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

Yuming Li; Pin Ni; Victor Chang

doi:10.5220/0007722000520058

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

Yuming Li, Pin Ni, Victor Chang

International Business School Suzhou

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

16 Citations (Scopus)

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 language	English
Title of host publication	COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk
Editors	Victor Mendez Munoz, Farshad Firouzi, Ernesto Estrada, Victor Chang
Publisher	SciTePress
Pages	52-58
Number of pages	7
ISBN (Electronic)	9789897583667
DOIs	https://doi.org/10.5220/0007722000520058
Publication status	Published - 2019
Event	4th International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS 2019 - Heraklion, Crete, Greece Duration: 2 May 2019 → 4 May 2019

Publication series

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

Conference

Conference	4th International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS 2019
Country/Territory	Greece
City	Heraklion, Crete
Period	2/05/19 → 4/05/19

Keywords

Deep Q-Network
Deep Reinforcement Learning (DRL)
Stock Market Strategy

Access to Document

10.5220/0007722000520058

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 V. M. Munoz, F. Firouzi, E. Estrada, & 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

Li, Yuming ; Ni, Pin ; Chang, Victor. / An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model. COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk. editor / Victor Mendez Munoz ; Farshad Firouzi ; Ernesto Estrada ; Victor Chang. SciTePress, 2019. pp. 52-58 (COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk).

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title = "An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model",

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.",

keywords = "Deep Q-Network, Deep Reinforcement Learning (DRL), Stock Market Strategy",

author = "Yuming Li and Pin Ni and Victor Chang",

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language = "English",

series = "COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk",

publisher = "SciTePress",

pages = "52--58",

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Li, Y, Ni, P & Chang, V 2019, An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model. in VM Munoz, F Firouzi, E Estrada & V Chang (eds), COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk. COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk, SciTePress, pp. 52-58, 4th International Conference on Complexity, Future Information Systems and Risk, COMPLEXIS 2019, Heraklion, Crete, Greece, 2/05/19. https://doi.org/10.5220/0007722000520058

An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model. / Li, Yuming; Ni, Pin; Chang, Victor.
COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk. ed. / Victor Mendez Munoz; Farshad Firouzi; Ernesto Estrada; Victor Chang. SciTePress, 2019. p. 52-58 (COMPLEXIS 2019 - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AB - 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.

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

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

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