active equity management

60 ACTIVE EQUITY MANAGEMENT FOR THE FUTURE FALL 2009

The recent volatility of global markets,not seen since the Great Depressionof the 1930s,will serve as a catalystto further change the way the insti-

tutional investment management industrylabels the broad categories of active equitymanagement.The evolution of techniques anddistinctive elements we refer to as quantitativeand fundamental approaches has been ongoingfor many years.

It is perhaps tempting to generalize.Those labeled as quantitative managers arepainted today with an inappropriately broadbrush.This tendency emerged slowly over the1985–2000 period and accelerated rapidly after2000 in the professional equity arena. Simul-taneously, a myriad of fundamental managersslowly converged with the adoption of quan-titative signals and disciplines. The equitymarket liquidity crises experienced in thesummer of 2007 were an abrupt alert for somemarket participants. Too many assets in acommon strategy ultimately create market fail-ures, albeit sometimes short lived, as in thespirit of dynamic hedging in 1987.

This article considers the future of activeequity strategies. The scope of the analysisincludes equity managers that hold publiclytraded securities and seek to outperform easilyreplicable passive benchmarks on a risk-adjusted basis. Each portfolio holding com-prises a very small percentage of the sharesoutstanding in a security, which renders the

managers price takers,not price setters, insiders,or major holders of private equity.

What will define successful active equitymanagement in the future? How will expertsdescribe successful managers? Following is abrief reminder of several of the more impor-tant elements in the historical backdrop.A likelyincrease in the diversity of approaches acrossthe active investment arena is visible on thehorizon. Despite divergent styles, the varietyof approaches to active equity managementreduces to one common descriptor—infor-mation. Managers differ from each other inthe ways they access and leverage informa-tion in terms of their information sources,information processing, and informationimplementation.

THE SUCCESS FACTOR

We believe future success will build on(and require) new levels of technological effi-ciency for three key asset management buildingblocks. Each building block has a rich historyand at the same time is still evolving.The threeessential building blocks are 1) examininguncommon information sources with moderntechnology;2) evaluating this information withmodern portfolio theory, practice, and securityvaluation;and 3) extracting valued-added resultsby processing information with expert systemsto assist investor decision making in forming,controlling, and implementing portfolios.

Active Equity Managementfor the FutureERIC H. SORENSEN

ERIC H. SORENSEN

is the president and CEO ofPanAgora Asset Manage-ment in Boston, [email protected]

JPM-SORENSEN:Layout 1 10/23/09 10:33 AM Page 60

The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

The active management winners of tomorrow willhave passion for these building blocks and will under-stand how to evolve them on a proprietary basis. In thefuture, we will leave behind simple categorical language,such as stock-picking fundamental insight and four-factor quantmodel.Catchy yet misleading phrases and fads, such as fun-damental indices and computer-driven algorithms, willalso come and go, as will the next batch of superficialmarketing-driven and noise-creating buzz.

For the near term, institutions will continue to clumpmanagers into arbitrary buckets—fundamental, quantita-tive, enhanced, hedge fund, stock pickers, price-driventechnicians, and so on—because this practice makes cat-egorization easier. Regardless of the label, the commongoal is clear—to win. In the sometimes chaotic world ofstock price bursts (busts), there is one unique stock everyday (or trading interval)—the one with precisely medianperformance. For any investment interval, the median-performance stock stands as unique in that it separates thestocks above (i.e., the winners) from those below (i.e., thelosers).Return dispersion is always an element of the con-test and, ceteris paribus, the more the better. Better allo-cation to the winners and away from the losers determinessuccess. The game will continue to be worth winning.However, will it also be worth playing?1

Qian,Hua, and Sorensen [2007] argued that in manyinstances the fundamental principles that guide quantita-tive modeling are no different from the principles that guidefundamental research and management; the distinctionbetween the two is that “quantitative lies broadly in per-fecting the comprehensive portfolio system, whereas funda-mental management lies in deeply comprehending the perfectstock” (p. 7). One attribute of each will mark success forboth—creative subjectivity.Yes, subjectivity, but only driven bytrue creativity. As managers, we are often asked: “What areyour differentiators?”Our first answer should be:“We think.We innovate.”2 Strategists get to think. Fundamentalists getto think, as in “we think this stock will outperform.”

