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Dynamic modelling of optimal pricing and trading policies under uncertainty

Dynamic modelling of optimal pricing and trading policies under uncertainty
Dynamic modelling of optimal pricing and trading policies under uncertainty
The objective of this thesis is to present a set of useful tools for problems of sequential decision making under uncertainty. Specifically, we study three applications of dynamic planning: dynamic pricing of non-durable products in the context of Markov processes, dynamic pricing of high end fashionable products with autoregressive demand, and the dynamic trading of financial securities with added sign constraints.
Market volatility, incomplete or delayed information, and unpredictability of underlying systems are integral to real-world problems. It is important to establish methods to integrate these factors into the modelling framework of choice. In this research we study stochastic dynamic programs and their use in finding optimal or near-optimal strategies for the above problems.

In the first of three papers comprising this thesis, we examine the dynamic pricing problem in the context of Markov decision processes, and explore the structural characteristics of the model. Our results support the use of exact methods when assuming the state of the system (demand) is unobservable. The second paper is concerned with a dynamic pricing problem that assumes an autoregressive evolution model for the demand. We provide a simple but ef- fective approximate dynamic programming method that outperforms the classic methods of solving dynamic programming problems. Finally, in the third paper, we examine the dynamic trading of large blocks of securities by extending the dynamic programming framework to include constraints and additional information. We explore the characteristics of the model to improve on the closed form solutions available in the literature, but we also utilise a heuristic approximate dynamic programming method to provide near-optimal results when the problem is augmented with necessary constraints to handle practical settings.
Abbaszadeh, Shahin
63a3d311-bef9-4b54-ae35-4ba3f0985264
Abbaszadeh, Shahin
63a3d311-bef9-4b54-ae35-4ba3f0985264
Wu, Yue
e279101b-b392-45c4-b894-187e2ded6a5c

Abbaszadeh, Shahin (2015) Dynamic modelling of optimal pricing and trading policies under uncertainty. University of Southampton, Southampton Business School, Doctoral Thesis, 136pp.

Record type: Thesis (Doctoral)

Abstract

The objective of this thesis is to present a set of useful tools for problems of sequential decision making under uncertainty. Specifically, we study three applications of dynamic planning: dynamic pricing of non-durable products in the context of Markov processes, dynamic pricing of high end fashionable products with autoregressive demand, and the dynamic trading of financial securities with added sign constraints.
Market volatility, incomplete or delayed information, and unpredictability of underlying systems are integral to real-world problems. It is important to establish methods to integrate these factors into the modelling framework of choice. In this research we study stochastic dynamic programs and their use in finding optimal or near-optimal strategies for the above problems.

In the first of three papers comprising this thesis, we examine the dynamic pricing problem in the context of Markov decision processes, and explore the structural characteristics of the model. Our results support the use of exact methods when assuming the state of the system (demand) is unobservable. The second paper is concerned with a dynamic pricing problem that assumes an autoregressive evolution model for the demand. We provide a simple but ef- fective approximate dynamic programming method that outperforms the classic methods of solving dynamic programming problems. Finally, in the third paper, we examine the dynamic trading of large blocks of securities by extending the dynamic programming framework to include constraints and additional information. We explore the characteristics of the model to improve on the closed form solutions available in the literature, but we also utilise a heuristic approximate dynamic programming method to provide near-optimal results when the problem is augmented with necessary constraints to handle practical settings.

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Published date: March 2015
Organisations: University of Southampton, Southampton Business School

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Local EPrints ID: 384097
URI: http://eprints.soton.ac.uk/id/eprint/384097
PURE UUID: a774f9b3-56d5-4144-96a9-d3b39b9be6ad

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Date deposited: 08 Dec 2015 11:18
Last modified: 18 Jul 2017 04:29

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