Show simple item record

dc.contributor.advisorMao, Connie X.
dc.creatorHE, Xuezhong
dc.date.accessioned2021-05-24T18:39:00Z
dc.date.available2021-05-24T18:39:00Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/20.500.12613/6458
dc.description.abstractThis dissertation implements an optimal trading strategy based on the machine learning method and extreme value theory (EVT) to obtain an excess return on investments in the capital market. The trading strategy outperforms the benchmark S&P 500 index with higher returns and lower volatility through effective market timing. In addition, this dissertation starts by modeling the market tail risk using the EVT and reinforcement learning methods, distinguishing from the traditional value at risk method. In this dissertation, I used EVT to extract the characteristics of the tail risk, which are inputs for reinforcement learning. This process is proved to be effective in market timing, and the trading strategy could avoid market crash and achieve a long-term excess return. In sum, this study has several contributions. First, this study takes a new method to analyze stock price (in this dissertation, I use the S&P 500 index as a stock). I combined the EVT and reinforcement learning to study the price tail risk and predict stock crash efficiently, which is a new method for tail risk research. Thus, I can predict the stock crash or provide the probability of risk, and then, the trading strategy can be built. The second contribution is that this dissertation provides a dynamic market timing trading strategy, which can significantly outperform the market index with a lower volatility and a higher Sharpe ratio. Moreover, the dynamic trading process can provide investors an intuitive sense on the stock market and help in decision-making. Third, the success of the strategy shows that the combination of EVT and reinforcement learning can predict the stock crash very well, which is a great improvement on the extreme event study and deserves further study.
dc.format.extent68 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectFinance
dc.subjectExtreme value theory
dc.subjectReinforcement learning
dc.subjectTail risk
dc.subjectTrading strategy
dc.titleMarket Timing strategy through Reinforcement Learning
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberRytchkov, Oleg
dc.contributor.committeememberBakshi, Xiaohui Gao
dc.contributor.committeememberBasu, Sudipta, 1965-
dc.description.departmentBusiness Administration/Finance
dc.relation.doihttp://dx.doi.org/10.34944/dspace/6440
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreeD.B.A.
dc.identifier.proqst14387
dc.date.updated2021-05-19T16:08:23Z
dc.embargo.lift05/19/2023
dc.identifier.filenameHE_temple_0225E_14387.pdf


Files in this item

Thumbnail
Name:
HE_temple_0225E_14387.pdf
Embargo:
2023-05-19
Size:
1.242Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record