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Dynamic Pricing and Learning in Pred...

Schultz, Adam.

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Dynamic Pricing and Learning in Prediction Markets.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dynamic Pricing and Learning in Prediction Markets./
作者:	Schultz, Adam.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	166 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
標題:	Operations research. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10282483
ISBN:	9780355079319

Dynamic Pricing and Learning in Prediction Markets.
Schultz, Adam.

Dynamic Pricing and Learning in Prediction Markets. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 166 p.

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--The University of Chicago, 2017.

In this dissertation, we explore the nature of dynamic pricing, information aggregation, and bias in prediction markets. We begin with Chapter 1, in which we develop a dynamic control model to analyze how a monopolistic market maker can optimally set prices in a prediction market while learning information about the event outcome. We demonstrate the market maker's optimal policy when facing myopic agents, and prove that a myopic (greedy) policy performs relatively well in this context. We also introduce a setting where a sophisticated agent (i.e., insider trader) can exploit the market maker. We characterize the amount of harm imposed on the market maker by the presence of this strategic agent, and propose a policy the market maker can adapt to mitigate the presence of the strategic agent.

ISBN: 9780355079319Subjects--Topical Terms:

547123
Operations research.

Dynamic Pricing and Learning in Prediction Markets.
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520 $a In this dissertation, we explore the nature of dynamic pricing, information aggregation, and bias in prediction markets. We begin with Chapter 1, in which we develop a dynamic control model to analyze how a monopolistic market maker can optimally set prices in a prediction market while learning information about the event outcome. We demonstrate the market maker's optimal policy when facing myopic agents, and prove that a myopic (greedy) policy performs relatively well in this context. We also introduce a setting where a sophisticated agent (i.e., insider trader) can exploit the market maker. We characterize the amount of harm imposed on the market maker by the presence of this strategic agent, and propose a policy the market maker can adapt to mitigate the presence of the strategic agent.
520 $a In Chapter 2, we explore how market makers use pricing as a mechanism to aggregate information in a biased prediction market. We collect a novel data set of time series data to study a sports betting market, including Twitter data to control for breaking news events that lead to information changes in the market. After investigating how market makers adjust prices, we present an approach to estimate potential bias in bettors' beliefs about the game outcomes. This model allows us to perform a counterfactual analysis in which we characterize the optimal point spread for the market maker for each game. We use this model to assess market makers' expected profit performance. We demonstrate that market makers' pricing policies do not follow the oft-cited strategy of "balancing" the bet and analyze how market makers benefit from deviations from this policy.
520 $a In Chapter 3, we explore how biases evolve over time in prediction markets. In particular, we study NBA point spread betting markets and futures odds betting markets for the NCAA tournament. We conclude that biases appear to persist in these markets over time, and we explore potential reasons for this market behavior.
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