Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness-Reference-Cited by-同舟云学术

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Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness

Published:2023-11-25 Issue: Volume: Page:
ISSN:
Container-title:4th ACM International Conference on AI in Finance
language:
Short-container-title:

Author:

Coletta Andrea¹^ORCID,Jerome Joseph²^ORCID,Savani Rahul³^ORCID,Vyetrenko Svitlana⁴^ORCID

Affiliation:

1. J.P. Morgan AI Research, United Kingdom

2. Computer Science, University of Liverpool, United Kingdom

3. Department of Computer Science, University of Liverpool, United Kingdom and The Alan Turing Institute, United Kingdom

4. J.P. Morgan AI Research, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604237.3626854

Reference56 articles.

1. Martin Arjovsky , Soumith Chintala , and Léon Bottou . 2017 . Wasserstein generative adversarial networks . In Proc. of ICML. Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein generative adversarial networks. In Proc. of ICML.

2. Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization

3. Tucker Hybinette Balch , Mahmoud Mahfouz , Joshua Lockhart , Maria Hybinette , and David Byrd . 2019. How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?arXiv:1906.12010 ( 2019 ). Tucker Hybinette Balch, Mahmoud Mahfouz, Joshua Lockhart, Maria Hybinette, and David Byrd. 2019. How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?arXiv:1906.12010 (2019).

4. Evasion Attacks against Machine Learning at Test Time

5. Pros and cons of GAN evaluation measures

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