Historically,much of the “mythology” surroundingthe generic quant process simply paints over logical cre-ativity. Among the list of assertions that compose quanti-tative mythology, the number one myth is: “All it takes is acomputer and a math nerd.” Just like the dejected cavemanof the GEICO commercials: “Even a caveman can do it!”

Consider quant myth number two:“They all do thesame thing, and if they all do it, it won’t work.”But whatif a manager has the creativity to bring innovative and

evolving approaches and techniques to bear on the invest-ment process? All managers use models. Fundamentalstock analysts use earnings models based on cash flowconcepts and accounting inputs. Similarly, quantitativeskill with creativity enlarges into comprehensive expertsystems utilizing models at several levels. In both cases,true experience and expertise require judgment, priors,conditioning, and nonlinear thinking.Subjectivity,on theone hand, and a disciplined rules-based process, on theother, should not dichotomize investment philosophiesthat are successful in the future. Managers are not all thesame,which makes the game worth playing for those thatcombine creativity with modern information processingtools.3

FUNDAMENTAL PROCESSES: BEHAVIORPROBLEMS

The floor plan of a fundamental investment shopconjures up a vision of rows of analysts with green eye-shades pouring over written material in order to “cover”lists of stocks. The material varies, but typically includesfinancial statements, notes on face-to-face interrogationof corporate officials, and various heuristic “models” ofthought processes to determine relative security value.Two prime advantages of the fundamental approach arethe riches of 1) depth of analysis and 2) human insight.Two disadvantages of the approach are 1) lack of breadth(limited capacity to cover enough securities) and 2) thepotential for human behavioral biases.

Analysts often behave and even compete in waysthat detract from the goal of objective value-added invest-ment advice. Over 30 years ago, Treynor [1978] aptlycharacterized one behavioral problem that can emerge inan investment management organization. Securities(research) analysts have to compete to influence the port-folio makeup and their opinions often take the form ofbuy recommendations in order to accelerate the decision-making process.Given these biases,“analysts who get rec-ognized and rewarded are those who 1) succeed inpersuading portfolio managers to buy … and 2) whoserecommended securities appreciate …,a process that trans-forms various analysts … into rivals” (Treynor [1978,p.430]).Of course,many behavioral biases can creep intoan analyst’s mind. Examples of the pitfalls of free subjec-tivity, regardless of the objective data, include “falling inlove with a stock” or “herding.”

FALL 2009 THE JOURNAL OF PORTFOLIO MANAGEMENT 61


The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

Over the past 35 years, large teams of sell-side andbuy-side fundamental analysts have come and gone.4 Asif deciphering the accounting numbers was not a largeenough challenge,understanding psychology and assessingmanagement behavior have also become necessary skillsin fundamental analysis.One problem is prevalent—cor-porate management often illusively articulates the funda-mentals.As one analyst puts it, “I bring in a psychologist’scouch more than a spreadsheet.” More than one con-sulting firm has begun to conduct training that equipsanalysts in “detecting deception.”The challenge is to iden-tify verbal and nonverbal behavior that signals corporatemanagement’s attempts to obscure and/or misinform.5

Management more often than not provides informationthat will obscure, at least until the corporate reports offermore clarity.

QUANTITATIVE PROCESSES:MATH PROBLEMS

Just as with judgments on fundamental approaches,observers have traditionally associated both strengths andweaknesses with quantitative methods.The prime advan-tages of quantitative methods are considered to be 1) breadthof analysis and 2) discipline. The disadvantages are con-sidered to be 1) modeling that is overly dependent onpast relationships and 2) perhaps excessive rigidity inimplementation as market regimes shift.

Quantitative management is sometimes consideredexclusively an exercise in risk analysis. This is certainlypart of it, encompassing sources of volatility, tracking error,and so on. In the past 30 years,however,quantitative man-agers have devised sometimes simple, and sometimes morecomplex, mathematical and statistical methods (models)to forecast the future returns on large and broad universesof stocks.

Models that predict the future often rely on histor-ical analyses to backtest theory.On the one hand, this pro-duces a more scientific, data-rational result.On the otherhand, it may lead to predictions that fail when the secu-rities world becomes dynamic and/or volatile.Moreover,any observed success of simple models may make mythnumber two, discussed earlier, a reality. If more playersreplicate similar indicators and have common models forstock selection,overcapacity can become a problem.Someactive managers experienced extraordinary volatility inthe summer of 2007 when some leveraged funds wereforced to liquidate.The liquidation brought abrupt selling

of some quantitative equity positions. Clearly, breadthfrom quantitative signals has a potential downside. In con-trast to fundamental managers who can manage a rela-tively narrow and/or specific portfolio that is somewhatunique to their proprietary research insights, some quantsare exposed to unpredictable regime shifts because theyhold positions that are common to other portfolios.

