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Article summary:
1. This paper proposes a novel algorithmic trading model based on recurrent reinforcement learning, optimized for making consecutive trading signals.
2. The proposed model incorporates a hybrid learning loss and self-attention mechanism to allow sequences of hidden features for reinforcement learning to contain the original state’s characteristics fully.
3. The proposed model is verified using major market indices and representative stocks in each sector of S&P500, showing superior performance over well-known baseline benchmark models.
Article analysis:
This article presents a novel algorithmic trading model based on recurrent reinforcement learning with hybrid loss and self-attention mechanism for generating trading signals. The authors claim that their proposed model has superior performance over well-known baseline benchmark models, which is verified using major market indices and representative stocks in each sector of S&P500.
The article appears to be reliable as it provides evidence for its claims through experiments conducted on real data sets. However, there are some potential biases that should be noted. Firstly, the authors do not provide any information about the data sets used in their experiments or how they were collected, which could lead to bias in the results due to selection bias or other factors. Secondly, the authors do not discuss any possible risks associated with their proposed model or any counterarguments that could be made against it, which could lead to an incomplete understanding of its implications and potential drawbacks. Finally, the article does not present both sides equally; instead it focuses solely on promoting its own proposed model without exploring alternative approaches or solutions.