Model-based Reinforcement Learning in Modified Levy Jump-Diffusion MarkovDecision Model and Its Financial Applications
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.
Type
Honors thesisDepartment
Computer SciencePermalink
https://hdl.handle.net/10161/15753Provenance
Thesis placed under embargo at request of author.--mjf33 11/7/2018
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.Collections
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