Nikan Firoozye

Personal Details

First Name:	Nikan
Middle Name:
Last Name:	Firoozye
Suffix:
RePEc Short-ID:	pfi363
	[This author has chosen not to make the email address public]

Affiliation

Financial Computing and Analytics Group
University College London (UCL)

London, United Kingdom
https://www.ucl.ac.uk/computer-science/fca
RePEc:edi:fcucluk (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2022. "The Recurrent Reinforcement Learning Crypto Agent," Papers 2201.04699, arXiv.org, revised May 2022.

   Cited by:

   1. V. Lanzetta, 2024. "Transfer learning for financial data predictions: a systematic review," Papers 2409.17183, arXiv.org.

2. Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2021. "Reinforcement Learning for Systematic FX Trading," Papers 2110.04745, arXiv.org, revised May 2022.

   Cited by:

   1. Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2022. "The Recurrent Reinforcement Learning Crypto Agent," Papers 2201.04699, arXiv.org, revised May 2022.
   2. V. Lanzetta, 2024. "Transfer learning for financial data predictions: a systematic review," Papers 2409.17183, arXiv.org.

3. Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2021. "Online Learning with Radial Basis Function Networks," Papers 2103.08414, arXiv.org, revised Oct 2022.

   Cited by:

   1. Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2021. "Reinforcement Learning for Systematic FX Trading," Papers 2110.04745, arXiv.org, revised May 2022.

4. Adriano Koshiyama & Sebastian Flennerhag & Stefano B. Blumberg & Nick Firoozye & Philip Treleaven, 2020. "QuantNet: Transferring Learning Across Systematic Trading Strategies," Papers 2004.03445, arXiv.org, revised Jun 2020.

   Cited by:

   1. Elizabeth Fons & Paula Dawson & Xiao-jun Zeng & John Keane & Alexandros Iosifidis, 2020. "Augmenting transferred representations for stock classification," Papers 2011.04545, arXiv.org.
   2. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.
   3. Mostafa Shabani & Dat Thanh Tran & Juho Kanniainen & Alexandros Iosifidis, 2022. "Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification," Papers 2207.11577, arXiv.org.
   4. Gabriel Borrageiro & Nick Firoozye & Paolo Barucca, 2021. "Online Learning with Radial Basis Function Networks," Papers 2103.08414, arXiv.org, revised Oct 2022.

5. Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2019. "Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination," Papers 1901.01751, arXiv.org, revised Mar 2019.
   • Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2021. "Generative adversarial networks for financial trading strategies fine-tuning and combination," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 797-813, May.

   Cited by:

