Li, Yuming;
Ni, Pin;
Chang, Victor;
(2019)
An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model.
In: Muñoz, Victor Méndez and Firouzi, Farshad and Estrada, Ernesto and Chang, Victor, (eds.)
Proceedings of the 4th International Conference on Complexity, Future Information Systems and Risk COMPLEXIS.
(pp. pp. 52-58).
SciTePress: Setúbal, Portugal.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | An Empirical Research on the Investment Strategy of Stock Market based on Deep Reinforcement Learning model |
| Event: | 4th International Conference on Complexity, Future Information Systems and Risk |
| Location: | Heraklion, GREECE |
| Dates: | 2 May 2019 - 4 May 2019 |
| ISBN-13: | 978-989-758-366-7 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5220/0007722000520058 |
| Publisher version: | https://doi.org/10.5220/0007722000520058 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Deep Reinforcement Learning (DRL), Stock Market Strategy, Deep Q-Network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS 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 |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159893 |
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