18 www.cfapubs.org ©2012 CFA Institute

Financial Analysts JournalVolume 68 Number 2©2012 CFA Institute

P E R S P E C T I V E S

Adaptive Markets and the New World OrderAndrew W. Lo

n the wake of the financial crisis of 2007–2009,investors, financial advisers, portfolio manag-ers, and regulators are still at a loss as to howto make sense of its repercussions and where

to turn for guidance. The traditional paradigms ofmodern portfolio theory and the efficient marketshypothesis (EMH) seem woefully inadequate, butsimply acknowledging that investor behavior maybe irrational is cold comfort to individuals whomust decide how to allocate their assets amongincreasingly erratic and uncertain investment alter-natives. The reason for this current state of confu-sion and its strange dynamics is straightforward:Many market participants are now questioning thebroad framework in which their financial decisionsare being made. Without a clear and credible nar-rative of what happened, how it happened, why ithappened, whether it can happen again, and whatto do about it, their only response is to react instinc-tively to the most current crisis, which is a surerecipe for financial ruin.

In this article, I describe how the adaptive mar-kets hypothesis—an alternative to the EMH thatreconciles the apparent contradiction betweenbehavioral biases and the difficulty of outperform-ing passive investment vehicles—can make senseof both the current market turmoil and the emer-gence and popularity of the EMH in the decadesleading up to the recent crisis. Contrary to currentpopular sentiment, the EMH is not wrong; it ismerely incomplete. Markets are well behaved mostof the time, but like any other human invention,they are not infallible and they can break downfrom time to time for understandable and predict-able reasons. By viewing financial markets andinstitutions from the perspective of evolutionarybiology rather than physics,1 I believe we can con-struct a much deeper and more accurate narrativeof how markets work and what we can do to pre-pare for their periodic failures.

The Traditional Investment ParadigmTo understand the limitations of the traditionalinvestment paradigm, consider its most commontenets: (1) There is a positive trade-off between riskand reward across all financial investments—assetswith higher risk offer higher expected return; (2) thistrade-off is linear, risk is best measured by equity“beta,” and excess returns are measured by “alpha,”the average deviation of a portfolio’s return from thecapital asset pricing model (CAPM) benchmark; (3)reasonably attractive investment returns may beachieved by passive, long-only, highly diversifiedmarket-cap-weighted portfolios of equities (i.e.,those containing only equity betas and no alpha); (4)strategic asset allocation among asset classes is themost important decision that an investor makes inselecting a portfolio best suited to his risk toleranceand long-run investment objectives; and (5) allinvestors should be holding stocks for the long run.Collectively, these five basic principles have becomethe foundation of the investment managementindustry, influencing virtually every product andservice offered by professional portfolio managers,investment consultants, and financial advisers.

Like all theories, these statements are meant tobe approximations to a much more complex reality.Their usefulness and accuracy depend on a numberof implicit assumptions regarding the relationshipbetween risk and reward, including the following:A1. The relationship is linear.A2. The relationship is static across time and cir-

cumstances.A3. The relationship’s parameters can be accu-

rately estimated.A4. Investors have rational expectations.A5. Asset returns are stationary (i.e., their joint

distribution is constant over time).A6. Markets are efficient.

Each of these assumptions can be challenged ontheoretical, empirical, and experimental grounds, butall theories are, by definition, abstractions thatinvolve simplifying assumptions. The relevant ques-tion is not whether these assumptions are literallytrue—they are not—but, rather, whether the approx-imation errors they generate are sufficiently small

Andrew W. Lo is the Harris & Harris Group Professorat the MIT Sloan School of Management and chairmanand chief investment strategist at AlphaSimplex Group,LLC, Cambridge, Massachusetts.

I

March/April 2012 www.cfapubs.org 19

Adaptive Markets and the New World Order

that they can be ignored for practical purposes. Ipropose that the answer to this question has changedover the past decade. From the 1940s to the early2000s—a period of relatively stable financial marketsand regulations—these assumptions were reason-able approximations to U.S. financial markets. How-ever, the approximation errors have greatly increasedin more recent years for reasons that we can identify,to the point where they can no longer be ignored.

Figure 1 provides a clear illustration of thisperspective, in which the cumulative total return ofthe CRSP value-weighted stock market return indexfrom January 1926 to December 2010 is plotted on alogarithmic scale (so that the same vertical distancecorresponds to the same percentage return regard-less of the time period considered). This strikinggraph shows that the U.S. equity market has been aremarkably reliable source of investment returnfrom the 1940s to the early 2000s, yielding an unin-terrupted and nearly linear log-cumulative-growthcurve over these six decades. This period, which

followed the Great Depression, should be called theGreat Modulation because of the stability that char-acterized financial markets during this time.2 Whilethere were certainly some sizable ups and downsover this time span, from the perspective of an inves-tor with a 10- to 20-year horizon, investing in a well-diversified portfolio of U.S. equities would havegenerated comparable average returns and volatil-ity at almost any point during this 60-year period.

In such a stable financial environment,assumptions A1–A6 do yield reasonable approxi-mations; hence, it is not surprising that the tradi-tional investment paradigm of a linear risk–rewardtrade-off, passive buy-and-hold index funds, and60/40 asset allocation heuristics emerged andbecame popular during this period. The morepressing issue at hand is whether the most recentdecade can be ignored as a temporary anomaly—the exception that proves the rule—or if it is aharbinger of a new world order. There is mountingevidence that supports the latter conclusion.

Figure 1. Cumulative Total Return of CRSP Value-Weighted Stock Market Return Index, January 1926–December 2010

Notes: This figure provides a semi-logarithmic plot of cumulative total return of the CRSP value-weighted return index from January1926 to December 2010 and a standard plot of the total number of stocks used in the index.

Sources: CRSP and author’s calculations.

VWRETD Cumulative ReturnNumber of Stocks

Cumulative Return

10,000

1,000

100

10

1

0.1

Number of Stocks

10,000

8,000

6,000

4,000

2,000

1,000

9,000

7,000

5,000

3,000

030/Jan/26 31/Jan/46 31/Jan/66 31/Jan/0631/Jan/86

20 www.cfapubs.org ©2012 CFA Institute

Financial Analysts Journal

A New World OrderAlthough every generation of investors is likely toconsider its own environment unique and special,with several unprecedented features and innova-tions, there are objective reasons to believe that theenvironment of the last decade is significantly dif-ferent from that of the six decades prior. One obvi-ous indication is volatility, something all investorshave become painfully aware of over the past fewyears. Figure 2 depicts the annualized volatility ofdaily CRSP value-weighted index returns overtrailing 125-day windows, a measure of short-termvolatility. This graph shows that the aftermath ofthe 1929 stock market crash was an extremely vol-atile period, which was followed by the decades ofthe Great Modulation, when volatility was consid-erably more muted. However, the highest-volatilityperiod occurred not during the Great Depressionbut much more recently, during the fourth quarterof 2008 in the wake of the Lehman Brothers bank-ruptcy. Other market statistics—such as tradingvolume, market capitalization, trade-executiontimes, and the sheer number of listed securities andinvestors—point to a similar conclusion: Today’sequity markets are larger, faster, and more diversethan at any other time in modern history. We areliving in genuinely unusual times.

This pattern may well be a reflection of a muchbroader trend: population growth. Figure 3 depictsthe estimated world population from 10,000 BC tothe present in logarithmic scale, and as with U.S.equities, this series also exhibits exponentialgrowth (of course, the two phenomena are not unre-lated).3 Figure 3 shows three distinct periods ofhuman population growth over the last 12 millen-nia: low growth during the Stone Age, from 10,000BC to 4,000 BC; moderate growth from the start ofthe Bronze Age, around 4,000 BC, to the IndustrialRevolution in the 1800s; and much faster growthsince then. In 1900, the world population was esti-mated to be approximately 1.5 billion individuals;the most recent estimate puts the current worldpopulation at about 7 billion. Within the space of acentury, we have more than quadrupled the num-ber of inhabitants on this planet, and the vast major-ity of these individuals must work to survive andwill, therefore, engage in some form of life-cyclesavings and investment activity during their life-times. This naturally increases the required scale offinancial markets as well as the complexity of theinteractions among the various counterparties.

This dramatic increase in human population isno accident—it is a direct consequence of technolog-ical innovation that has allowed us to manipulate

Figure 2. Annualized Volatility of Daily CRSP Value-Weighted Index Returns over Trailing 125-Day Windows, 2 January 1926 to 31 December 2010

Sources: CRSP and author’s calculations.

Volatility (%)

70

60

50

40

30

20

10

02/Jan/26 8/Sep/38 29/Aug/51 22/Jul/66 24/Aug/81 5/Aug/96

The Great Modulation

March/April 2012 www.cfapubs.org 21

Adaptive Markets and the New World Order

our environment and natural resources to meet and,ultimately, greatly exceed our subsistence needs.Advances in agricultural, medical, manufacturing,transportation, information, and financial technol-ogies have all contributed to this extraordinary runof reproductive success by Homo sapiens. Theseadvances are largely the result of competitive eco-nomic forces, by which innovation is richlyrewarded and through which recent innovationsquickly become obsolete. This has led to a far differ-ent world today than the world of just a few decadesago. One of the most compelling illustrations of thisdifference is the pair of graphs in Figure 4, whichdisplay the population size, per capita GDP, andaverage life expectancy for various countries at twopoints in time: 1939 (Panel A) and 2009 (Panel B). In1939, the United States—the large light-green circlein the upper-right range of Panel A—was in anenviable position, with one of the highest levels ofper capita GDP and life expectancy among themajor industrialized countries of the world andonly a handful of close competitors.

Panel B, however, tells a very different story:A mere six decades later, the United States is nolonger the only dominant economic force in theglobal economy. It is now surrounded by manysizable competitors, including Japan (the largest

red circle closest to the United States) and Europe(the many orange circles to the left of the UnitedStates). And the two most populous countries in theworld—China (the largest red circle) and India (thelargest light-blue circle)—have had an enormousimpact on global trade patterns, labor supply, rel-ative wages and production costs, foreignexchange rates, and innovation and productivity injust the last 20 years. Given these seismic economicshifts, is it any wonder that the dynamics of globalfinancial asset prices, which must ultimately reflectthe supply and demand of real assets, have becomeless stable in recent years? The Great Modulation isgiving way to a new world order.

The Adaptive Markets HypothesisThese large-scale economic changes and their polit-ical and cultural consequences are the ultimate rea-sons that assumptions A1–A6 have become lessplausible in the current environment, and whyfinancial market dynamics today are so differentfrom what they were during the Great Modulation.Technological advances are often accompanied byunintended consequences, including pollution,global warming, flu pandemics, and, of course,financial crises. Financial technology in particularhas been a double-edged sword. It has facilitated

Figure 3. Estimated World Population from 10,000 BC to the Present

Sources: U.S. Census Bureau (International Data Base) and author’s calculations.

World Population (millions)

10,000

1,000

100

10

110,000 BC 2000 AD6,000 BC8,000 BC 4,000 BC 2,000 BC 0 AD

2011: 6.9 Billion

1900: 1.5 Billion

22 www.cfapubs.org ©2012 CFA Institute

Financial Analysts Journal

tremendous global economic growth over the pastdecade by gathering and channeling vast amountsof capital from asset owners in one part of the worldto entrepreneurs in other parts of the world who areable to make better use of it. But globally connectedcapital markets imply that local financial shockswill spread more quickly to other regions, as wehave seen with the recent financial crisis and thespillover effects of the ongoing European sovereigndebt crisis.