   1. Adriano Koshiyama & Sebastian Flennerhag & Stefano B. Blumberg & Nick Firoozye & Philip Treleaven, 2020. "QuantNet: Transferring Learning Across Systematic Trading Strategies," Papers 2004.03445, arXiv.org, revised Jun 2020.
   2. Szymon Kubiak & Tillman Weyde & Oleksandr Galkin & Dan Philps & Ram Gopal, 2023. "Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe," Papers 2311.16004, arXiv.org.
   3. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2019. "Quant GANs: Deep Generation of Financial Time Series," Papers 1907.06673, arXiv.org, revised Dec 2019.
   4. Chung I Lu & Julian Sester, 2024. "Generative model for financial time series trained with MMD using a signature kernel," Papers 2407.19848, arXiv.org, revised Jul 2024.
   5. Francesca Biagini & Lukas Gonon & Niklas Walter, 2024. "Universal randomised signatures for generative time series modelling," Papers 2406.10214, arXiv.org, revised Sep 2024.
   6. Xiaoyu Tan & Zili Zhang & Xuejun Zhao & Shuyi Wang, 2022. "DeepPricing: pricing convertible bonds based on financial time-series generative adversarial networks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-38, December.
   7. Alexandre Miot, 2020. "Adversarial trading," Papers 2101.03128, arXiv.org.
   8. Matteo Rizzato & Julien Wallart & Christophe Geissler & Nicolas Morizet & Noureddine Boumlaik, 2022. "Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation," Papers 2209.03935, arXiv.org, revised May 2024.
   9. Gautier Marti & Victor Goubet & Frank Nielsen, 2021. "cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope," Papers 2107.10606, arXiv.org.
   10. Gautier Marti, 2019. "CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks," Papers 1910.09504, arXiv.org, revised Dec 2019.
   11. Amine Assouel & Antoine Jacquier & Alexei Kondratyev, 2021. "A Quantum Generative Adversarial Network for distributions," Papers 2110.02742, arXiv.org.
   12. Rizzato, Matteo & Wallart, Julien & Geissler, Christophe & Morizet, Nicolas & Boumlaik, Noureddine, 2023. "Generative Adversarial Networks applied to synthetic financial scenarios generation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
   13. Song Wei & Andrea Coletta & Svitlana Vyetrenko & Tucker Balch, 2023. "INTAGS: Interactive Agent-Guided Simulation," Papers 2309.01784, arXiv.org, revised Nov 2023.
   14. Junyi Li & Xitong Wang & Yaoyang Lin & Arunesh Sinha & Micheal P. Wellman, 2020. "Generating Realistic Stock Market Order Streams," Papers 2006.04212, arXiv.org.
   15. Hans Buhler & Blanka Horvath & Terry Lyons & Imanol Perez Arribas & Ben Wood, 2020. "A Data-driven Market Simulator for Small Data Environments," Papers 2006.14498, arXiv.org.
   16. Emiel Lemahieu & Kris Boudt & Maarten Wyns, 2023. "Generating drawdown-realistic financial price paths using path signatures," Papers 2309.04507, arXiv.org.
   17. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2022. "Estimating risks of option books using neural-SDE market models," Papers 2202.07148, arXiv.org.
   18. Chung I Lu, 2023. "Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation," Papers 2307.07694, arXiv.org, revised Jul 2023.
   19. Edmond Lezmi & Jules Roche & Thierry Roncalli & Jiali Xu, 2020. "Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks," Papers 2007.04838, arXiv.org.
   20. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
   21. Sohyeon Kwon & Yongjae Lee, 2024. "Can GANs Learn the Stylized Facts of Financial Time Series?," Papers 2410.09850, arXiv.org.
   22. Magnus Wiese & Ben Wood & Alexandre Pachoud & Ralf Korn & Hans Buehler & Phillip Murray & Lianjun Bai, 2021. "Multi-Asset Spot and Option Market Simulation," Papers 2112.06823, arXiv.org.
   23. Carvajal-Patiño, Daniel & Ramos-Pollán, Raul, 2022. "Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies," Research in International Business and Finance, Elsevier, vol. 62(C).
   24. Magnus Wiese & Lianjun Bai & Ben Wood & Hans Buehler, 2019. "Deep Hedging: Learning to Simulate Equity Option Markets," Papers 1911.01700, arXiv.org.
   25. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.

6. Adriano Koshiyama & Nick Firoozye, 2019. "Avoiding Backtesting Overfitting by Covariance-Penalties: an empirical investigation of the ordinary and total least squares cases," Papers 1905.05023, arXiv.org.

   Cited by:

   1. Adriano Koshiyama & Sebastian Flennerhag & Stefano B. Blumberg & Nick Firoozye & Philip Treleaven, 2020. "QuantNet: Transferring Learning Across Systematic Trading Strategies," Papers 2004.03445, arXiv.org, revised Jun 2020.
   2. Nikan Firoozye & Vincent Tan & Stefan Zohren, 2022. "Canonical Portfolios: Optimal Asset and Signal Combination," Papers 2202.10817, arXiv.org, revised Jul 2023.
      • Firoozye, Nikan & Tan, Vincent & Zohren, Stefan, 2023. "Canonical portfolios: Optimal asset and signal combination," Journal of Banking & Finance, Elsevier, vol. 154(C).
   3. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.

Articles

1. Adriano Koshiyama & Stefano B. Blumberg & Nick Firoozye & Philip Treleaven & Sebastian Flennerhag, 2022. "QuantNet: transferring learning across trading strategies," Quantitative Finance, Taylor & Francis Journals, vol. 22(6), pages 1071-1090, June.

   Cited by:

   1. Kleyton da Costa, 2023. "Anomaly Detection in Global Financial Markets with Graph Neural Networks and Nonextensive Entropy," Papers 2308.02914, arXiv.org, revised Aug 2023.

2. Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2021. "Generative adversarial networks for financial trading strategies fine-tuning and combination," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 797-813, May.
   • Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2019. "Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination," Papers 1901.01751, arXiv.org, revised Mar 2019.
   See citations under working paper version above.

Books

Sorry, no citations of books recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 8 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-BIG: Big Data (4) 2018-10-22 2019-01-14 2020-04-13 2021-03-29. Author is listed
2. NEP-CMP: Computational Economics (4) 2019-01-14 2020-04-13 2021-10-18 2022-01-31. Author is listed
3. NEP-CWA: Central and Western Asia (2) 2021-10-18 2022-03-28. Author is listed
4. NEP-BEC: Business Economics (1) 2019-05-20
5. NEP-FOR: Forecasting (1) 2021-03-29
6. NEP-MST: Market Microstructure (1) 2020-04-13
7. NEP-PAY: Payment Systems and Financial Technology (1) 2022-01-31
8. NEP-UPT: Utility Models and Prospect Theory (1) 2021-10-18

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