Li, Yuming;
Ni, Pin;
Chang, Victor;
(2020)
Application of deep reinforcement learning in stock trading strategies and stock forecasting.
Computing
, 102
(6)
pp. 1305-1322.
10.1007/s00607-019-00773-w.
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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.
| Type: | Article |
|---|---|
| Title: | Application of deep reinforcement learning in stock trading strategies and stock forecasting |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s00607-019-00773-w |
| Publisher version: | https://doi.org/10.1007/s00607-019-00773-w |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Theory & Methods, Computer Science, Reinforcement learning, Financial strategy, Deep Q learning, OPTIMIZATION |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10157990 |
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