國立虎尾科技大學 |

Dynamic Pricing and Learning in Prediction Markets.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Dynamic Pricing and Learning in Prediction Markets./
作者:	Schultz, Adam.
面頁冊數:	1 online resource (166 pages)
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:	click for full text (PQDT)
ISBN:	9780355079319

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

Dynamic Pricing and Learning in Prediction Markets. - 1 online resource (166 pages)

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

Thesis (Ph.D.)

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355079319Subjects--Topical Terms:

573517
Operations research.
Index Terms--Genre/Form:

554714
Electronic books.

Dynamic Pricing and Learning in Prediction Markets.
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100 1 $a Schultz, Adam. $3 1182223
245 1 0 $a Dynamic Pricing and Learning in Prediction Markets.
264 0 $c 2017
300 $a 1 online resource (166 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Advisers: John R. Birge; N. Bora Keskin.
502 $a Thesis (Ph.D.) $c The University of Chicago $d 2017.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Operations research. $3 573517
650 4 $a Commerce-Business. $3 1179465
655 7 $a Electronic books. $2 local $3 554714
690 $a 0796
690 $a 0505
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Chicago. $b Business. $3 1179073
773 0 $t Dissertation Abstracts International $g 78-12B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10282483 $z click for full text (PQDT)