Financial Methods for Online Advertising

153 Pages·2015·2.76 MB·English

by  Bowei Chen

Financial Methods for Online
Advertising
Bowei Chen
Adissertationsubmittedinpartialfulfillment
oftherequirementsforthedegreeof
DoctorofPhilosophy
of
UniversityCollegeLondon.
DepartmentofComputerScience
UniversityCollegeLondon
16thJanuary2015
2
I, Bowei Chen, confirm that the work presented in this thesis is my own. Where
informationhasbeenderivedfromothersources,Iconfirmthatthishasbeenindicated
inthework.
©2014BoweiChen.
Allrightsreserved.
“Whenever a theory appears to you as the only possible one,
take this as a sign that you have neither understood the theory
northeproblemwhichitwasintendedtosolve.”
–SirKarlRaimundPopper
ObjectiveKnowledge: AnEvolutionaryApproach(1972)
Abstract
Online advertising, a form of advertising that reaches consumers through the World
Wide Web, has become a multi-billion dollar industry. Using the state of the art com-
puting technologies, online auctions have become an important sales mechanism for
automating transactions in online advertising markets, where advertisement (shortly
ad) inventories, such as impressions or clicks, are able to be auctioned off in millisec-
ondsaftertheyaregeneratedbyonlineusers. However,withprovidingnon-guaranteed
deliveries,thecurrentauctionmechanismshaveanumberoflimitationsincluding: the
uncertainty in the winning payment prices for buyers; the volatility in the seller’s rev-
enue; and the weak loyalty between buyer and seller. To address these issues, this
thesis explores the methods and techniques from finance to evaluate and allocate ad
inventories over time and to design new sales models. Finance, as a sub-field of mi-
croeconomics,studieshowindividualsandorganisationsmakedecisionsregardingthe
allocation of resources over time as well as the handling of risk. Therefore, we believe
thatfinancialmethodscanbeusedtoprovidenovelsolutionstothenon-guaranteedde-
liveryprobleminonlineadvertising. Thisthesishasthreemajorcontributions. Wefirst
study an optimal dynamic model for unifying programmatic guarantee and real-time
bidding in display advertising. This study solves the problem of algorithmic pricing
and allocation of guaranteed contracts. We then propose a multi-keyword multi-click
adoption. Thisworkdiscussesaflexiblewayofguaranteeddeliveriesinthesponsored
search context, and it’s evaluation is under the no arbitrage principle and is based on
the assumption that the underlying winning payment prices of candidate keywords for
specificpositionsfollowageometricBrownianmotion. However,accordingtoourdata
analysis and other previous research, the same underlying assumption is not valid em-
pirically for display ads. We therefore study a lattice framework to price an ad option
based on a stochastic volatility underlying model. This research extends the usage of
adoptionstodisplayadvertisinginamoregeneralsituation.
Acknowledgements
IhavebeenveryluckytolearnfromanumberofoutstandingresearchersatUniversity
CollegeLondon(UCL)duringmyPhDstudy. Firstofall,Iwishtoexpressmygreatest
thankstomyfirstsupervisor,JunWang,whoseexcellentguidancehasbeenabsolutely
essential for the completion of this thesis. I would like to thank him for introducing
me into the field of online advertising, and his consistent encouragement and support.
Over the years, I have learned many things from him: high quality research, effective
communication skills and hard-working attitude. I can not ask for more and the high
standardshehasimpartedtomewillbenefitmyresearcheffortsmywholelifelong.
IamgratefultoIngemarCoxforbeingmysecondsupervisorandhisconstructive
technicalguidanceonmyresearch. IwouldliketothankMohanKankanhalliformany
enjoyable discussions during his visit at UCL, and his support in my research and job
search. I would like to thank Sofia Ceppi for supervising me at Microsoft Research
Cambridgeandheradviceonalgorithmicgametheoryandmechanismdesign. Iwould
like to thank Philip Trelevean and Dell Zhang for being my thesis examiners and their
usefulcomments. IalsoappreciatePhilip’sguidanceandsupportonmyinternshipand
jobsearch. IwouldliketothankJinqiuHuaforrecommendingmetoUCLandhiskind
advice on talks, research, and everything in between. I am grateful to all the faculty,
staff,andstudentsatUCLwhohavehelpedmeinwayslargeandsmall. Iappreciatethe
funding supports from the Faculty of Engineering (Dean’s Prize), the UCL Advances
(PhDEnterpriseScholarship)andtheUCLBITE(SecondmentExchangeAward).
