IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2412.02660.html
   My bibliography  Save this paper

A Markowitz Approach to Managing a Dynamic Basket of Moving-Band Statistical Arbitrages

Author

Listed:
  • Kasper Johansson
  • Thomas Schmelzer
  • Stephen Boyd

Abstract

We consider the problem of managing a portfolio of moving-band statistical arbitrages (MBSAs), inspired by the Markowitz optimization framework. We show how to manage a dynamic basket of MBSAs, and illustrate the method on recent historical data, showing that it can perform very well in terms of risk-adjusted return, essentially uncorrelated with the market.

Suggested Citation

  • Kasper Johansson & Thomas Schmelzer & Stephen Boyd, 2024. "A Markowitz Approach to Managing a Dynamic Basket of Moving-Band Statistical Arbitrages," Papers 2412.02660, arXiv.org.
  • Handle: RePEc:arx:papers:2412.02660
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2412.02660
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuji Yamada & James A. Primbs, 2018. "Model Predictive Control for Optimal Pairs Trading Portfolio with Gross Exposure and Transaction Cost Constraints," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 25(1), pages 1-21, March.
    2. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    3. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    4. Geczy, Christopher C. & Musto, David K. & Reed, Adam V., 2002. "Stocks are special too: an analysis of the equity lending market," Journal of Financial Economics, Elsevier, vol. 66(2-3), pages 241-269.
    5. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
    6. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    7. Dongcheol Kim & Byeung‐Joo Lee, 2023. "Shorting costs and profitability of long–short strategies," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 277-316, March.
    8. João Frois Caldeira & Gulherme Valle Moura, 2013. "Selection of a Portfolio of Pairs Based on Cointegration: A Statistical Arbitrage Strategy," Brazilian Review of Finance, Brazilian Society of Finance, vol. 11(1), pages 49-80.
    9. Thomas Günter Fischer & Christopher Krauss & Alexander Deinert, 2019. "Statistical Arbitrage in Cryptocurrency Markets," JRFM, MDPI, vol. 12(1), pages 1-15, February.
    10. Nicolas Huck, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," Post-Print hal-02143971, HAL.
    11. Christopher Krauss, 2017. "Statistical Arbitrage Pairs Trading Strategies: Review And Outlook," Journal of Economic Surveys, Wiley Blackwell, vol. 31(2), pages 513-545, April.
    12. Marco Avellaneda & Jeong-Hyun Lee, 2010. "Statistical arbitrage in the US equities market," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 761-782.
    13. Johansen, Soren, 2000. "Modelling of cointegration in the vector autoregressive model," Economic Modelling, Elsevier, vol. 17(3), pages 359-373, August.
    14. Tadahiro Nakajima, 2019. "Expectations for Statistical Arbitrage in Energy Futures Markets," JRFM, MDPI, vol. 12(1), pages 1-12, January.
    15. James A. Primbs & Yuji Yamada, 2018. "Pairs trading under transaction costs using model predictive control," Quantitative Finance, Taylor & Francis Journals, vol. 18(6), pages 885-895, June.
    Full references (including those not matched with items on IDEAS)

    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. Kasper Johansson & Thomas Schmelzer & Stephen Boyd, 2024. "Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization," Papers 2402.08108, arXiv.org.
    2. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    3. Han, Chulwoo & He, Zhaodong & Toh, Alenson Jun Wei, 2023. "Pairs trading via unsupervised learning," European Journal of Operational Research, Elsevier, vol. 307(2), pages 929-947.
    4. Fabian Waldow & Matthias Schnaubelt & Christopher Krauss & Thomas Günter Fischer, 2021. "Machine Learning in Futures Markets," JRFM, MDPI, vol. 14(3), pages 1-14, March.
    5. Schnaubelt, Matthias & Fischer, Thomas G. & Krauss, Christopher, 2020. "Separating the signal from the noise – Financial machine learning for Twitter," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    6. Ziping Zhao & Rui Zhou & Zhongju Wang & Daniel P. Palomar, 2018. "Optimal Portfolio Design for Statistical Arbitrage in Finance," Papers 1803.02974, arXiv.org.
    7. Clegg, Matthew & Krauss, Christopher, 2016. "Pairs trading with partial cointegration," FAU Discussion Papers in Economics 05/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    8. Erdinc Akyildirim & Ahmet Goncu & Alper Hekimoglu & Duc Khuong Nguyen & Ahmet Sensoy, 2023. "Statistical arbitrage: factor investing approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(4), pages 1295-1331, December.
    9. Law, K.F. & Li, W.K. & Yu, Philip L.H., 2018. "A single-stage approach for cointegration-based pairs trading," Finance Research Letters, Elsevier, vol. 26(C), pages 177-184.
    10. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
    11. Ariel Neufeld & Julian Sester & Daiying Yin, 2022. "Detecting data-driven robust statistical arbitrage strategies with deep neural networks," Papers 2203.03179, arXiv.org, revised Feb 2024.
    12. Stübinger, Johannes & Endres, Sylvia, 2017. "Pairs trading with a mean-reverting jump-diffusion model on high-frequency data," FAU Discussion Papers in Economics 10/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    13. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    14. Marianna Brunetti & Roberta De Luca, 2023. "Pairs trading in the index options market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 145-173, March.
    15. Rama Cont & Mihai Cucuringu & Chao Zhang, 2021. "Cross-Impact of Order Flow Imbalance in Equity Markets," Papers 2112.13213, arXiv.org, revised Jun 2023.
    16. Marianna Brunetti & Roberta De Luca, 2023. "Pre-selection in cointegration-based pairs trading," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1611-1640, December.
    17. Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
    18. Ziping Zhao & Daniel P. Palomar, 2017. "Mean-Reverting Portfolio Design with Budget Constraint," Papers 1701.05016, arXiv.org.
    19. Tian-Shyr Dai & Yi-Jen Luo & Hao-Han Chang & Chu-Lan Kao & Kuan-Lun Wang & Liang-Chih Liu, 2024. "Asymptotic analyses for trend-stationary pairs trading strategy in high-frequency trading," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1391-1411, November.
    20. Fernando Caneo & Werner Kristjanpoller, 2021. "Improving statistical arbitrage investment strategy: Evidence from Latin American stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4424-4440, July.

    More about this item

    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:2412.02660. 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.