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 th...

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Veröffentlicht in:Computing Jg. 102; H. 6; S. 1305 - 1322
Hauptverfasser: Li, Yuming, Ni, Pin, Chang, Victor
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 01.06.2020
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Decision making
Decision theory
Deep learning
Economic forecasting
Information Systems Applications (incl.Internet)
Mathematical models
Securities markets
Signal processing
Software Engineering
68T01
Deep Q learning
91G10
Financial strategy
Reinforcement learning
ISSN:0010-485X, 1436-5057
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-019-00773-w