This new world order is the context in whichthe adaptive markets hypothesis (AMH) hasemerged as a more complete explanation of thebehavior of financial markets and their partici-pants.4 The AMH begins with the recognition thathuman behavior is a complex combination of mul-tiple decision-making systems, of which logicalreasoning is only one among several. The ability toengage in abstract thought, to communicate thesethoughts to others, and to act in a coordinated

fashion to achieve complex goals seem to beuniquely human traits that have allowed us todominate our environment like no other species,ushering in the age of Homo economicus (at leastaccording to economists). Neuroscientists havetraced these abilities to a region of the brain knownas the neocortex, an anatomical structure that isunique to mammals and is particularly large anddensely connected in Homo sapiens.5 On an evolu-tionary timescale, the neocortex is the most recentcomponent of the brain—hence, its name.

Before humans developed these impressivefaculties for abstraction, however, we still managedto survive in a hostile and competitive world,thanks to more primitive decision-making mecha-nisms. One example is the fight-or-flight response,a series of near-instantaneous physiological reac-tions to physical threats that include increasedblood pressure and blood flow to the large-musclegroups; the constriction of blood vessels in other

Figure 4. Population Size, Per Capita GDP, and Average Life Expectancyfor Various Countries, 1939 and 2009

(continued)

Life Expectancy (years)A. 1939

85

75

80

70

65

60

55

50

45

40

35

30

25

Country Population Is Represented by the Size of theCircles According to the Following Scale:

Per Capita GDP (inflation-adjusted PPP$)

0 1.46Population (billions)

1,000 2,000 10,000400200 4,000 20,000 40,000

East Asia and PacificSub-Saharan Africa South Asia

Americas Middle East and North AfricaEurope and Central Asia

March/April 2012 www.cfapubs.org 23

Adaptive Markets and the New World Order

parts of the body; rapid release into the bloodstreamof such nutrients as glucose; decreased appetite,digestive activity, and sexual desire; tunnel visionand loss of hearing; and much faster reflexes.6 Inshort, the fight-or-flight response prepares us todefend ourselves from attack by either evading ourattacker or fighting to the death. This complex col-lection of decisions is not made voluntarily and aftercareful deliberation but is a “hardwired” automaticresponse in most animal species; if we had to thinkabout whether to react in this manner, we wouldend up as dinner for less pensive predators.

The key insight regarding these variousdecision-making mechanisms is that they are notcompletely independent. In some cases, such as thefight-or-flight response, they work together toachieve a single purpose: a physiological reactioninvolving several systems that are typically inde-pendent but that become highly correlated under

specific circumstances.7 Such coordination, how-ever, sometimes implies that certain decision-making components take priority over others. Forexample, Baumeister, Heatherton, and Tice (1994)documented many instances in which an extremeemotional reaction can “short-circuit” logical delib-eration, inhibiting activity in the neocortex. From anevolutionary standpoint, this makes perfect sense—emotional reactions are a call to arms that should beheeded immediately because survival may dependon it, and higher-brain functions, such as languageand logical reasoning, are suppressed until thethreat is over (i.e., until the emotional reaction sub-sides). When being chased by a tiger, it is moreadvantageous to be frightened into scrambling upa tree than to be able to solve differential equations!

From a financial decision-making perspective,however, this reaction can be highly counterpro-ductive. Adaptations like the fight-or-flight

Figure 4. Population Size, Per Capita GDP, and Average Life Expectancyfor Various Countries, 1939 and 2009 (continued)

Note: PPP stands for purchasing power parity.

Source: http://gapminder.org.

Life Expectancy (years)B. 2009

85

80

75

70

65

60

55

50

45

40

35

30

25

201,000 2,000 10,000400200 4,000 20,000 40,000

Country Population Is Represented by the Size of theCircles According to the Following Scale:

Per Capita GDP (inflation-adjusted PPP$)

East Asia and PacificSub-Saharan Africa South Asia

Americas Middle East and North AfricaEurope and Central Asia

0 1.46Population (billions)

24 www.cfapubs.org ©2012 CFA Institute

Financial Analysts Journal

response emerged in response to physical threats,not financial losses, yet our instinctive response toboth is much the same. The behavioral biases thatpsychologists and behavioral economists have doc-umented are simply adaptations that have beentaken out of their evolutionary context: Fight-or-flight is an extremely effective decision-makingsystem in a street fight but is potentially disastrousin a financial crisis.8

The focus of the AMH is not on any singlebehavior but, rather, on how behavior responds tochanging market conditions. In the framework ofthe AMH, individuals are neither perfectly rationalnor completely irrational but are intelligent,forward-looking, competitive investors who adaptto new economic realities. When John MaynardKeynes was criticized for flip-flopping on the goldstandard, he is said to have replied, “When the factschange, sir, I change my mind; what do you do?”By modeling the change in behavior as a function ofthe environment in which investors find them-selves, it becomes clear that efficient and irrationalmarkets are two extremes, neither of which fullycaptures the state of the market at any point in time.

