December 2023
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18 Reads
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22 Citations
Expert Systems with Applications
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December 2023
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18 Reads
·
22 Citations
Expert Systems with Applications
December 2022
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67 Reads
More and more stock trading strategies are constructed using deep reinforcement learning (DRL) algorithms, but DRL methods originally widely used in the gaming community are not directly adaptable to financial data with low signal-to-noise ratios and unevenness, and thus suffer from performance shortcomings. In this paper, to capture the hidden information, we propose a DRL based stock trading system using cascaded LSTM, which first uses LSTM to extract the time-series features from stock daily data, and then the features extracted are fed to the agent for training, while the strategy functions in reinforcement learning also use another LSTM for training. Experiments in DJI in the US market and SSE50 in the Chinese stock market show that our model outperforms previous baseline models in terms of cumulative returns and Sharp ratio, and this advantage is more significant in the Chinese stock market, a merging market. It indicates that our proposed method is a promising way to build a automated stock trading system.
... Finally, some libraries have been developed for use in financial reinforcement learning studies, and several of these libraries have been made available as open-source tools [56]. Another study proposes a DRL-based trading system employing a cascaded LSTM-PPO model to better capture hidden information in daily stock data, achieving 5% to 52% performance improvements over baseline models in metrics like cumulative returns and profitability across major indices including DJI, SSE50, SENSEX, and FTSE100 [57]. In the literature, a modified actor-critic RL model integrating technical analysis metrics to address multidimensional noise and transaction costs has been shown to outperform pure RL and traditional benchmarks on the S&P500 dataset [58]. ...
December 2023
Expert Systems with Applications