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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Quant GANs: deep generation of financial time series
Magnus Wiese, Robert Knobloch, Ralf Korn, et al.
Quantitative Finance (2020) Vol. 20, Iss. 9, pp. 1419-1440
Open Access | Times Cited: 62

Showing 1-25 of 62 citing articles:

An Introduction to Deep Reinforcement Learning
Vincent François-Lavet, Peter Henderson, Riashat Islam, et al.
Foundations and Trends® in Machine Learning (2018) Vol. 11, Iss. 3-4, pp. 219-354
Open Access | Times Cited: 897
Neural Networks for Option Pricing and Hedging: A Literature Review
Johannes Ruf, Weiguan Wang
SSRN Electronic Journal (2019)
Open Access | Times Cited: 61
Generative adversarial networks for financial trading strategies fine-tuning and combination
Adriano Koshiyama, Nick Firoozye, Philip Treleaven
Quantitative Finance (2020) Vol. 21, Iss. 5, pp. 797-813
Open Access | Times Cited: 60
Generative adversarial networks in time series: A survey and taxonomy
Eoin Brophy, Zhengwei Wang, Qi She, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 43
TCGAN: Convolutional Generative Adversarial Network for time series classification and clustering
Fanling Huang, Yangdong Deng
Neural Networks (2023) Vol. 165, pp. 868-883
Open Access | Times Cited: 19
Generative Adversarial Networks in finance: an overview
Florian Eckerli, Joerg Osterrieder
SSRN Electronic Journal (2021)
Open Access | Times Cited: 40
Sig-wasserstein GANs for time series generation
Hao Ni, Łukasz Szpruch, Marc Sabate-Vidales, et al.
(2021), pp. 1-8
Open Access | Times Cited: 34
Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
R. Bhushan Gopaluni, Aditya Tulsyan, Benoît Chachuat, et al.
IFAC-PapersOnLine (2020) Vol. 53, Iss. 2, pp. 218-229
Open Access | Times Cited: 34
Generative AI: Overview, Economic Impact, and Applications in Asset Management
Martin Luk
SSRN Electronic Journal (2023)
Closed Access | Times Cited: 11
If You Like It, GAN It—Probabilistic Multivariate Times Series Forecast with GAN
Alireza Koochali, Andreas Dengel, Sheraz Ahmed
(2021), pp. 40-40
Open Access | Times Cited: 26
Interpretable Machine Learning for Diversified Portfolio Construction
Markus Jaeger, Stephan Krügel, Dimitri Marinelli, et al.
The Journal of Financial Data Science (2021) Vol. 3, Iss. 3, pp. 31-51
Open Access | Times Cited: 24
Deep Hedging under Rough Volatility
Blanka Horvath, Josef Teichmann, Žan Žurič
Risks (2021) Vol. 9, Iss. 7, pp. 138-138
Open Access | Times Cited: 20
Deep Hedging of Derivatives Using Reinforcement Learning
Jay Cao, Jacky Chen, John Hull, et al.
The Journal of Financial Data Science (2020) Vol. 3, Iss. 1, pp. 10-27
Open Access | Times Cited: 21
Generative Adversarial Networks applied to synthetic financial scenarios generation
Matteo Rizzato, Julien Wallart, Christophe Geissler, et al.
Physica A Statistical Mechanics and its Applications (2023) Vol. 623, pp. 128899-128899
Open Access | Times Cited: 7
Algorithms in future capital markets
Adriano Koshiyama, Nick Firoozye, Philip Treleaven
(2020), pp. 1-8
Open Access | Times Cited: 20
Multivariate Time Series Synthesis Using Generative Adversarial Networks
Mark Leznik, Patrick Michalsky, Peter Willis, et al.
(2021)
Open Access | Times Cited: 16
TSGBench: Time Series Generation Benchmark
Yihao Ang, Qiang Huang, Yifan Bao, et al.
Proceedings of the VLDB Endowment (2023) Vol. 17, Iss. 3, pp. 305-318
Closed Access | Times Cited: 6
Connecting GANs, MFGs, and OT
Haoyang Cao, Xin Guo, Mathieu Laurière
arXiv (Cornell University) (2020)
Open Access | Times Cited: 16
Algorithms in Future Capital Markets
Adriano Koshiyama, Nick Firoozye, Philip Treleaven
SSRN Electronic Journal (2020)
Closed Access | Times Cited: 15
Conditional Loss and Deep Euler Scheme for Time Series Generation
Carl Remlinger, Joseph Mikael, Romuald Élie
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 7, pp. 8098-8105
Open Access | Times Cited: 9
A generative model of a limit order book using recurrent neural networks
Hanna Hultin, Henrik Hult, Alexandre Proutière, et al.
Quantitative Finance (2023) Vol. 23, Iss. 6, pp. 931-958
Open Access | Times Cited: 5
Reinforcement Learning in Economics and Finance.
Arthur Charpentier, Romuald Élie, Carl Remlinger
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 13
Delta Hedging of Derivatives using Deep Reinforcement Learning
Alexandru Giurca, Svetlana Borovkova
SSRN Electronic Journal (2021)
Closed Access | Times Cited: 10
Time series (re)sampling using Generative Adversarial Networks
Christian M. Dahl, Emil N. Sørensen
Neural Networks (2022) Vol. 156, pp. 95-107
Open Access | Times Cited: 7
Generating Realistic Stock Market Order Streams
Junyi Li, Xitong Wang, Yaoyang Lin, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 10
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