In fact, human responses to risk are more sub-tle than the fight-or-flight mechanism might imply.One striking illustration is the “Peltzman effect,”named after the University of Chicago economistSam Peltzman. In a controversial empirical studyon the impact of government regulations requiringthe use of automobile safety devices, such as seatbelts, Peltzman (1975) found that these regulationsdid little to reduce the number of highway deathsbecause people adjusted their behavior accord-ingly, presumably driving faster and more reck-lessly. Although in certain cases, the number offatalities among the occupants of autos involved inaccidents did decline over time, his analysisshowed that this decline was almost entirely offsetby an increase in the number of pedestrian deathsand nonfatal accidents. He concluded that the ben-efits of safety regulations were mostly negated bychanges in driver behavior. Since then, many stud-ies have extended Peltzman’s original study byconsidering additional safety devices, such as airbags, antilock brakes, and crumple zones. In somecases, these new studies have confirmed, but inother cases they have refuted, Peltzman’s originalfindings after controlling for such other factors asenforcement practices, driver age, rural versusurban roads, vehicle weight, and so on.9

These mixed results are not surprising giventhe many different contexts in which we drive auto-mobiles. While an impatient commuter might welltake advantage of improved safety by drivingfaster and getting to work a few minutes early, the

same may not apply to visiting tourists. In a recentstudy of the Peltzman effect, however, Sobel andNesbit (2007) investigated the one driving contextin which there are very few confounding factorsand no doubt that all drivers are singularly focusedon arriving at their final destination as quickly aspossible—NASCAR races. Their conclusion: “Ourresults clearly support the existence of offsettingbehavior in NASCAR—drivers do drive more reck-lessly in response to the increased safety of theirautomobiles” (p. 81). When the only goal is toreduce driving time, it seems perfectly rational thatincreased safety would induce drivers to drivefaster. From a financial perspective, this is com-pletely consistent with basic portfolio theory: If anasset’s volatility declines but its expected returnremains unchanged, investors will put moremoney into such an asset, other things (like corre-lations to other assets) being equal.

If safety improvements are genuinely effective,such adaptive behavior may result in the appropri-ate outcomes. But what if these “improvements”were not, in fact, as effective as they were perceivedto be? In such cases, drivers may end up takingmore risk than they intended to because they feelsafer than they really are, greatly increasing thelikelihood of unintended consequences. Risk per-ception may differ from risk reality, which playeda critical factor in the recent financial crisis. Giventhe AAA ratings of the safest tranches of collateral-ized debt obligations (CDOs) and the relativelyshort and default-free history of those new securi-ties, investors may have thought they were saferthan they were.10

It is easy to see how the adaptive nature ofhuman risk preferences can generate market bub-bles and crashes if perception can become discon-nected from reality from time to time. This potentialdisconnect is one of the most important motiva-tions for producing and publicizing accurate, objec-tive, and timely risk analytics in financialcontexts—negative feedback loops are one ofnature’s most reliable mechanisms for maintainingstability, or homeostasis. The aggregation of theseindividual behavioral dynamics explains why mar-kets are never completely efficient or irrational—they are simply adaptive.

Practical ImplicationsAlthough in its infancy, the AMH offers at least fiveimmediate practical implications for investors, port-folio managers, and policymakers. Perhaps the mostimportant implication from an investment manage-ment perspective is that the trade-off between riskand reward is not stable over time or circumstancesbut varies as a function of the population of market

March/April 2012 www.cfapubs.org 25

Adaptive Markets and the New World Order

participants and the business environment in whichthey are immersed. During periods of market dislo-cation—when fear rules the day—investors willreduce their holdings of risky assets and move intosafer investments. This will have the effect of reduc-ing the average return on risky assets and increasingthe average return on safer ones, exactly the oppo-site of what rational finance predicts: The “madnessof mobs” replaces the wisdom of crowds. However,during more “normal” periods when market condi-tions are benign (i.e., when price fluctuations arewithin historically observed ranges and currentevents have no overriding consequences for marketvaluations or the conduct of business), assumptionsA1–A6 are good approximations to reality and thewisdom of crowds returns.

If, over an extended period of time, the wisdomof crowds is more common than the madness ofmobs—as theory and empirical evidence seem tosuggest—then statistical averages over long hori-zons (e.g., market risk premiums) will largelyreflect the wisdom of crowds. This observation,however, does not imply that the wisdom ofcrowds must hold at every point in time. There maybe periods of collective fear or greed when themadness of mobs takes over, not unlike the notionof “punctuated equilibria” in evolutionary biologywhen, after long periods of evolutionary stasis,relatively sudden changes lead to extinctions andnew species in their aftermath.11 It is precisely dur-ing such periods in financial history that bubblesand crashes emerge, and assumptions A1–A6become less accurate approximations to reality.

This dynamic can be observed in the historicaldata, as Table 1 and Figure 5 illustrate. Table 1reports the historical means and standard devia-tions of stocks and bonds from January 1926 toDecember 2010, which confirms the traditionalview that there is a positive risk–reward trade-off.However, Figure 5, which depicts 1,250-day(approximately five-year) rolling-window geomet-

rically compounded returns and standard devia-tions of daily CRSP value-weighted index returns,shows a time-varying and often negative relation-ship between the two; the correlation betweenthese two daily series is 59.9 percent!

This empirical anomaly was first documentedby Fischer Black (1976), who explained it as a “lever-age effect”: When equity prices decline (generatingnegative returns), this implies higher equity volatil-ity because corporations with debt in their capitalstructure are now more highly leveraged. Thisexplanation seems eminently plausible, except thatthis so-called leverage effect is still present and evenstronger among all-equity-financed companies(Hasanhodzic and Lo 2011). The AMH provides analternative explanation: Sudden increases in equityvolatility cause a significant portion of investors toreduce their equity holdings rapidly through afight-or-flight response, better known in financialcontexts as a “flight to safety.” This process ofdivestment puts downward pressure on equityprices and upward pressure on the prices of saferassets, causing the normally positive associationbetween risk and reward to be temporarily violated.Once these emotional responses subside, the mad-ness of mobs is replaced by the wisdom of crowdsand the usual risk–reward relation is restored.