I would like to express my deep gratitude to my family and friends, for their con-
tinuous love, support, and encouragement. Special thanks go to my wife Xin Wang,
who has always been there right beside me through the most difficult periods of my
PhD journey. Without her many sacrifices, I would not have made it this far. Finally, I
would love to dedicate this thesis to my baby boy Yanchen Chen, who has brought so
muchhappinessandjoytoourlives.
Contents
Abstract 4
Acknowledgements 5
ListofFigures 14
ListofTables 17
Notation 18
1 Introduction 19
1.1 MajorTypesofOnlineAds . . . . . . . . . . . . . . . . . . . . . . . . 20
1.2 MarketParticipants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.3 FundamentalChallenges . . . . . . . . . . . . . . . . . . . . . . . . . 23
1.4 ProposedSolutionsUsingFinancialMethods . . . . . . . . . . . . . . 25
1.5 StructureoftheThesis . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2 Background 29
2.1 LiteratureReviewonOnlineAdvertising . . . . . . . . . . . . . . . . . 29
2.1.1 EvolutionofOnlineAdvertisingSalesMechanisms . . . . . . . 29
2.1.2 GuaranteedAdvertisingDeliveries . . . . . . . . . . . . . . . . 33
2.2 PreliminariesonFinancialMethods . . . . . . . . . . . . . . . . . . . 35
2.2.1 Uncertainty,RiskandTimeValueofMoney . . . . . . . . . . . 35
2.2.2 RevenueManagement . . . . . . . . . . . . . . . . . . . . . . 36
2.2.3 OptionsandOptionPricingMethods . . . . . . . . . . . . . . 37
3 OptimalPricingandAllocationofDisplayInventories 45
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2 TheModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Contents 7
3.2.1 ProblemFormulation . . . . . . . . . . . . . . . . . . . . . . . 49
3.2.2 DistributionofBidsinRTB . . . . . . . . . . . . . . . . . . . 50
3.2.3 RiskAversionandPurchaseBehaviour . . . . . . . . . . . . . 51
3.2.4 OptimalDynamicPrices . . . . . . . . . . . . . . . . . . . . . 52
3.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3.2 BiddingBehaviours . . . . . . . . . . . . . . . . . . . . . . . 57
3.3.3 SupplyandDemand . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.4 BidsandPaymentPrices . . . . . . . . . . . . . . . . . . . . . 59
3.3.5 DemandforGuaranteedImpressions . . . . . . . . . . . . . . . 62
3.3.6 RevenueAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4 Multi-KeywordMulti-ClickAdOptionsforSponsoredSearch 69
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.2 FlexibleGuaranteedDeliveriesviaMulti-KeywordMulti-ClickAdOp-
tions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.3 OptionPricingMethods . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3.1 UnderlyingStochasticModel . . . . . . . . . . . . . . . . . . 74
4.3.2 TerminalValuePricing . . . . . . . . . . . . . . . . . . . . . . 75
4.3.3 Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.4 RevenueAnalysisforSearchEngine . . . . . . . . . . . . . . . . . . . 79
4.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.5.1 DataandExperimentalDesign . . . . . . . . . . . . . . . . . . 82
4.5.2 ParameterEstimationandOptionPricing . . . . . . . . . . . . 83
4.5.3 ModelValidationandRobustnessTest . . . . . . . . . . . . . . 87
4.5.4 EffectsonSearchEngine’sRevenue . . . . . . . . . . . . . . . 93
4.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.7 ChapterAppendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.7.1 ProofoftheNo-EarlyExercisePropertyfortheAdOption . . . 95
4.7.2 DerivationoftheAdOptionPricingFormula . . . . . . . . . . 96
Contents 8
4.7.3 OptionPricingFormulasforSpecialCases . . . . . . . . . . . 98
5 LatticeMethodsforPricingDisplayAdOptions 100
