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Application of deep reinforcement learning in stock trading strategies and stock forecasting

Application of deep reinforcement learning in stock trading strategies and stock forecasting The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of deep reinforcement learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of deep reinforcement learning in financial markets and the credibility and advantages of strategic decision-making. Keywords Reinforcement learning · Financial strategy · Deep Q learning Mathematics Subject Classification 68T01 · 91G10 1 Introduction 1.1 Background As the current Artificial Intelligence methods have become closer to the way humans think and behave, there is a need to develop something innovative. deep reinforcement B Victor Chang victorchang.research@gmail.com Yuming Li Y.Li278@liverpool.ac.uk Pin Ni P.Ni2@liverpool.ac.uk Department of Computer Science, University of Liverpool, Liverpool, UK School of Computing Engineering and Digital Technologies, Teesside University, Middlesbrough, UK 123 Y. Li et al. learning (DRL), http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computing Springer Journals

Application of deep reinforcement learning in stock trading strategies and stock forecasting

Computing , Volume OnlineFirst – Dec 23, 2019

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References (34)

Publisher
Springer Journals
Copyright
Copyright © 2019 by Springer-Verlag GmbH Austria, part of Springer Nature
Subject
Computer Science; Computer Science, general; Information Systems Applications (incl.Internet); Computer Communication Networks; Software Engineering; Artificial Intelligence; Computer Appl. in Administrative Data Processing
ISSN
0010-485X
eISSN
1436-5057
DOI
10.1007/s00607-019-00773-w
Publisher site
See Article on Publisher Site

Abstract

The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of deep reinforcement learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of deep reinforcement learning in financial markets and the credibility and advantages of strategic decision-making. Keywords Reinforcement learning · Financial strategy · Deep Q learning Mathematics Subject Classification 68T01 · 91G10 1 Introduction 1.1 Background As the current Artificial Intelligence methods have become closer to the way humans think and behave, there is a need to develop something innovative. deep reinforcement B Victor Chang victorchang.research@gmail.com Yuming Li Y.Li278@liverpool.ac.uk Pin Ni P.Ni2@liverpool.ac.uk Department of Computer Science, University of Liverpool, Liverpool, UK School of Computing Engineering and Digital Technologies, Teesside University, Middlesbrough, UK 123 Y. Li et al. learning (DRL),

Journal

ComputingSpringer Journals

Published: Dec 23, 2019

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