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

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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.

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Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, UK

    Yuming Li & Pin Ni

  2. School of Computing Engineering and Digital Technologies, Teesside University, Middlesbrough, UK

    Victor Chang

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  1. Yuming Li
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  2. Pin Ni
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  3. Victor Chang
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Correspondence to Victor Chang.

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Li, Y., Ni, P. & Chang, V. Application of deep reinforcement learning in stock trading strategies and stock forecasting. Computing 102, 1305–1322 (2020). https://doi.org/10.1007/s00607-019-00773-w

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