Model-based Reinforcement Learning in Modified Levy Jump-Diffusion MarkovDecision Model and Its Financial Applications

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Date

2017-11-15

Advisors

Liu, Jian-Guo
Parr, Ronald

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Abstract

This thesis intends to address an important cause of the 2007-2008 financial crisis by incorporating prediction on asset pricing jumps in asset pricing models, the non-normality of asset returns. Several different machine learning techniques, including the Unscented Kalman Filter and Approximate Planning are used, and an improvement in Approximate Planning is developed to improve algorithm time complexity with limited loss in optimality. We obtain significant result in predicting jumps with market sentiment memory extracted from Twitter. With the model, we develop a reinforcement learning module that achieves good performance and which captures over 60% of profitable periods in the market.

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Provenance

Thesis placed under embargo at request of author.--mjf33 11/7/2018

Citation

Citation

Zhu, Zheqing (2017). Model-based Reinforcement Learning in Modified Levy Jump-Diffusion MarkovDecision Model and Its Financial Applications. Honors thesis, Duke University. Retrieved from https://hdl.handle.net/10161/15753.


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