On Correlated Stock Market Time Series Generation

Author:

Masi Giuseppe1ORCID,Prata Matteo1ORCID,Conti Michele1ORCID,Bartolini Novella1ORCID,Vyetrenko Svitlana2ORCID

Affiliation:

1. Sapienza University of Rome, IT

2. J. P. Morgan Chase, US

Funder

JPMorgan Chase AI Research Faculty Award

Publisher

ACM

Reference30 articles.

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2. Martin Arjovsky , Soumith Chintala , and Léon Bottou . 2017 . Wasserstein Generative Adversarial Networks . In Proceedings of the 34th International Conference on Machine Learning. PMLR, 214–223 . Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein Generative Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning. PMLR, 214–223.

3. Shaojie Bai , J Zico Kolter , and Vladlen Koltun . 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 ( 2018 ). Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).

4. Investor Sentiment in the Stock Market

5. Aleksandar Bojchevski , Oleksandr Shchur , Daniel Zügner , and Stephan Günnemann . 2018 . NetGAN: Generating Graphs via Random Walks . In Proceedings of the 35th International Conference on Machine Learning. PMLR, 610–619 . Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann. 2018. NetGAN: Generating Graphs via Random Walks. In Proceedings of the 35th International Conference on Machine Learning. PMLR, 610–619.

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