So,who started this quant stuff anyway? In the early1920s, Knight [1921], who was at the University ofChicago, defined risk as quantifiable uncertainty. Later,VonNeumann and Morgenstern [1944] presented a rationalfoundation for decision making under risk by identifyinga number of expected-utility rules.Markowitz [1952] ush-ered in an analytical and normative paradigm for modernportfolio construction with his general framework foroptimal exchanges of expected return (mean) for expectedrisk (covariance). All of this work led to formal equilib-rium propositions that established theoretical linearitybetween expected return and risk (the market price of risk).Beta was born.

Parallel with the science, the financial communitybegan to focus on securities analysis technique.The Invest-ment Analysts Society of Chicago, formed in 1925, wasfollowed by the establishment of the New York Societyof Securities Analysts in 1937.The latter organization wasmotivated by Ben Graham and others who also launchedthe Financial Analysts Journal in 1945.

Ben Graham, thought by many practitioners to bethe “father of securities analysis,” threw in an active mea-sure of value to drive the stock selection process.His man-aged portfolios were broad based and, therefore, morelikely to resemble those of a modern quantitative practi-tioner than those of a rifle-shooting, narrowly focusedfundamental stock picker.

PRICE EFFICIENCIES AND EARNINGS:COMMON REALITIES FOR ALL

In order to understand the challenges for active man-agement and the intense competition to earn incrementalreturns, two key aspects of stock investing must be appre-ciated. First is the importance of market efficiency—predicting future prices is highly competitive,and it shouldbe, because the rewards are high and the barriers to entryare relatively low. Second is the importance of informa-tion about future corporate earnings—predicting futureearnings and cash flows can be the key to success.



The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

The second half of the twentieth century witnessedthe explosion of technology on many fronts. Moderninvestment research and practice grew with the combinedinfluences of modern economic modeling, advances incomputer technology, and publishing of vast amounts ofacademic work in empirical finance.The early empiricalwork emanated from the behavior of stock prices.Manyacademics made reference to Brownian motion, randomwalk, stochastic price changes, martingale efficiency, andthe like.6 A lay interpretation of these studies reduces tosomething like “prices have no memory”; if so, activitiessuch as technical analysis should not produce abnormalreturns.

Fama [1970] did the industry (and academic com-munity) a great service by summarizing stock marketpricing efficiencies in a pragmatic context. The financialand academic community finally had a straightforwarddefinition and a testable hypothesis. In Fama’s words, stockpricing is efficient if it precludes making abnormal profits(i.e.,more profits than are justified by the investment risks)conditioned on some type of available information, suchas past prices, fundamental data, insider information, andso forth. It stands to reason that if being a winner in stockinvesting is lucrative, multitudes of players will devoteenormous energy and ingenuity to succeed. Volumes ofdirect academic study and formal research are availableon this subject spanning the past 40 years.7

The value proposition of the professional moneymanager must rest heavily on the belief in a defined processthat can overcome an efficient-market hurdle or obstacleand that essentially violates the notion of an efficientmarket. Despite the logical appeal (and early empiricalevidence) that supported market efficiency,hundreds (andperhaps thousands) of institutions had success with themission of disproving efficiencies for profit. For example,consider the acceleration in recent years of so-called high-frequency trading. Perhaps more than half of today’svolume in global equity is high frequency (extremely shortholding periods). Armed with high-speed computers,continuous market data feeds,proximity inside the marketmicrostructure, and creative price-pattern algorithms, thenew world of computer-driven trading has revolution-ized the once visual chart mentality of the old-schoolmarket technician. One might argue that the high-volume, intraday trading by dealers (and hedge fundplayers) today does not create any true information, butrather it creates noise and, worse yet, creates instabilityand detracts from long-run equilibrium pricing. But, it

