Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: Authors have given an idea to of using chaotic neural networks, transformers, and deep reinforcement learning (DRL) to learn trading policies.
strengths_and_weaknesses: Strong points:
1. Authors idea is to merge DRL, transformers and chaotic activations in predicting stocks as well its trade size and price.\

Weakness:
1. There is no proper block diagram have been shown how the authors are planning to achieve their objective.
2. Idea seems interesting but there is nothing has been mentioned about data, experimental setup, codes locations, which makes it difficult to understand how the idea can be reproduced by the others as well as what are the findings user themselves after experimenting.

suggested_changes: 1. Please add the URM statement before references.
2. As idea seems to be novel but there is no proof has been given to verify its validity. Therefore, authors can give first a small block diagram to explain the components of their idea in brief. Later on, authors can explain the findings of their results obtained using their methods through one figure or table.
3. Finally authors should share the data as well as code repository for other users to reproduce the results.
review_title: null

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: The paper considers identifying profitable trading strategies by leveraging chaotic transformers to train on historical financial data while avoid overfitting.
strengths_and_weaknesses: The article proposes the use of Chaotic Transformers for identifying profitable trading strategies that could avoid overfitting.
 
Strengths:
- The authors propose the combination of (1) the chaotic activation function, (2) the transformers architecture, (3) deep reinforcement learning-based trading algorithms, (4) newer datasets for training, and (5) developments in time-series data. Given that these methods and ideas perform well individually, the authors believe that combining these approaches can lead to a superior and profitable trading strategy.

Weaknesses:
- The article lacks significant details about the proposed algorithm. In particular, exact system diagrams, experiments as well as comparisons with baselines are missing.
- Relevant literature analysis and intuition to build the new diagram are missing as well.
- Overall, without these details, it is hard to evaluate the proposed technique.
suggested_changes: - The article proposes a new idea and architecture. However, it does not include the exact details about how the various components are being mixed together. Due to the lack of information about how these components interact, it is hard to evaluate the newly proposed approach. Adding a system diagram will aid in understanding the direct contribution of this paper.
- Experimentation and comparisons are required to showcase the efficacy of the newly developed trading strategies. Baselines as well as relevant hyperparameter tuning will aid in understanding the methodology better. The computational cost of the new algorithm should be included as well.
- Relevant literature to support the development of such an algorithm should be added. It could provide the motivation for studying the new algorithm.
review_title: null

Decision: Invite to revise