The second implication of the AMH is thatmarket efficiency is not an all-or-nothing conditionbut a continuum, one that depends on the relativeproportion of market participants who are makinginvestment decisions with their prefrontal cortexesversus their more instinctive faculties, such asfight-or-flight. In other words, the degree of marketefficiency—similar to the energy efficiency of an airconditioner or a hot-water heater—should be mea-sured; it is directly related to the degree to which agiven set of market participants is adapted to theenvironment in which the market has developed.A relatively new market is likely to be less efficient

Table 1. Historical Means and Standard Deviations of Stocks and Bonds, January 1926–December 2010

Asset ClassGeometric Mean

(%)Arithmetic Mean

(%)Standard Deviation

(%)

Small-company stocks 12.1 16.7 32.6Large-company stocks 9.9 11.9 20.4Long-term corporate bonds 5.9 6.2 8.3Long-term government bonds 5.5 5.9 9.5Intermediate-term government bonds 5.4 5.5 5.7U.S. Treasury bills 3.6 3.7 3.1

Source: Ibbotson (2011, Table 2–1).

26 www.cfapubs.org ©2012 CFA Institute

Financial Analysts Journal

than a market that has been in existence fordecades, but even in the latter case, inefficienciescan arise if the environment shifts or the populationof investors changes materially. In fact, market effi-ciency can be measured and managed, as manysecurity exchanges routinely do as part of theirongoing efforts to improve market quality. Thisperspective implies that assumptions A1–A6should be viewed not as either true or false but,rather, as approximations that may become moreor less accurate as market conditions and investorpopulations change.

This observation leads to a third implication ofthe AMH, which is that investment policies mustbe formulated with these changes in mind andshould adapt accordingly. When assumptions A1–A6 are reasonable approximations to current con-ditions, traditional investment approaches are ade-quate, but when those assumptions break down,perhaps because of periods of extreme fear orgreed, the traditional approaches may no longer beeffective. One obvious example is the notion ofdiversification. Diversifying one’s investmentsacross 500 individual securities—for instance, thestocks in the S&P 500 Index—used to be sufficient

to produce relatively stable and attractive returnsover extended periods of time. However, in today’senvironment, these 500 securities are so tightly cou-pled in their behavior that they offer much lowerdiversification benefits than in the past. The princi-ple of diversification is not wrong; it is simplyharder to achieve in today’s macro-factor-drivenmarkets. Therefore, its implementation must beadapted to the current environment (e.g., diversi-fying across a broader array of investments in mul-tiple countries and according to factor exposuresinstead of or in addition to asset classes).

A fourth implication of the AMH involves animportant consequence of competition, innovation,and natural selection in financial markets: thetransformation of alpha. Under assumptions A1–A6, alpha should always equal zero. Under theAMH, alpha can be positive from time to time, butany unique and profitable investment opportunitywill eventually be adopted by many investors, inwhich case the alpha will either be reduced to zeroor reach an equilibrium level at which the risksassociated with its exploitation are sufficient tolimit the number of willing participants to somefinite and sustainable number. This is one possible

Figure 5. Geometrically Compounded Returns and Standard Deviations of Daily CRSP Value-Weighted Index Returns over 1,250-Day RollingWindows, 19 March 1930 to 31 December 2010

Sources: CRSP and author’s calculations.

1,250-Day Rolling-Window Volatility (annualized)

1,250-Day Rolling-Window Geometric Compound Return (annualized)

Percent

50

40

30

20

10

0

–10

–20

–3019/Mar/30 17/Nov/42 7/Aug/56 9/Oct/86 17/Oct/016/Oct/71

March/April 2012 www.cfapubs.org 27

Adaptive Markets and the New World Order

explanation of the relatively stable risk–rewardtrade-off of U.S. equities during the Great Modula-tion, and of why that trade-off seems to havechanged in recent years as greater consolidationamong asset owners, owing to economies of scaleand scope, and mounting pressure in low-yieldenvironments to find new sources of expectedreturn have intensified global competition acrossall financial markets and strategies. Even morestriking examples can be found in the hedge fundindustry (see Lo 2010; Getmansky, Lee, and Lo,forthcoming 2012), the Galápagos Islands of thefinancial sector, where evolution is apparent to thenaked eye and alphas decay rapidly, only to reap-pear a few years later after enough investors andmanagers have moved on to greener pastures.

Finally, the AMH has a significantly differentimplication for asset allocation than does the tradi-tional investment paradigm. Under the stable envi-ronment of assumptions A1–A6, simple assetallocation rules, such as a static 60/40 stock/bondportfolio, might suffice. But if there is significantvolatility of volatility and risk premiums also varyover time, then defining portfolios in terms of theusual asset-based portfolio weights may not bevery useful from a decision-making perspective. Inparticular, a 60 percent asset weight in equities mayyield a volatility of 0.60 0.20 = 12 percent duringnormal times, but during the fourth quarter of 2008,when the CBOE Volatility Index reached 80 per-cent, such an allocation would have yielded a vol-atility of approximately 48 percent. Given thatinvestors are usually more concerned with risk andreward than with the numerical values of theirportfolio weights, the AMH suggests that denomi-nating asset allocations in risk units may be moreuseful and more stable, particularly when assump-tions A1–A6 break down. For example, if an inves-tor is comfortable with an annualized returnvolatility of 10 percent for her entire portfolio, thiscan be the starting point of an asset allocation strat-egy in which a risk budget of 10 percent is allocatedacross several asset classes—say, 5 percent risk toequities, 3 percent risk to bonds, and 1 percent riskto commodities.12 As the volatilities and correla-tions of the underlying assets change over time, theportfolio weights change in tandem to maintainconstant risk weights and portfolio volatility,reducing the potential for fight-or-flight responsesbecause the investor experiences fewer surpriseswith respect to her portfolio’s realized risk levels.13

Specifying risk allocations has the added ben-efit that during periods of heightened volatility,exposures to risky assets will be reduced so as tomaintain comparable levels of volatility; recall thatthese periods are precisely when the madness of

mobs is most likely to emerge, causing the expectedreturns of risky assets to decline owing to flight-to-safety divesting. In the traditional investment par-adigm, it never pays to engage in such “tactical”shifts because market timing has been shown to bevirtually impossible and, therefore, ineffective, andby reducing exposure to risky assets from time totime, a portfolio will forgo the risk premiums asso-ciated with those risky assets. This misleading con-clusion provides the starkest contrast between theEMH and the AMH—if risk premiums and volatil-ities are constant over time, then static portfolioweights may indeed be adequate, but if they varyover time in response to observable market condi-tions, then an adaptive strategy may be superior.