evidently makes money for some (perhaps many) playersand justifies the game.8

At the other end of the equilibrium spectrum, stockprice behavior related to corporate earnings changes islikely to remain the single, most important emphasis infundamental stock selection. It is also modeled in mostquantitative approaches. Graham et al. [1962] were earlyarticulators of this axiom: “The most important singlefactor determining a stock’s value is now held to be theindicated average future earning power, i.e., the estimatedaverage earnings for a future span of years” (p. 28). Theconcepts of growth in cash flow and of trend in prof-itability have come under such scrutiny by analysts,quants,and portfolio managers that they should reside at the epi-center of market efficiency. Indeed,Graham’s main focuswas to carefully consider the limit (value) that an investorshould pay for future earnings. In the world of public cor-porate data analysis, all roads lead to knowing next peri-od’s (year’s) earnings.This year’s stock price winners (losers)are next year’s earnings winners (losers). But the goal ismore than forecasting big or small earnings growth moves;it is forecasting ex post growth relative to ex ante expec-tations.This observation is tantamount to being axiomatic.

It is little wonder that earnings trends, earnings fun-damentals, and consensus earnings expectations analysesrepresent the cornerstone of fundamental and quantita-tive managers alike. Myriad studies fill finance and eco-nomics sections of libraries with testimonials.

One of the earliest, as well as most direct, graphicempirical analysis of knowing future earnings is simple andelegant. Niederhoffer and Regan [1972] calculated astraightforward metric to compare the top-performingstocks and the bottom-performing stocks in 1970. Thewinners were up an average of 48% and the losers weredown an average of –57%. The differentiator was notex ante forecasted earnings—the losers had twice themagnitude of forecasted growth than the winners. Thedifferentiator was what actually happened ex post versusexpectations; that is, the great stock performers reportedearnings that, on average, grew three times faster thanexpected,whereas the losers reported big losses comparedto prior expectations of positive growth of 15%.

Hundreds of research papers attest, and numerousearnings calculations evidence, broad exposure to futureearnings across the investment practice landscape.We haveearnings-surprise factors, earnings-revision metrics, earn-ings-momentum measures, and so on.On the one hand,quants have drawn criticism for data-mining databases that



The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

aggregate analysts’ consensus expectations. On the otherhand, fundamental securities analysts have sometimesbecome too concerned with the forecasts of other ana-lysts and avoid risk taking in their predictions by huggingthe consensus.

A few general results about corporate earnings needbe kept in mind. First, growth rates are often not as sus-tainable as one might hope. Forecasting earnings is diffi-cult enough for CEOs and CFOs of a company, and evenmore so for analysts outside the firm. Second, the propen-sity exists for earnings and cash flow trends to mean revert,which means that forecasts are often biased (i.e., too highif trending up and too low if trending down.) This wasfirst observed by Little and Rayner [1962] in U.K. firms50 years ago.

Third, forecasting the consensus earnings expecta-tion, and indeed any unexpected deviations from thatexpectation, is a natural determinant of the stock priceevolution process. Fourth, the magnitude of stock priceswings is a leveraged play on perceived fundamentals ofthe firm; that is, the prospect of risky earnings means fun-damental volatility on steroids.

VALUATION, MISPRICING,AND THE FIRST FACTOR MODEL

Early in the evolution of quantitative modeling, val-uation and pricing disparities appeared to be the hallmarkof disciplined quantitative investing. Quants could screenlarge databases for “cheap” stocks and presumably playagainst the biased behavior of the traders and fundamentalportfolio managers who accounted for the bulk of tradingvolume and volatility in single names. Mean reversion infundamentals (and stock prices) dictated stock opportu-nities created by overly optimistic (pessimistic) marketparticipants. Formal methods of achieving “low-priced”portfolios put academic valuation concepts into practice;for example, John Burr Williams authored The Theory ofInvestment Value in 1938, and from the 1960s through the1980s quantitative practitioners based their dividend dis-count models on such present-value-of-future-earningsconcepts. As noted earlier, Graham was likely the first toformally invest this way.

But what constitutes “cheapness?” Don’t some stocksdeserve higher prices for the general expectation of higherand/or safer future growth? That question was posed byWhitbeck and Kisor [1963] over 45 years ago when theypublished (in our opinion) the first multi-factor model.

They estimated a rather simple model of a P/E ratio byfitting a linear cross-sectional regression of normalizedP/E ratios as a function of growth rate, dividend payout,and risk (standard deviation of earnings). This simplescheme effectively establishes a normative benchmark tocalibrate the attractiveness of a stock. Whitbeck and Kisor[1963] did ascribe the important role of the stock analyst:“the task of projection …, to assay the future prospects ofindividual firms … is not an easy one … for it involves aunique combination of analytical talent, sustained effort,and in some cases, intellectual wizardry” (p. 61).

