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Quantitative Finance Volume 21, 2021 - Issue 5
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Research Papers

Generative adversarial networks for financial trading strategies fine-tuning and combination

Adriano KoshiyamaDepartment of Computer Science, University College London, LondonWC1E 6BT, UKCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-7536-1503View further author information
ORCID Icon,
Nick FiroozyeDepartment of Computer Science, University College London, LondonWC1E 6BT, UKORCID Iconhttps://orcid.org/0000-0002-6460-0406View further author information
ORCID Icon &
Philip TreleavenDepartment of Computer Science, University College London, LondonWC1E 6BT, UKView further author information
Pages 797-813 | Received 15 Jan 2020, Accepted 26 Jun 2020, Published online: 01 Sep 2020

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Giorgio Costa & Garud N. Iyengar. (2023) Distributionally robust end-to-end portfolio construction. Quantitative Finance 23:10, pages 1465-1482.
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Magnus Wiese, Robert Knobloch, Ralf Korn & Peter Kretschmer. (2020) Quant GANs: deep generation of financial time series. Quantitative Finance 20:9, pages 1419-1440.
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Ruixun Zhang, Chaoyi Zhao & Guanglian Lin. Interpretable image-based deep learning for price trend prediction in ETF markets. The European Journal of Finance 0:0, pages 1-29.
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Articles from other publishers (2)

Shuntaro Takahashi, Yu Chen & Kumiko Tanaka-Ishii. (2019) Modeling financial time-series with generative adversarial networks. Physica A: Statistical Mechanics and its Applications 527, pages 121261.
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Samuel N. Cohen, Derek Snow & Lukasz Szpruch. (2021) Black-Box Model Risk in Finance. SSRN Electronic Journal.
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