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.2 LatticesfortheGBMUnderlyingModel . . . . . . . . . . . . . . . . . 101
5.2.1 BinomialLattice . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.2.2 TrinomialLattice . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.3 CensoredBinomialLatticefortheSVUnderlying . . . . . . . . . . . . 107
5.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.4.1 DatasetsandExperimentalDesign . . . . . . . . . . . . . . . . 112
5.4.2 FitnessofGBMandSVModels . . . . . . . . . . . . . . . . . 113
5.4.3 DeliveryPerformanceforAdvertiser . . . . . . . . . . . . . . . 119
5.4.4 RevenueAnalysisforPublisherandSearchEngine . . . . . . . 122
5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.6 ChapterAppendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.6.1 Proof of Equivalence of the Option Price under the One-Step
BinomialLattice . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.6.2 Convergence of the Binomial Lattice Option Pricing Model to
theBSMModel . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.6.3 Trinomial Lattice Methods for Pricing Display Ad Options
withtheGBMUnderlying . . . . . . . . . . . . . . . . . . . . 128
5.6.4 BinomialDiffusionApproximation . . . . . . . . . . . . . . . 128
5.6.5 MathematicalResultsoftheSVModel . . . . . . . . . . . . . 129
6 Conclusion 133
6.1 ConcludingRemarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
6.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.2.1 OptimalStochasticDynamicModels . . . . . . . . . . . . . . 135
6.2.2 StochasticProcessesforMarketPrice . . . . . . . . . . . . . . 135
6.2.3 Game-TheoreticalModelsforAdOptionPricing . . . . . . . . 136
6.2.4 MarketDesignofAdDerivatives . . . . . . . . . . . . . . . . 136
Contents 9
Appendices 137
A GlossaryofTechnicalTerms 138
B RelatedPublications 141
Bibliography 143
List of Figures
1.1 Displayads(indottedbluelinebox)ontheYahoo! CarsWebpage. . . 22
1.2 Organicsearchresults(inbluelinebox)andpaidsearchresults(indot-
tedbluelinebox)ontheGoogleSponsoredsearchresultspage(SERP). 22
1.3 DifferencesandrelationshipsofthecontributionsinChapters3-5. . . . 26
3.1 Systematic view of PG and RTB in display advertising, where [t ,t ]
0 n
isthetimeperiodthatapublisher(orSSP)sellstheguaranteedimpres-
sionswhichwillbecreatedinfutureperiod[t ,t ]. . . . . . . . . . . 46
n n+1
3.2 Theimpactofmodelparametersontheguaranteedsellingprices: α,β,
ζ,ηaredefinedinEqs.(3.6)&(3.7);ωκistheexpectedsizeofpenalty;
γ isthepercentageofestimatedfutureimpressionstosellinadvanced;
T is the length of guaranteed selling period; τ is the remaining time to
thedeliverydate;p(τ)istheguaranteedsellingpriceatτ. . . . . . . . . 55
3.3 OverviewofstatisticsforthewinningadvertisersfromtheSSPdataset
inthetrainingperiod. . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.4 Overview of daily supply and demand of ad slots in the SSP dataset
in the training period: S is the number of total supplied impressions;
Q is the number of total demand impressions; ξ is the per impression
demand(i.e.,thenumberofadvertiserswhobidforanimpression). . . 60
3.5 HierarchicalclustertreeofadslotsintheSSPdatasetwherethecluster
metricisaveragedistanceintheperimpressiondemandξ. . . . . . . . 60
3.6 Overview of advertisers’ hourly arrival per day, where the red shaded
barrepresentsthepeakhour. . . . . . . . . . . . . . . . . . . . . . . . 61
3.7 Empirical example of estimating π(ξ), φ(ξ) and ψ(ξ) for AdSlot25
from historical bids, where ξ is the per impression demand, π(ξ) is the
expected winning bid, φ(ξ) is the expected payment price and ψ(ξ) is
thestandarddeviationofpaymentprices. . . . . . . . . . . . . . . . . 62