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- W4285302016 abstract "Investors must devise an effective stock trading plan. The stock market is complex and dynamic, making it difficult to find an optimal approach. A reinforcement learning strategy is used because it can manage domains with stochastic transitions without a model. This work will investigate how RL may be used to maximise market returns whilst keeping the human element in mind. The learning agent is trained on stock prices and public interest patterns collected from Google Trends. A convolution neural network is trained on stock data and Google Trends using deep Q-learning (DQN). This paper investigates the use of graphical social media trends to train a reinforcement learning agent for stock trading. The agent’s performance was tested using a variety of stocks and phrases. Preliminary results demonstrate the agent performs effectively when trained on associated positive data sets." @default.
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- W4285302016 date "2022-01-01" @default.
- W4285302016 modified "2023-09-29" @default.
- W4285302016 title "Convolution Neural Network (CNN) Based Deep Q-Learning to Maximise the Returns from Stock Market" @default.
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- W4285302016 doi "https://doi.org/10.1007/978-981-19-1559-8_16" @default.
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