is thatmorecanbesqueezedfromanopportunity.Imagine
a widelyknownmodelidentifiesIBMas1%undervalued.
Withoutunderstandingtransactioncosts,a typicalcompany
mighttradeonly1,000shares,lestit risktoomuchslippage
andpushpricesabovethe1%spreadit’sseekingtocapture.
A companythatknows,withperhaps80%probability,that
infact5,000sharescanbesafelyboughtwithoutmoving
themarkettohigherpricescanmakea biggerbet.Manyin
theindustrysayRenaissancehasthemostadvancedunder-
standingoftransactioncosts,andthat’sonesecrettoits
unequaledtrackrecord.
Tosqueezetransactioncostsfurther,somequantmanagers
buildtheirownhigh-frequencytradingoperations,inwhich
theycanactasmarketmakers,earningmoneybymatching
buyersandsellers.Butjustasimportant,runningtheseplat-
formshelpsthemgaindeeperinsightsintothebehaviorofthe
market.It’sakintoWarrenBuffetthavinghisowntraderson
theflooroftheNewYorkStockExchangeratherthanusing
a WallStreetbrokerage.Buffett’sownpeoplemighttellhim
thingsaboutthemoodonthefloorthatthebrokers’wouldn’t.
Anotherworkaroundforquantmanagersstrugglingwith
marketdataistofindotherkindsofinformationtomine.
They’refeedingintotheircomputerseverythingfromsatellite
photosofparkinglotstosocialmediafeeds.“Alternativedata
mightbemorehelpfultofirmsthatarelessskilledatwring-
ingsignaloutofclassicdatasets,”saysJonMcAuliffe,a pro-
fessorattheUniversityofCaliforniaatBerkeleyandthechief
investmentofficeratVoleonCapitalManagementLP.Trouble
is, suchdatagetseasierandeasiertofind,soit maynotpro-
vide anedgeforlong.(BloombergLP,whichownsBloomberg
Businessweek, providesclientswithaccesstoalternativedata.)
Giventhecomplexityofnoisydata,③mostcompaniestry
to keepthemodelsassimpleaspossible.NickPatterson,who
spenta decadeasa researcheratRenaissance,says,“One
tool thatRenaissanceusesis linearregression,whicha high
schoolstudentcouldunderstand”(OK,a particularlysmart
high schoolstudent;it’saboutfindingtherelationshipbetween
two variables).Headds:“It’ssimple,buteffectiveif youknow
how toavoidmistakesjustwaitingtobemade.”Legendholds
that atonetimethecrownjewelsofthefirmcouldbewritten
downona single8.5-by-11-inchsheetofpaper.A
s muchashedgefundsareusingcomputersfordata
crunchingandpatternrecognition,findingnew
marketsignalsis stilla humanendeavor.Elitequan-
titativemanagersemployhugestaffs—sometimesin
the hundreds—andshowupatmachinelearningconferences
to recruitfreshPh.D.s.
Tobuilda trulyautonomousinvestingsystem—onein
whichthecomputeritselfisthinkingaboutsignalsand
strategiestotry—researcherswilllikelyneedtocrackthe
problemofcausality.Thatmeansnotonlynoticingthat,
for instance,a riseina particularstockisoftenaccompa-
nied bya bumpininterestrates,butalsobeingabletocome
up witha reason for it. Humansaregoodatthiskindofthinking,butAIhasonlystartedtomakeprogress.
Anothermethod,knownasdeeplearning,hasdrivenrecent
advances in AI, such as image recognition and speech trans-
lation. Researchers are tying to bring it to finance, though its
use is still limited. Zack Lipton, a professor at Carnegie Mellon,
has co-authored a paper showing one possible approach. It
addresses the noise problem by predicting not stock prices,
but the changes in company fundamentals—such as revenue
or profit margins—that ultimately drive returns.
The adversarial nature of trading means that most develop-
ments remain shrouded in secrecy. That makes high-quality
AI scientists hard to recruit. Scientists like to publish and col-
laborate. “We love discovering new things about markets and
have a great community of people within the firm that we’re
abletoshareresultswith,butunfortunatelywecan’tcommu-
nicatethemtoa wideraudience,”saysPeteMuller,thefounder
ofPDTPartners LLC and a pioneer in the field.
The prospect of searching for ghostly signals that eventu-
ally disappear can also dissuade some people from working
in finance. “In my mind, a top researcher would need a two-
to five-times salary multiple to completely forgo the ability to
publish and make the lifestyle trade-offs necessary to work
in finance,” Lipton says. Still, there’s the lure of a tough prob-
lem, combined with the chance to make serious money. “Using
machines to beat the markets is a really difficult challenge,”
says McAuliffe, whose résumé includes biological research and
a stint at Amazon.com Inc. “But I don’t think it’s impossible.” <BW>
�Dewey, a freelance contributor, is a portfolio manager at Royal
BridgeCapital,a NewYork-based hedge fund.Rethinking
WealthINDEX
SOCIALISM
ByMattBruenig61Bloomberg BusinessweekPREVIOUS SPREAD AND THIS PAGE: ILLUSTRATIONS BY 731
③ Just cleaning the data to remove errors and outliers is a big taskAmericanshaveabout$100tril-
lion of wealth. If that was divided
evenly across the population,
each individual would have a
net worth of $300,000. At a 5%
rate of return, this money would
deliver to every person $15,000
annually in investment income.
Poverty would be eliminated,
inequality would be slashed,
and the power of concentrated
wealthwouldbeneutralized.
If thissoundslikea socialist
fantasy,it is:Some20th-century
Marxist economists argued that
it should be possible to bring a
country’s assets into collective
ownershipandpayeveryonea
universaldividend.Butpartof
thatsocialistidea—thatmillions
ofpeoplecouldownacountry’s
capital together—has, in a way,
already happened. Look at the
rise of index funds, such as
those run by BlackRock Inc.
and Vanguard Group Inc. These
companies now manage trillions
of dollars for tens of millions of
clients and are major investors in many public companies.
Thebulkoftheseassetsareheldbythewealthy,butit’snothard
toseehowa socialistsocietycouldbuildonthisdesign.A huge,
government-operated mutual fund—in which each citizen owned an equal,
nontransferable share—could work just as well as index funds do today.
The government could create an investment fund, use wealth taxes to
gradually fill it up with return-generating assets, and then pay out an
annual dividend to every American based on the fund’s return. Alaska’s
Permanent Fund, which pays a dividend to state residents, and Norway’s
Government Pension Fund Global have proved that such portfolios can
work not just in theory, but also in practice.
�Bruenig is president of People’s Policy Project, a think tank.