Inside the Black Box: The Simple Truth About Quantitative Trading

228 Pages·2009·3.01 MB·English

by  Rishi K Narang(auth.)

Inside the
Black Box
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Inside the
Black Box
The Simple Truth About
Quantitative Trading
RISHI K NARANG
John Wiley & Sons, Inc.
Copyright(cid:2)C 2009byRishiKNarang.Allrightsreserved.
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LibraryofCongressCataloging-in-PublicationData:
Narang,RishiK,1974–
Insidetheblackbox:thesimpletruthaboutquantitativetrading/
RishiKNarang.
p.cm.–(Wileyfinanceseries)
Includesbibliographicalreferencesandindex.
ISBN978-0-470-43206-8(cloth)
1.Portfoliomanagement–Mathematicalmodels. 2.Investment
analysis–Mathematicalmodels. 3.Stocks–Mathematicalmodels. I.Title.
HG4529.5.N372009
332.64'2–dc22
2009010579
PrintedintheUnitedStatesofAmerica
10 9 8 7 6 5 4 3 2 1
This book is dedicated to my father and mother, Thakur and
Krishna Narang, to whom I owe so much, and to my wife and
partner of many years, Carolyn Wong, whose love and
support make a hell of a lot of things really a lot better.
Contents
Preface xi
Acknowledgments xv
PARTONE
TheQuantUniverse
CHAPTER1
WhyDoesQuantTradingMatter? 3
TheBenefitofDeepThought 7
TheMeasurementandMismeasurementofRisk 8
DisciplinedImplementation 10
Summary 10
CHAPTER2
AnIntroductiontoQuantitativeTrading 11
WhatIsaQuant? 12
WhatIstheTypicalStructureofaQuantitativeTrading
System? 14
Summary 17
PARTTWO
InsidetheBlackBox
CHAPTER3
AlphaModels:HowQuantsMakeMoney 21
TypesofAlphaModels:TheoryDrivenandDataDriven 22
Theory-DrivenAlphaModels 24
Data-DrivenAlphaModels 37
ImplementingtheStrategies 38
vii
viii CONTENTS
BlendingAlphaModels 48
Summary 53
CHAPTER4
RiskModels 55
LimitingtheAmountofRisk 57
LimitingtheTypesofRisk 60
Summary 64
CHAPTER5
TransactionCostModels 67
DefiningTransactionCosts 68
TypesofTransactionCostModels 72
Summary 77
CHAPTER6
PortfolioConstructionModels 79
Rule-BasedPortfolioConstructionModels 80
PortfolioOptimizers 85
OutputofPortfolioConstructionModels 95
HowQuantsChooseaPortfolioConstructionModel 96
Summary 96
CHAPTER7
Execution 99
OrderExecutionAlgorithms 100
High-FrequencyTrading:BlurringtheLinebetween
AlphaandExecution 105
TradingInfrastructure 107
Summary 108
CHAPTER8
Data 111
TheImportanceofData 111
TypesofData 113
SourcesofData 115
CleaningData 117
StoringData 122
Summary 124
Contents ix
CHAPTER9
Research 125
BlueprintforResearch:TheScientificMethod 125
IdeaGeneration 127
Testing 129
Summary 145
PARTTHREE
APracticalGuideforInvestorsinQuantitativeStrategies
CHAPTER10
RisksInherenttoQuantStrategies 149
ModelRisk 150
RegimeChangeRisk 154
ExogenousShockRisk 158
Contagion,orCommonInvestor,Risk 159
HowQuantsMonitorRisk 166
Summary 168
CHAPTER11
CriticismsofQuantTrading:SettingtheRecordStraight 169
TradingIsanArt,NotaScience 169
QuantsCauseMoreMarketVolatilityby
UnderestimatingRisk 171
QuantsCannotHandleUnusualEventsorRapid
ChangesinMarketConditions 174
QuantsAreAlltheSame 176
OnlyaFewLargeQuantsCanThriveintheLongRun 178
QuantsAreGuiltyofDataMining 179
Summary 182
CHAPTER12
EvaluatingQuantsandQuantStrategies 185
GatheringInformation 186
EvaluatingaQuantitativeTradingStrategy 188
EvaluatingtheAcumenofQuantitativeTraders 191
TheEdge 193
EvaluatingIntegrity 196
HowQuantsFitintoaPortfolio 198
Summary 201
x CONTENTS
CHAPTER13
LookingtotheFutureofQuantTrading 203
Notes 207
AbouttheAuthor 212
Index 213
Preface
A
n unnecessary opaqueness surrounds quantitative trading strategies
(knowntomanyasblackboxes),despitetheirimportancetothecapital
marketsandthesensational,widelyknownexamplesoftheirsuccessesand
failures. This opaqueness, which quants themselves frequently perpetuate,
exacerbatesanalreadywidespreadmisunderstandingofquantitativetrading
inthebroaderinvestmentcommunity.
This book takes you on a tour through the black box, inside and out.
It sheds light on the work that quants do, lifting the veil of mystery that
surroundsquantitativetradingandallowingthoseinterestedindoingsoto
evaluatequantsandtheirstrategies.
The first thing that should be made clear is that people, not machines,
are responsible for most of the interesting aspects of quantitative trading.
Quantitative trading can be defined as the systematic implementation of
trading strategies that human beings create through rigorous research. In
thiscontext,systematicisdefinedasadisciplined,methodological,andau-
tomated approach. Despite this talk of automation and systematization,
people conduct the research and decide what the strategies will be, peo-
ple select the universe of securities for the system to trade, and people
choose what data to procure and how to clean those data for use in a
systematic context, among a great many other things. These people, the
onesbehindquanttradingstrategies,arecommonlyreferredtoasquantsor
quanttraders.
Quants employ the scientific method in their research. Though this re-
search is aided by technology and involves mathematics and formulae, the
research process is thoroughly dependent on human decision making. In
fact,humandecisionspervadenearlyeveryaspectofthedesign,implemen-
tation, and monitoring of quant trading strategies. As it turns out, quant
strategiesandtraditionaldiscretionaryinvestmentstrategies,whichrelyon
humandecisionmakerstomanageportfoliosdaytoday,arerathersimilar
inwhattheydo.
The differences between a quant strategy and a discretionary strategy
can be seen in how the strategy is created and in how it is implemented.
By carefully researching their strategies, quants are able to assess their
xi