Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713432
Title: Algorithmic learning from financial predictions
Author: Taptagaporn, Pongphat
Awarding Body: London School of Economics and Political Science (LSE)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2017
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Abstract:
We study how financial predictions can be used in learning algorithms for problems such as portfolio selection and derivatives pricing, from the perspective of minimizing regret; the worst-case loss (across all possible price paths) against some optimal benchmark model with superior information. Unlike most studies in financial mathematics, we do not make any underlying assumptions beyond the existence of such predictions, so our results are robust in the model-free sense. This thesis consists of three main ideas: 1. Study a portfolio selection model that competes with an optimal static trading strategy (the best fixed strategy in hindsight) using predictions of the optimal portfolio allocation. 2. Study a portfolio selection model that competes (in probability) with an optimal dynamic trading strategy (the best greedy strategy in hindsight) using price predictions of each asset in the portfolio. 3. Derive robust derivative pricing bounds for vanilla options and various exotic derivatives based on price predictions of the underlying asset(s). This work is focused on the mathematical analysis of these models, using techniques from theoretical algorithmic and statistical learning.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.713432  DOI: Not available
Keywords: QA Mathematics
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