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Deep reinforcement learning for stock prediction
- 1 The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong,China
* Author to whom correspondence should be addressed.
Abstract
Due to its chaotic and high volatility character, as well as many other uncertainties from reality, predicting the price of stock market is always a challenging goal to achieve. Due to those characteristics mentioned above, it could be regarded as a classification problem, and then many methods using different machine learning tools could be well applied to solve the challenging problem. Within these methods, deep neural network is a popular and highly noticed one in recent years. This is mainly because of its unique advantages compared to the more conventional machine learning methods, the highly complex nonlienearity and deep nonlinear topologies, to appropriately describe the complex situations. Later, after adding advantages of reinforcement learning to enable the model of the advantages to improve feature dimensions, the deep reinforcement learning method is well proposed to improve the performance. Deep reinforcement learning is a method to combine the advantages of deep learning and the advantages of reinforcement learning, and this paper will discuss its characters and advantages, and finally talk about its limitations and future development.
Keywords
Stock prediction, Deep neural network, Reinforcement learning, Deep reinforcement learning
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Cite this article
Wang,M. (2024). Deep reinforcement learning for stock prediction. Applied and Computational Engineering,69,85-90.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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