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Authors: Yuming Li 1 ; Pin Ni 1 and Victor Chang 2

Affiliations: 1 Department of Computer Science, University of Liverpool, Liverpool, U.K., Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou and China ; 2 International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China, Research Institute of Big Data Analytics (RIBDA), Xi’an Jiaotong-Liverpool University, Suzhou and China

Keyword(s): Deep Reinforcement Learning (DRL), Stock Market Strategy, Deep Q-Network.

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.

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Paper citation in several formats:
Li, Y.; Ni, P. and Chang, V. (2019). An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model. In Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS; ISBN 978-989-758-366-7; ISSN 2184-5034, SciTePress, pages 52-58. DOI: 10.5220/0007722000520058

@conference{complexis19,
author={Yuming Li. and Pin Ni. and Victor Chang.},
title={An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model},
booktitle={Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS},
year={2019},
pages={52-58},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007722000520058},
isbn={978-989-758-366-7},
issn={2184-5034},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk - COMPLEXIS
TI - An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model
SN - 978-989-758-366-7
IS - 2184-5034
AU - Li, Y.
AU - Ni, P.
AU - Chang, V.
PY - 2019
SP - 52
EP - 58
DO - 10.5220/0007722000520058
PB - SciTePress

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