Risk-denominated portfolio weights are onlypart of the solution to the asset allocation problemunder the AMH; the impact of time-varyingexpected returns and correlations of individualasset classes, which may be quite different undervarious market regimes, must also be taken intoaccount. A more integrated approach is to useadaptive statistical estimators for all the relevantparameters of an investor’s environment and con-struct allocations that incorporate estimation error,regime shifts, institutional changes, and more real-istic models of investor preferences and behavior.Such an approach has the added advantage that instable environments, truly adaptive portfolio poli-cies will eventually reduce to traditional static ones,implying little cost in implementing adaptive strat-egies during normal periods and significant bene-fits during market dislocations.

The practical implementation of the AMH isclearly more challenging than the simpler heuris-tics of the traditional investment paradigm. Thesechallenges, however, are considerably less daunt-ing today given the vast improvements in tradingtechnology, automated execution algorithms,lower trading costs, better statistical measures oftime-varying parameters using various online datasources, the greater liquidity of exchange-tradedindex futures and other derivative securities, andbetter-educated investors and portfolio managers.Nevertheless, the increased complexity of today’sinvestment environment is undeniable. It is areflection not just of recent financial crises but of amuch larger and more complex global economy towhich we must learn to adapt by applying moreeffective financial technologies.

ConclusionThere is a well-known parable about five monkswho, blind from birth, encounter an elephant forthe very first time and are later asked to describeit. The monk who felt the elephant’s trunk insists

28 www.cfapubs.org ©2012 CFA Institute

Financial Analysts Journal

that an elephant is just like a snake, the monk whofelt the elephant’s leg claims that an elephant is justlike a tree, and so on. Their individual perspectivesare not wrong, but they each possess an incompleteunderstanding of the animal. Under stable, sta-tionary, and predictable economic conditions, mar-kets generally work well and the EMH serves as areasonably good approximation to reality; undermore dynamic and stochastic environments, theEMH becomes less plausible and behavioral regu-larities seem to emerge. The AMH provides anintegrated and logically consistent framework forreconciling these disparate perspectives and offersseveral practical insights with respect to investingin the current economic climate of uncertainty andmarket turmoil.

Of course, the AMH is not yet as well devel-oped as the EMH, but this is changing as we beginto collect more relevant data for measuring theevolutionary dynamics of financial markets andinvestor behavior across time and circumstances.

The early evidence suggests that the AMH canexplain not only departures from the EMH andbehavioral regularities but also how markets shiftfrom the wisdom of crowds to the madness of mobsand back again.14 By studying the forces behindsuch changes, we can begin to develop more effec-tive models for managing our investments andcome closer to the ultimate goal of efficiently allo-cating scarce resources to support steady economicgrowth while maintaining financial stability.

I thank Tom Brennan, Jerry Chafkin, Jayna Cummings,Arnout Eikeboom, and Bob Merton for helpful commentsand discussion. The views and opinions expressed in thisarticle are those of the author only and do not necessarilyrepresent the views and opinions of AlphaSimplexGroup, MIT, any of their affiliates and employees, or anyof the individuals acknowledged above.

This article qualifies for 0.5 CE credit.

Notes1. For the hazards of “physics envy” in finance, see Lo and

Mueller (2010).2. This term also refers to both economic and regulatory

reforms that were put in place in the wake of the GreatDepression to modulate financial activity, including muchof the U.S. code that now governs the entire financial sys-tem: the Glass–Steagall Act of 1932, the Banking Act of 1933,the Securities Act of 1933, the Securities Exchange Act of1934, the Investment Company Act of 1940, and the Invest-ment Advisers Act of 1940. The Great Modulation shouldnot be confused with the Great Moderation, a term coinedby Stock and Watson (2002) that refers to the 1987–2007period of lower volatility in the U.S. business cycle. The twoconcepts are clearly related.

3. I obtained world population data from the U.S. CensusBureau’s International Data Base. The U.S. Census Bureaureports several sources for world population estimates from10,000 BC to 1950, some of which include lower and upperestimates; I averaged these estimates to yield a single esti-mate for each year in which figures are available. From 1951to 2011, the U.S. Census Bureau provides a unique estimateper year. For further details, see www.census.gov/popula-tion/international/data/idb/worldpopinfo.php.

4. See Lo (2004, 2005) and Brennan and Lo (2011) for moredetailed expositions of the adaptive markets hypothesis.

5. See Schoenemann (2006) for a review of the literature onhuman brain evolution and a comparison of subcompo-nents of the brain across hominid species.

6. See de Becker (1997), Zajonc (1980, 1984), and Lo (2011).7. It is no coincidence that the all-too-familiar pattern of

abruptly increasing correlations of previously uncorrelatedactivities exists in physiological phenomena as well as infinancial time series—fight-or-flight responses can triggerflights to safety.

