Grant: Collaborative Research and Development Grants
University of Waterloo
$97,000.00
Efficient algorithms for online ad markets with time constraints
EF 2019-20
Résumé
Algorithmic trading is an essential part of today's economy. This proposal in on the design and implementation of algorithmic trading in the context of ad auctions in the wireless mobile context. This work is being done in collaboration with our industrial partner Addictive Mobility, Toronto that provides cutting edge solutions for providing ad space to advertisers in the context of mobile wireless systems by trading on ad auction exchanges.The proposed research is to develop advanced algorithms that help improve the yield and maximize the profitability for algorithmic trading in the ad auction context. The key issues are bidding strategies that must operate at very fast time scales of around 100 milliseconds. The research will consist of three main components: 1) Development of models and techniques for ad space bidding given strict time constraints.2) Development of algorithmic trading strategies, and 3) Implementation of strategies. We will work closely with our industrial partner in the 2nd and 3rd thrusts.The research will involve many components: 1) Building models of random interactions using ideas from random exchange theory, 2) Understanding the role of budget and time constraints both in terms of execution time as well as campaign time horizon, 3) Understanding the role of information in the context of actions, 4) Develop techniques and algorithms for price discovery, and 5) Development of real-time bidding algorithms that can operate on the information history within the constraints of the auction durations.The techniques will involve Markov models, mean field theory, stochastic decision theory, learning, and the development of algorithms.
Programme
Collaborative Research and Development Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
501092-2016
Name nom
Mazumdar, Ravi
Department département
Electrical & Computer Engineering
Organization id
33
Institution établissement
University of Waterloo
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2019
Competition year année de concours
2016
Award amount
97000
Program id
CRDPJ
Program name en
Collaborative Research and Development Grants
Program name fr
Subventions de recherche et développement coopérative
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
804
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Communication systems and services (planning, organization, services)
Area of application fr
Systèmes et services de communications (planification, organisation et services)
Research subject code
2713
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Algorithms
Research subject fr
Algorithmes
Installment
inst:3 - 3
Partie
3
Nb partie
3
Application title
Efficient algorithms for online ad markets with time constraints
Keyword
NSERC
Application summary
Algorithmic trading is an essential part of today's economy. This proposal in on the design and implementation of algorithmic trading in the context of ad auctions in the wireless mobile context. This work is being done in collaboration with our industrial partner Addictive Mobility, Toronto that provides cutting edge solutions for providing ad space to advertisers in the context of mobile wireless systems by trading on ad auction exchanges.The proposed research is to develop advanced algorithms that help improve the yield and maximize the profitability for algorithmic trading in the ad auction context. The key issues are bidding strategies that must operate at very fast time scales of around 100 milliseconds. The research will consist of three main components: 1) Development of models and techniques for ad space bidding given strict time constraints.2) Development of algorithmic trading strategies, and 3) Implementation of strategies. We will work closely with our industrial partner in the 2nd and 3rd thrusts.The research will involve many components: 1) Building models of random interactions using ideas from random exchange theory, 2) Understanding the role of budget and time constraints both in terms of execution time as well as campaign time horizon, 3) Understanding the role of information in the context of actions, 4) Develop techniques and algorithms for price discovery, and 5) Development of real-time bidding algorithms that can operate on the information history within the constraints of the auction durations.The techniques will involve Markov models, mean field theory, stochastic decision theory, learning, and the development of algorithms.