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dc.contributor.advisor Kwon, Bongsuk -
dc.contributor.author Choe, Gangmin -
dc.date.accessioned 2024-04-11T15:19:12Z -
dc.date.available 2024-04-11T15:19:12Z -
dc.date.issued 2024-02 -
dc.description.abstract In this paper, we examine the empirical performance of investment strategies based on market prices of assets. This project is constructed in a threefold cases. First, we test the performance of technical indicators EMA and MACD by historical price data in United States and South Korea compare with buy and hold strategy. Second, we provide a trading strategy of a single stock with NASDAQ composite index as a trading signal generator. To the end, we investigate Deep Reinforcement Learning for asset allocation portfolio. In each case, we compare the profitability and risk factor with buy and hold or traditional investment strategies. Some strategies show that they beat the market in whole test period, but the others show the different performance depend on the time periods. -
dc.description.degree Master -
dc.description Department of Mathematical Sciences -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/82060 -
dc.identifier.uri http://unist.dcollection.net/common/orgView/200000743119 -
dc.language ENG -
dc.publisher Ulsan National Institute of Science and Technology -
dc.rights.embargoReleaseDate 9999-12-31 -
dc.rights.embargoReleaseTerms 9999-12-31 -
dc.subject Investment -
dc.subject Reinforcement Learning -
dc.title Analysis of trading strategies and asset allocation with deep reinforcement learning -
dc.type Thesis -

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