8. See Lo (2011) for a more detailed discussion of the neuro-scientific perspective on financial crises and Brennan andLo (2011) for an evolutionary framework in which variousbehavioral regularities, such as risk aversion, loss aversion,and probability matching, emerge quite naturally throughthe forces of natural selection.

9. See, for example, Crandall and Graham (1984); Farmer, Lund,Trempel, and Braver (1997); and Cohen and Einav (2003).

10. However, even in this case, the wisdom of crowds sug-gested an important difference between CDOs and othersecurities with identical credit ratings. For example, in anApril 2006 publication by the Financial Times (Senior 2006),Cian O’Carroll, European head of structured products atFortis Investments, explained why CDOs were in such highdemand: “You buy a AA-rated corporate bond, you getpaid LIBOR plus 20 bps; you buy a AA-rated CDO, and youget LIBOR plus 110 bps.” Did investors wonder why CDOswere offering 90 bps of additional yield or where that extrayield might be coming from? It may not have been thedisciples of the EMH who were misled during those frothytimes, but more likely those who were convinced they haddiscovered a free lunch.

11. See, for example, Eldredge and Gould (1972). This referenceto punctuated equilibria is not merely an analogy—it ismeant to apply quite literally to financial institutions andeconomic relationships, which are subject to evolutionaryforces in their own right.

12. The fact that the volatilities of the individual componentsdo not add up to 10 percent is not a typographical error; itis a deliberate reminder that the return volatility of a port-folio is not the sum of the volatilities of the individualcomponents (owing to correlations among the assets as wellas the nonlinearity of the square-root function). Moreover,cash is also an investable asset, but because its nominalreturns have zero volatility, cash investments will not

March/April 2012 www.cfapubs.org 29

Adaptive Markets and the New World Order

appear in any risk budget despite their importance formanaging portfolio volatility. Therefore, the notion of a“risk budget” must be used with caution so as not to mis-lead or confuse investors.

13. This asset allocation strategy may seem like “portfolioinsurance” and related dynamic asset allocation strategies(Black and Perold 1992; Perold and Sharpe 1995), but thereare several important differences. The motivation for risk-denominated asset allocation (RDAA) strategies is the non-stationarity of risk levels due to behavioral responses; port-folio insurance typically assumes stationary returns.Changes in portfolio weights from RDAA strategies aredriven by changes in short-term volatility; changes in port-folio weights from portfolio insurance strategies are drivenby short-term losses. And finally, the amount of time vari-ation in the portfolio weights of RDAA strategies is deter-mined by the volatility of volatility—if volatility is

relatively stable, then RDAA strategy weights will be rela-tively smooth. The time variation in portfolio insurancestrategy weights is determined by the level of volatility—higher volatility will require more frequent portfolio rebal-ancing. Of course, the two types of strategies will likelyyield positively correlated return streams because losses areusually accompanied by increased volatility (Black 1976;Hasanhodzic and Lo 2011); hence, RDAA and portfolioinsurance strategies may be changing risk levels at approx-imately the same times. However, the magnitude and fre-quency of those changes are not the same and are driven bydifferent variables, so the correlation will not be perfect andthe ultimate risk–reward profiles of the two types of strat-egies can be quite different.

14. See, for example, Brennan and Lo (2011, 2012); Getmansky,Lee, and Lo (forthcoming 2012); and Lo (2010, 2011).

ReferencesBaumeister, R., T. Heatherton, and D. Tice. 1994. Losing Con-trol: How and Why People Fail at Self-Regulation. San Diego:Academic Press.

Black, F. 1976. “Studies of Stock Price Volatility Changes.” Pro-ceedings of the 1976 Meeting of the American Statistical Asso-ciation (Business and Economics Section):177–181.

Black, F., and A. Perold. 1992. “Theory of Constant ProportionPortfolio Insurance.” Journal of Economic Dynamics & Control,vol. 16, no. 3–4 (July–October):403–426.

Brennan, T., and A. Lo. 2011. “The Origin of Behavior.” QuarterlyJournal of Finance, vol. 1, no. 1 (March):55–108.

———. 2012. “An Evolutionary Model of Bounded Rationalityand Intelligence.” MIT Laboratory for Financial EngineeringWorking Paper No. 2012–001.

Cohen, A., and L. Einav. 2003. “The Effect of Mandatory SeatBelt Laws on Driving Behavior and Traffic Fatalities.” Review ofEconomics and Statistics, vol. 85, no. 4 (November):828–843.

Crandall, R., and J. Graham. 1984. “Automobile Safety Regula-tion and Offsetting Behavior: Some New Empirical Estimates.”American Economic Review, vol. 74, no. 2 (May):328–331.

de Becker, G. 1997. The Gift of Fear: Survival Signals That ProtectUs from Violence. Boston: Little, Brown and Company.

Eldredge, N., and S. Gould. 1972. “Punctuated Equilibria: AnAlternative to Phyletic Gradualism.” In Models in Paleobiology.Edited by T. Schopf. San Francisco: Freeman, Cooper:82–115.

Farmer, C., A. Lund, R. Trempel, and E. Braver. 1997. “FatalCrashes of Passenger Vehicles before and after Adding AntilockBraking Systems.” Accident Analysis and Prevention, vol. 29, no. 6(November):745–757.

Getmansky, M., P. Lee, and A. Lo. Forthcoming 2012. “HedgeFunds: An Industry in Transition.” Annual Review of FinancialEconomics.

Hasanhodzic, J., and A. Lo. 2011. “Black’s Leverage Effect Is NotDue to Leverage.” Working paper (15 February).

Ibbotson, R. 2011. Ibbotson® SBBI® 2011 Classic Yearbook. Chi-cago: Morningstar, Inc.

Lo, A. 2004. “The Adaptive Markets Hypothesis: Market Effi-ciency from an Evolutionary Perspective.” Journal of PortfolioManagement, vol. 30, no. 5 (30th Anniversary Issue):15–29.

