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Modeling financial time-series with generative adversarial networks

Citations

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Cited by:

  1. 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.
  2. Igor Sadoune & Marcelin Joanis & Andrea Lodi, 2025. "Implementing a Hierarchical Deep Learning Approach for Simulating Multilevel Auction Data," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2029-2056, April.
  3. Çelik, Gaffari & Talu, Muhammed Fatih, 2020. "Resizing and cleaning of histopathological images using generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
  4. 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.
  5. 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.
  6. Jungsik Hwang, 2020. "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States," Papers 2007.06848, arXiv.org.
  7. Jos'e-Manuel Pe~na & Fernando Su'arez & Omar Larr'e & Domingo Ram'irez & Arturo Cifuentes, 2023. "A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation," Papers 2302.02269, arXiv.org, revised May 2024.
  8. 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.
  9. Jun Lu & Danny Ding, 2022. "A Hybrid Approach on Conditional GAN for Portfolio Analysis," Papers 2208.07159, arXiv.org.
  10. Abolfazl Saghafi & Maryam Bagherian & Farhad Shokoohi, 2025. "Forecasting Forex EUR/USD Closing Prices Using a Dual-Input Deep Learning Model with Technical and Fundamental Indicators," Mathematics, MDPI, vol. 13(9), pages 1-17, April.
  11. Seyed Mohammad Sina Seyfi & Azin Sharifi & Hamidreza Arian, 2020. "Portfolio Risk Measurement Using a Mixture Simulation Approach," Papers 2011.07994, arXiv.org.
  12. Rama Cont & Mihai Cucuringu & Renyuan Xu & Chao Zhang, 2022. "Tail-GAN: Learning to Simulate Tail Risk Scenarios," Papers 2203.01664, arXiv.org, revised May 2025.
  13. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Papers 2207.05701, arXiv.org.
  14. Ye-Sheen Lim & Denise Gorse, 2021. "Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow," Papers 2109.13905, arXiv.org.
  15. Sohyeon Kwon & Yongjae Lee, 2024. "Can GANs Learn the Stylized Facts of Financial Time Series?," Papers 2410.09850, arXiv.org.
  16. 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).
  17. Andrea Di Iura & Giulia Terenzi, 2022. "A Bayesian analysis of gain-loss asymmetry," SN Business & Economics, Springer, vol. 2(5), pages 1-23, May.
  18. Magnus Wiese & Lianjun Bai & Ben Wood & Hans Buehler, 2019. "Deep Hedging: Learning to Simulate Equity Option Markets," Papers 1911.01700, arXiv.org.
  19. Zhang, Cheng-Jun & Zhu, Xue-lian & Yu, Wen-bin & Liu, Jin & Chen, Ya-dang & Yao, Yu & Wang, Su-xun, 2024. "Predicting popularity of online products via collective recommendations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
  20. Andrea Coletta & Matteo Prata & Michele Conti & Emanuele Mercanti & Novella Bartolini & Aymeric Moulin & Svitlana Vyetrenko & Tucker Balch, 2021. "Towards Realistic Market Simulations: a Generative Adversarial Networks Approach," Papers 2110.13287, arXiv.org.
  21. Haochong Xia & Shuo Sun & Xinrun Wang & Bo An, 2023. "Market-GAN: Adding Control to Financial Market Data Generation with Semantic Context," Papers 2309.07708, arXiv.org, revised Feb 2024.
  22. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
  23. Shangguan, Anqi & Xie, Guo & Fei, Rong & Mu, Lingxia & Hei, Xinhong, 2023. "Train wheel degradation generation and prediction based on the time series generation adversarial network," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  24. repec:hal:wpaper:hal-03716692 is not listed on IDEAS
  25. David Kuo Chuen Lee & Chong Guan & Yinghui Yu & Qinxu Ding, 2024. "A Comprehensive Review of Generative AI in Finance," FinTech, MDPI, vol. 3(3), pages 1-19, September.
  26. Francesca Biagini & Lukas Gonon & Niklas Walter, 2024. "Universal randomised signatures for generative time series modelling," Papers 2406.10214, arXiv.org, revised Sep 2024.
  27. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Applied Economics and Finance, Redfame publishing, vol. 9(3), pages 55-68, August.
  28. Gautier Marti & Victor Goubet & Frank Nielsen, 2021. "cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope," Papers 2107.10606, arXiv.org.
  29. Seyfi, Seyed Mohammad Sina & Sharifi, Azin & Arian, Hamidreza, 2021. "Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1056-1079.
  30. 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).
  31. Weilong Fu & Ali Hirsa & Jorg Osterrieder, 2022. "Simulating financial time series using attention," Papers 2207.00493, arXiv.org.
  32. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2022. "Estimating risks of option books using neural-SDE market models," Papers 2202.07148, arXiv.org.
  33. Jingyi Gu & Fadi P. Deek & Guiling Wang, 2023. "Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network," Papers 2302.14164, arXiv.org.
  34. Andrea Giuseppe Di Iura & Giulia Terenzi, 2021. "A Bayesian analysis of gain-loss asymmetry," Papers 2104.06044, arXiv.org.
  35. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
  36. Otto M. Pires & Mauro Q. Nooblath & Yan Alef C. Silva & Maria Heloísa F. Silva & Lucas Q. Galvão & Anton S. Albino, 2024. "Synthetic data generation with hybrid quantum-classical models for the financial sector," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(11), pages 1-11, November.
  37. Haoyang Cao & Xin Guo, 2021. "Generative Adversarial Network: Some Analytical Perspectives," Papers 2104.12210, arXiv.org, revised Sep 2021.
  38. Sergiy Tkachuk & Szymon {L}ukasik & Anna Wr'oblewska, 2024. "Consumer Transactions Simulation through Generative Adversarial Networks," Papers 2408.03655, arXiv.org.
  39. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.
  40. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
  41. Bate He & Eisuke Kita, 2021. "The Application of Sequential Generative Adversarial Networks for Stock Price Prediction," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 455-470, November.
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