282. 
  12. Ibid., 235. 
  13. Ibid., 2. 
  14. Freeman Dyson, “Our Biotech Future,” The New York Review of Books, July 29, 2007. 
  15. Andrew Lo, Adaptive Markets: Financial Evolution at the Speed of Thought (Princeton, NJ: Princeton University Press, 2017), 271–76. 
  16. Ibid., 271. 
  17. Ibid., 274. 
  18. Ibid., 261. 
  19. Ibid., 98. 
  20. Ibid., 99. 
  21. Ibid., 238. 
  22. J. Doyne Farmer and Duncan Foley, “The Economy Needs Agent-Based Modelling,” Nature 460 (2009): 685–86.