A Reinforcement Learning-based Encoder-Decoder Framework for Learning Stock Trading Rules

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Taghian Mehran; Asadi Ahmad; Safabakhsh Reza

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2023 | Volume:11 | Issue:1 Page(s): 103-118

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

A Reinforcement Learning-based Encoder-Decoder Framework for Learning Stock Trading Rules

Author(s)

Taghian Mehran | Asadi Ahmad | Safabakhsh Reza | Issue Writer Certificate

Keywords

Deep Reinforcement Learning

Deep Q-Learning

Single Stock Trading

Portfolio Management

Encoder-Decoder Framework

Abstract

The quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by machine learning models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning. In this paper, a novel end-to-end model based on the neural Encoder-Decoder Framework combined with Deep Reinforcement Learning is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans