An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model

Yuming Li, Pin Ni, Victor Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The stock market plays a major role in the entire financial market. How to obtain effective trading signals in the stock market is a topic that stock market has long been discussing. This paper first reviews the Deep Reinforcement Learning theory and model, validates the validity of the model through empirical data, and compares the benefits of the three classical Deep Reinforcement Learning models. From the perspective of the automated stock market investment transaction decision-making mechanism, Deep Reinforcement Learning model has made a useful reference for the construction of investor automation investment model, the construction of stock market investment strategy, the application of artificial intelligence in the field of financial investment and the improvement of investor strategy yield.

Original languageEnglish
Title of host publicationCOMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk
EditorsFarshad Firouzi, Ernesto Estrada, Victor Mendez Munoz, Victor Chang
PublisherSciTePress
Pages52-58
Number of pages7
ISBN (Electronic)9789897583667
DOIs
Publication statusPublished - 1 Jan 2019
Event4th International Conference on Complexity, Future Information Systems and Risk - Heraklion, Crete, Greece
Duration: 2 May 20194 May 2019
Conference number: 4

Publication series

NameCOMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk

Conference

Conference4th International Conference on Complexity, Future Information Systems and Risk
Abbreviated titleCOMPLEXIS 2019
CountryGreece
CityHeraklion, Crete
Period2/05/194/05/19

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  • Cite this

    Li, Y., Ni, P., & Chang, V. (2019). An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model. In F. Firouzi, E. Estrada, V. M. Munoz, & V. Chang (Eds.), COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk (pp. 52-58). (COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk). SciTePress. https://doi.org/10.5220/0007722000520058