But do we have to have the analysts? After all, eachmay have limited capacity—perhaps, 20 to 30 stocks—not to mention that a combination of analytical talentand intellectual wizardry probably costs a lot. Factor modelsevolved as a means to a more efficient way of identifyingattractive stocks.

Arriving at Salomon Brothers from academia in 1986,we discovered “the first audited factor model” by Bowerand Bower [1984], professors at the Dartmouth Schoolof Business, who conducted a performance analysis of afactor model that had been created and used at SalomonBrothers. The fundamental utility analyst team had esti-mated and invested in a process that utilized several vari-ables to factor analyze a universe of 93 electric utility stocks.The inputs were ROE, dividend yield, quality ranking,and regulatory environment ranking. In effect, Salomon’ssenior electric utility analyst had crossed over into theworld of quantitative factor modeling. During the testperiod from 1977 to 1981, portfolios of undervaluedstocks, as determined by the model, outperformed port-folios of overvalued stocks, ceteris paribus. This was prob-ably the first published performance evaluation of a livequant model with real dollars invested.

As head of Quantitative Equity Research at SalomonBrothers, early on we developed and produced multifactormodels for large-cap and small-cap U.S. stocks. Severalpension funds used these models in implementing quan-titative portfolios that would otherwise have been allocatedto index funds (or run with fundamental analysts). Thebasic approach was called the E Model and representedthe level, risk, and projection of earnings per share foreach company. The model was rather simple with a one-size-fits-all approach, except for the separation betweenlarge-cap S&P 500-like stocks from Russell 2000-likestocks. We used portfolio optimization to tilt the activestock exposures toward the high-ranking securities.9


JPM-SORENSEN:Layout 1 10/19/09 3:45 PM Page 64

The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

Today’s quantitative ranking systems are sometimesmuch more sophisticated than the 1980s E Model. Overthe years, however, extended periods have existed in whichsome of the basic fundamental ingredients tended toenhance portfolio returns. These ingredients measuredstock factors or attributes associated with alpha and weremostly consistent with the preferences of good funda-mental investors—buy good quality, buy at attractive prices,and buy when consensus opinions are improving, asreflected in price and/or earnings forecasts. In a recentNomura research publication, Mezrich [2009] again doc-umented the long-term positive contribution to perfor-mance of a simple blend of value and price momentum.His “cheap loser” portfolio consisted of highest book-to-price (B/P) and lowest price-momentum deciles in theRussell 1000, rebalanced monthly. This portfolio roaredahead during 2009, a major reversal considering that highmomentum had dominated the scene for most of the lastdecade.

Practitioners have made many improvements inmodeling and implementation over the past two decades,including better inputs, better factor specification (i.e.,nonlinear), better optimal-weighting algorithms (i.e., con-textual modeling), and so on. However, we are alwaysfaced with the likelihood that additional managers willinvoke increasingly similar techniques.

In contrast to advancing the search for new ingre-dients to add to quantitative approaches, some managerschoose to stick to simple criteria. Although these man-agers may represent that they are conducting incrementalresearch, they have found that adhering to a basic com-bination of price momentum and value has been ade-quate to run their portfolios. Yet other managers haveworked to create techniques to dynamically weight the rel-ative importance of factor categories over an economic(investment) cycle.

WHEN WE ALL LOOK THE SAME

In the history of financial markets, when we all look(behave) the same, we ultimately become quite ugly.Opportunity to make money attracts a crowd. To gener-alize, the progression has four phases and has been repeatedmany times in the history of capital markets: 1) innova-tive risk takers create, 2) copiers converge on the scene andcrowd in, 3) the masses stretch capacity, and 4) when thestrategy is strained, collective capitulation brings demise.

Phases 2 and 3 can last much longer and be muchmore rewarding to the mediocrity than one mightimagine. The latest example is the credit risk bubblethat slowly built up in bank, insurance, dealer, and hedgefund portfolios. The specific strategy in question differs,but the behavioral drivers are always the same. In aninterview in the Financial Times, on July 9, 2007 oneprominent bank CEO said in an uncannily timely pre-lude to Phase 4, “But as long as the music is playingyou’ve got to get up and dance.” And dance we do. Theattraction of the music has an insidious onset like a jetpilot who unknowingly experiences hypoxia (lack ofoxygen). Two elements are always present: 1) greed moti-vates and 2) leverage facilitates.

Other examples in recent history of collective capit-ulation include the 1987 stock market crash and the debtand currency crisis of 1997 and 1998. The 1987 portfolioinsurance (dynamic hedging) debacle was a simple strategy;use one signal (the level of the S&P 500) to trade oneinstrument (S&P 500 futures), and do it quickly. As volumegrew rapidly, capacity was reached in October 1987, cul-minating in a 22% decline in the market, literally overnight.

In 1998, the Long-Term Capital Management hedgefund (and innumerable dealer copycats) overcrowded andoverleveraged the supposedly hedged trades in emergingmarket debt. Only after the crisis in the fall of 1998 didthe world discover how excessive the notional exposureto a common strategy stretched the market’s capacity. Analternative title to Lowenstein’s [2001] excellent book onthe subject, When Genius Failed …, would be When GreedSucceeded … .

As stated earlier, quantitative equity once had suchgrowing success that it was sufficient to create a type ofmarket failure. The summer of 2007 was punctuated withvolatility in the equity markets related to the deleveragingof some rather large long–short equity funds for which bothsides of the portfolio had been populated using quantitativeattributes. As these funds liquated to generate the cash neededby investment management firms, many long-only U.S.equity managers with large dependence on traditional quantfactors (but who are still considered fundamental managers)suffered large daily swings in performance. Such swift capit-ulation helped redefine the meaning of product capacity.Creeping convergence by the many on a capacity-con-strained winning strategy will result in declining effectivecapacity for any one manager, even the innovative creators.

The creative early innovators of winning strategiesmake the most profit as others copy (converge), but stand


JPM-SORENSEN:Layout 1 10/19/09 3:45 PM 5

The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

to lose with the masses if they stay too long. Add leverageand greed to the equation and the cycle is exacerbated.This is precisely the scenario of the most recent (and per-haps most violent) of such phenomena—the global mort-gage default and insurance/financial intermediationcollapse of 2007, 2008, and beyond.10

THE FUTURE: SHINING THE LIGHTIN THE RIGHT SPOT

Today the realm of successful quantitative equityportfolio investing is moving ahead in some new ways. The“light” used to see the way is improving. The “spot” towhich it is directed has increased focus. To be effective,quantitative management must pursue the elements thatwork best among the fundamental techniques and con-tinually improve the quantitative methods that consis-tently add value. Recall the advantages of fundamentalanalysis: 1) depth of information leading to unique port-folios, theoretically creating a large set of non-overlap-ping portfolios that win (and lose) each horizon period,and 2) insight into market dynamics to prevent model-specification errors or poor timing.

Effective quantitative management will also requiremuch more sophisticated approaches to understanding,modeling, and taking advantage of high-frequency (andreal-time) stock price dependencies. Practitioners will beincreasingly competing in a world of high-frequencytraders—who create volatility. The two sides of this newvolatility are 1) alpha-destructive trading costs and 2) alpha-enhancing opportunity costs.

With today’s technology, a deeper focus and bettervision are possible, and many differentiators are availableto help make value-added investment decisions. We nowknow more about how to optimally combine alpha sources(Sorensen, Hua, and Qian [2004]), and we have createdinnovative approaches to measure the importance of alphasources based on their context. Contextual modeling pro-vides deeper fundamental insight by formally ascribingthe importance of a set of model inputs based on thehomogeneity of a set of stocks within a group accordingto risk fundamentals and characteristics (Sorensen et al.[2005]).

The successful manager should always search formore insightful data. With access to the Internet and othermediums, we can find uncommon databases that refine ourcalibration of future earnings and risks for companieswithout sacrificing breadth. Today, we have access to

industry databases, both micro and macro. We can testthese data for information content with much greaterefficiency than ever before.

We can craft new business models that better dif-ferentiate between winners and losers within an industryor sector. Consider the following hypothetical example.After in-depth interviews of airline industry experts,including securities analysts and corporate officials, webelieve the following is important in valuing an airline—the number of lost bags per month is inversely related tofuture earnings and, therefore, to stock performance. Thisrelationship can be thought of as a risk factor, qualityfactor, cost factor, or management-efficiency factor. Wethen locate data sources that allow for testing the vari-able’s importance with respect to past stock price perfor-mance. We also identify proprietary data sources that canbe obtained in an efficient and high-tech manner. Withinweeks of the new insight into airline industry perfor-mance, we can include this new ingredient, optimallyweighted as part of a logical, dynamic portfolio manage-ment system.