———. 2005. “Reconciling Efficient Markets with BehavioralFinance: The Adaptive Markets Hypothesis.” Journal of Invest-ment Consulting, vol. 7, no. 2 (Winter/Spring):21–44.

———. 2010. Hedge Funds: An Analytic Perspective. Princeton, NJ:Princeton University Press.

———. 2011. “Fear and Greed and Financial Crises: A CognitiveNeurosciences Perspective.” Forthcoming in Handbook on Sys-temic Risk. Edited by J.P. Fouque and J. Langsam. Cambridge,U.K.: Cambridge University Press.

Lo, A., and M. Mueller. 2010. “WARNING: Physics Envy MayBe Hazardous to Your Wealth.” Journal of Investment Manage-ment, vol. 8, no. 2 (Second Quarter):13–63.

Peltzman, S. 1975. “The Effects of Automobile Safety Regula-tion.” Journal of Political Economy, vol. 83, no. 4 (August):677–726.

Perold, A., and W. Sharpe. 1995. “Dynamic Strategies for AssetAllocation.” Financial Analysts Journal, vol. 51, no. 1 (January/February):149–160.

Schoenemann, T. 2006. “Evolution of the Size and FunctionalAreas of the Human Brain.” Annual Review of Anthropology,vol. 35 (October):379–406.

Senior, C. 2006. “CDOs Explode after Growing Interest.” FTMandate (April).

Sobel, R., and T. Nesbit. 2007. “Automobile Safety Regulationand the Incentive to Drive Recklessly: Evidence from NAS-CAR.” Southern Economic Journal, vol. 74, no. 1 (July):71–84.

Stock, J., and M. Watson. 2002. “Has the Business Cycle Changedand Why?” In NBER Macroeconomics Annual. Cambridge, MA:National Bureau of Economic Research.

Zajonc, R. 1980. “Feeling and Thinking: Preferences Need NoInferences.” American Psychologist, vol. 35, no. 2 (February):151–175.

———. 1984. “On the Primacy of Affect.” American Psychologist,vol. 39, no. 2 (February):117–123.

This communication is for information only and is intended for investment service providers or other Professional Clients, Qualified or Institutional Investors. The analyses and opinions referenced herein represent the subjective views of the author as referenced, are as of March 2012 and are subject to change. There can be no assurance that developments will transpire as may be forecasted in this material. This material may not be distributed, published, or reproduced, in whole or in part. Although Natixis Global Asset Management believes the information provided in this material to be reliable, it does not guarantee the accuracy, adequacy or completeness of such information.

In the EU (ex UK): Distributed by NGAM S.A., a Luxembourg management company authorized by the CSSF, or one of its branch offices.

In Switzerland: Provided by NGAM, Switzerland Sàrl.

In the UK: Approved for use by NGAM UK Limited, authorized and regulated by the Financial Services Authority.

In the DIFC: Distributed in and from the DIFC financial district to Professional Clients only by NGAM Middle East, a branch of NGAM UK Limited, which is regulated by the DFSA. Registered office: Office 603 - Level 6, Currency House Tower 2, PO Box 118257, DIFC, Dubai, United Arab Emirates.

In Singapore: Provided by NGAM Singapore (name registration no. 5310272FD), a division of Absolute Asia Asset Management Limited, to Institutional Investors and Accredited Investors for information only. Absolute Asia Asset Management Limited is authorized by the Monetary Authority of Singapore (Company registration No.199801044D) and holds a Capital Markets Services License to provide investment management services in Singapore. Registered office: 8 Eu Tong Sen Street, #23-90 The Central, Singapore 059818.

In Taiwan: Provided by NGAM Securities Investment Consulting (Taipei) Co., Ltd. a Securities Investment Consulting Enterprise, regulated by the Taiwan Financial Supervisory Commission. Registered address: Unit 3, 5/F, No.136, Chung Hsiao East Road, Section 3, Taipei, Taiwan, 106, R.O.C., license number 2009 FSC SICE No. 004, Tel. +886 2 2721 5777.

In Japan: Provided by Natixis Asset Management Japan Co., Ltd., a financial services firm, regulated by the Japanese Financial Services Agency. Registered address: 2-2-3 Uchisaiwaicho, Chiyoda-ku, Tokyo.

In the United States: Furnished by NGAM Distribution, L.P., 399 Boylston St., Boston, MA 02116

Natixis Global Asset Management consists of Natixis Global Asset Management, S.A., NGAM Distribution, L.P., NGAM Advisors, L.P., NGAM International, LLC, NGAM S.A., and NGAM S.A.’s business development units across the globe, each of which is an affiliate of Natixis Global Asset Management, S.A. The affiliated investment managers and distribution companies are each an affiliate of Natixis Global Asset Management, S.A.

This material should not be considered a solicitation to buy or an offer to sell any product or service to any person in any jurisdiction where such activity would be unlawful.

NGAM S.A., 51, avenue J.F. Kennedy, L-1855 Luxembourg, Grand Duchy of Luxembourg. • ngam.natixis.com

Any opinions or forecasts contained herein reflect the subjective judgments and assumptions of the authors only. There can be no assurance that developments will transpire as forecasted and actual results will be different. Other industry analysts and investment personnel, including those within AlphaSimplex, may have different views and assumptions. Portfolio managers may make investment recommendations that are not consistent with this information. The information does not represent or imply the actual or expected performance of any AlphaSimplex product. Accuracy of data is not guaranteed but represents our best judgment and can be derived from a variety of sources. The information is subject to change at any time without notice.

Past performance is no guarantee of future results.

All indexes are unmanaged and do not incur fees. You cannot invest directly in an index.

482655 WP62-0412