Portfolio Management based on Deep Reinforcement Learning with Adaptive Sampling

Yu Hsiang Miao, Yi Ting Hsiao, Szu Hao Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Quantitative trading finds stable and profitable trading strategies by observing historical data through statistics or mathematics methods. However, existing studies are still insufficient for the generalization of trading strategies. Therefore, this study takes the constituents of the Dow Jones Industrial Average as the target and applies the problem of optimizing the portfolio. The goal is to construct a portfolio of five assets from the constituent stocks, and this portfolio could achieve excellent performance through our trading strategy. Also, this study proposes a sampling strategy to determine which data is worth learning by observing the learning condition in order to save computational time. From the result of the experiment, we could observe that the model with our sampling strategy performed 6-7 % better than our baselines in terms of Sharpe ratio.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages130-133
Number of pages4
ISBN (Electronic)9781665404839
DOIs
StatePublished - Dec 2020
Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
Country/TerritoryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • Deep learning
  • Portfolio management
  • Reinforcement Learning

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