Many such examples are available if analysts dare toinnovate. On the one hand, fundamental analysts may pos-sess the insight, but lack the efficiency and scientific rigorin assigning it the appropriate level of importance. Onthe other hand, quantitative managers might miss theinsight altogether. An early anecdote of true insight—butone that was inefficiently deployed—was a set of PacificNorthwest forest products analyses in the early 1970s.Inventory control of lumber was critical to manufacturersin the boom–bust interest rate cycles following WorldWar II. So, one analyst employed a helicopter to fly overlumber yards to confirm the veracity of managementinformation, but problem weather and geographic dis-tance between inventory sites made the execution of thisplan quite difficult. Moreover, any insight was perhapsonly a small part of the puzzle. For example, the flyoversgave no information about upstream suppliers and down-stream customers or who had pricing power and who hadcost pressures. Today, modern quantitative systems andelectronic access to key data help to resolve complex prob-lems in a more tractable, comprehensive, and efficientlymodeled way.

New research with fundamental depth and focuswill be the hallmark of tomorrow’s successful active equitymanagers. We will see the evolution of quantitative equityinvesting marked by small, diversified, and additive ele-ments of innovative information analysis. Much of this



The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

information will be in sync with modern economic anddata-rational thought. It will also be essential to mod-ernize the use of high-frequency price patterns. The goalis to efficiently transform information into seamless fore-casts of the winners and losers across the global stock mar-kets, and also add value in the implementation. In the end,the technological and informational innovators will simplybe labeled “good investors.”

ENDNOTES

1In order for the game to be worth playing, the profes-sional equity manager must embrace and apply the three essen-tial building blocks. If the manager is unwilling to do so, itwould be wise to pursue an alternative career. Realistic careercounseling might include the following: 1) without an under-girding of sound theory, join the journalistic media; 2) withouta love for technological advance, join a library staff assigned toliterature; and 3) without a desire for speed, efficiency, and accu-racy, join a leisure cruise ship.

2We often ask prospective clients if they would like toknow what we think about the markets. On more than oneoccasion, the response has been roughly, “You’re quants. Youdon’t have to think.”

3In Michael Korda’s [2009] recent book, With Wings LikeEagles, about the World War II air war between Britain andGermany, he recounts that the RAF and Luftwaffe fighter pilotsand their respective aircraft (Spitfires versus Messerschmitts)were more or less equally matched in the summer of 1940. Thedifference was that Hugh Dowding, the meticulous and visionarycommander of the RAF, implemented a remarkable networkcomprising air crew, command posts, highly integrated civilianground teams, and the latest (yet untested) technology—radar.Dowding built a comprehensive “expert system” to mimic andanticipate the Luftwaffe’s movements, and it worked.

4The author has extensive first-hand work experiencewith fundamental research as a researcher, professor of securi-ties analyses, and most importantly living in large organizationswith major commitments to fundamental analyses on both thesell side and the buy side.

5One such firm that coaches analyst groups is BusinessIntelligence Advisors, Inc. Their website at www.biadvisors.comstates that “BIA’s proprietary detection of deception, strategicinterviewing, and investment intelligence collection modelsenable our clients to obtain, evaluate, and act on informationessential to their investment processes. BIA’s unique servicesare delivered by world-class intelligence experts, who provideindependent research, analysis, consulting, and training.”

6For example, see Cootner [1964], Osborne [1962], andMandelbrot [1966].

7Our summary review of 40 years of empirical study ofthe efficient market hypothesis (EMH) loosely comprises fourphases: Phase 1 when it was impossible to publish if theresearcher’s conclusion disproved or violated EMH; Phase 2when it was possible to publish on the topic of market ineffi-ciency if the researcher’s finding was labeled as an “anomaly”and probably would not be repeatable in the future; Phase 3when it was possible to publish a refutation of EMH if itincluded roughly 30 pages of statistical support in a 40-pagefinance journal; and Phase 4 when the research addressed “why”EMH did not hold and the explanation was behavioral finance.

8For good or bad, many of today’s players have moreregard for creating personal wealth than for seeking the truth.

9One specific public fund ran the E Model tilt for sev-eral years with consultant-audited performance metrics duringthe 1987–1995 period. The portfolios were internally referredto as the S&P 500 Tilt fund and the Russell 2000 Tilt Fund,for large- and small-cap stocks, respectively.

