IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2007.04838.html
   My bibliography  Save this paper

Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks

Author

Listed:
  • Edmond Lezmi
  • Jules Roche
  • Thierry Roncalli
  • Jiali Xu

Abstract

This article explores the use of machine learning models to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets. In particular, these synthetic data must preserve the probability distribution of asset returns, the stochastic dependence between the different assets and the autocorrelation across time. The article proposes then a new approach for estimating the probability distribution of backtest statistics. The final objective is to develop a framework for improving the risk management of quantitative investment strategies, in particular in the space of smart beta, factor investing and alternative risk premia.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2007.04838
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2007.04838
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
    3. Kaminski, Kathryn M. & Lo, Andrew W., 2014. "When do stop-loss rules stop losses?," Journal of Financial Markets, Elsevier, vol. 18(C), pages 234-254.
    4. Giovanni Mariani & Yada Zhu & Jianbo Li & Florian Scheidegger & Roxana Istrate & Costas Bekas & A. Cristiano I. Malossi, 2019. "PAGAN: Portfolio Analysis with Generative Adversarial Networks," Papers 1909.10578, arXiv.org.
    5. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Solveig Flaig & Gero Junike, 2021. "Scenario generation for market risk models using generative neural networks," Papers 2109.10072, arXiv.org, revised Aug 2023.
    2. Adil Rengim Cetingoz & Charles-Albert Lehalle, 2025. "Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance," Papers 2501.03993, arXiv.org, revised Jan 2025.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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 Dec 2024.
    2. Emiel Lemahieu & Kris Boudt & Maarten Wyns, 2023. "Generating drawdown-realistic financial price paths using path signatures," Papers 2309.04507, arXiv.org.
    3. 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.
    4. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.
    5. Alexandre Miot, 2020. "Adversarial trading," Papers 2101.03128, arXiv.org.
    6. 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.
    7. 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.
    8. Francesca Biagini & Lukas Gonon & Niklas Walter, 2024. "Universal randomised signatures for generative time series modelling," Papers 2406.10214, arXiv.org, revised Sep 2024.
    9. Weilong Fu & Ali Hirsa & Jorg Osterrieder, 2022. "Simulating financial time series using attention," Papers 2207.00493, arXiv.org.
    10. 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.
    11. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Papers 2207.05701, arXiv.org.
    12. Sohyeon Kwon & Yongjae Lee, 2024. "Can GANs Learn the Stylized Facts of Financial Time Series?," Papers 2410.09850, arXiv.org.
    13. 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).
    14. Adil Rengim Cetingoz & Charles-Albert Lehalle, 2025. "Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance," Papers 2501.03993, arXiv.org, revised Jan 2025.
    15. Jessica James & Louis Yang, 2010. "Stop-losses, maximum drawdown-at-risk and replicating financial time series with the stationary bootstrap," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 1-12.
    16. Song Wei & Andrea Coletta & Svitlana Vyetrenko & Tucker Balch, 2023. "INTAGS: Interactive Agent-Guided Simulation," Papers 2309.01784, arXiv.org, revised Nov 2023.
    17. Rama Cont & Mihai Cucuringu & Renyuan Xu & Chao Zhang, 2022. "Tail-GAN: Learning to Simulate Tail Risk Scenarios," Papers 2203.01664, arXiv.org, revised Mar 2023.
    18. Amine Assouel & Antoine Jacquier & Alexei Kondratyev, 2021. "A Quantum Generative Adversarial Network for distributions," Papers 2110.02742, arXiv.org.
    19. Chung I Lu, 2023. "Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation," Papers 2307.07694, arXiv.org, revised Jul 2023.
    20. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2007.04838. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.