10Interestingly, this debacle was also assisted by overlypowerful legislators in the U.S. Congress and certain complicitregulators. They effectively influenced artificially cheap supply(sources) of financial intermediation incentives toward non-productive, consumer-oriented uses. A number of public offi-cials in positions of authority over the financial systemengineered an unnatural growth in both supply and demand forcredit risk centered on consumer housing that quickly spreadto other sectors.

REFERENCES

Bower, R.S., and D.H. Bower. “The Salomon Brothers Elec-tric Utility Model: Another Challenge to Market Efficiency.”Financial Analysts Journal, Vol. 40, No. 5 (September/October1984), pp. 57–67.

Cootner, P.H. The Random Character of Stock Market Prices. Cam-bridge, MA: MIT Press, 1964.

Fama, E. “Efficient Capital Markets: A Review of Theory andEmpirical Work.” Journal of Finance, Vol. 25, No. 2 (May 1970),pp. 383–417.

Graham, B., D.L. Dodd, S. Cottle, and C. Tatham. SecuritiesAnalyses: Principles and Techniques. Columbus, OH: McGraw-Hill,1962.

Knight, F.H. Risk, Uncertainty, and Profit. Wilmington, MA:Houghton Mifflin Company, 1921.

Korda, M. With Wings Like Eagles. New York, NY: Harper/HarperCollins, 2009.



The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

Little, I.M.D., and A.C. Rayner. Higgledy Piggledy Growth Again.Oxford, U.K.: Blackwell, 1962.

Lowenstein, R. When Genius Failed: The Rise and Fall of Long-Term Capital Management. New York, NY: Random House,2001.

Mandelbrot, B. “Forecasts of Future Prices, Unbiased Mar-kets, and Martingale Models.” Journal of Business, 39 (1966),pp. 242–255.

Markowitz, H.M. “Portfolio Selection.” Journal of Finance,Vol. 7, No. 1 (1952), pp. 77–91.

Mezrich, J. “Round Trip for Risk: Is It Over?” Nomura Secu-rities International, May 12, 2009.

Niederhoffer, V., and P.J. Regan. “Earnings Changes, Analysts’Forecasts, and Stock Prices.” Financial Analysts Journal, Vol. 28,No. 3 (May/June 1972), pp. 65–71.

Osborne, M.F.M. “Periodic Structure in the Brownian Motionof Stock Prices.” Operations Research, Vol. 10, No. 3 (1962),pp. 345–379.

Qian, E., R. Hua, and E. Sorensen. Quantitative Equity PortfolioManagement: Modern Techniques and Applications. New York, NY:Chapman & Hall, 2007.

Sorensen, E.H., R. Hua, and E. Qian. “Contextual Fundamen-tals, Models, and Active Management.” Journal of Portfolio Man-agement, Vol. 32, No. 1 (Fall 2005), pp. 23–36.

Sorensen, E.H., R. Hua, E. Qian, and R. Schoen. “MultipleAlpha Sources and Active Management.” Journal of Portfolio Man-agement, Vol. 31, No. 2 (Winter 2004), pp. 39–45.

Treynor, J. “The Coming Revolution in Investment Man-agement.” In J. Lorie and R. Brealey, eds., Modern Developmentsin Investment Management. Fort Worth, TX: Dryden Press, 1978,pp. 424–431.

Von Neumann, J., and O. Morgenstern. Theory of Games andEconomic Behavior. Princeton, NJ: Princeton UniversityPress/Princeton Classic Editions, 1944.

Whitbeck, V.S., and M. Kisor, Jr. “A New Tool in InvestmentDecision Making.” Financial Analysts Journal, Vol. 19, No. 3(1963), pp. 55–62.

Williams, J.B. The Theory of Investment Value. Amsterdam, TheNetherlands: North Holland Publishing Company, 1938.

To order reprints of this article, please contact Dewey Palmieri [email protected] or 212-224-3675.



The

Jou

rnal

of

Port

folio

Man

agem

ent 2

009.

36.1

:60-

68. D

ownl

oade

d fr

om w

ww

.iijo

urna

ls.c

om b

y B

ill R

empe

l on

11/1

9/09

.It

is il

lega

l to

mak

e un

auth

oriz

ed c

opie

s of

this

art

icle

, for

war

d to

an

unau

thor

ized

use

r or

to p

ost e

lect

roni

cally

with

out P

ublis

her

perm

issi

on.

active equity management

Documents