IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2204.13587.html
   My bibliography  Save this paper

Supervised machine learning classification for short straddles on the S&P500

Author

Listed:
  • Alexander Brunhuemer
  • Lukas Larcher
  • Philipp Seidl
  • Sascha Desmettre
  • Johannes Kofler
  • Gerhard Larcher

Abstract

In this working paper we present our current progress in the training of machine learning models to execute short option strategies on the S&P500. As a first step, this paper is breaking this problem down to a supervised classification task to decide if a short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview over our evaluation metrics on different classification models. In this preliminary work, using standard machine learning techniques and without hyperparameter search, we find no statistically significant outperformance to a simple "trade always" strategy, but gain additional insights on how we could proceed in further experiments.

Suggested Citation

  • Alexander Brunhuemer & Lukas Larcher & Philipp Seidl & Sascha Desmettre & Johannes Kofler & Gerhard Larcher, 2022. "Supervised machine learning classification for short straddles on the S&P500," Papers 2204.13587, arXiv.org.
    • Handle: RePEc:arx:papers:2204.13587
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2204.13587
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Day, Theodore E & Lewis, Craig M, 1997. "Initial Margin Policy and Stochastic Volatility in the Crude Oil Futures Market," The Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 303-332.
    2. Peter Carr & Liuren Wu & Zhibai Zhang, 2019. "Using Machine Learning to Predict Realized Variance," Papers 1909.10035, arXiv.org.
    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. Shi, Wei & Irwin, Scott H., 2006. "What Happens when Peter can't Pay Paul: Risk Management at Futures Exchange Clearinghouses," 2006 Annual meeting, July 23-26, Long Beach, CA 21087, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    3. Paul Kupiec, 1998. "Margin Requirements, Volatility, and Market Integrity: What Have We Learned Since the Crash?," Journal of Financial Services Research, Springer;Western Finance Association, vol. 13(3), pages 231-255, June.
    4. Tibor Neugebauer & Sascha Füllbrunn, 2013. "Deflating Bubbles in Experimental Asset Markets: Comparative Statics of Margin Regulations," LSF Research Working Paper Series 13-14, Luxembourg School of Finance, University of Luxembourg.
    5. Alexander, Carol & Kaeck, Andreas & Sumawong, Anannit, 2019. "A parsimonious parametric model for generating margin requirements for futures," European Journal of Operational Research, Elsevier, vol. 273(1), pages 31-43.
    6. Apostolos Serletis & Asghar Shahmoradi, 2007. "Returns and Volatility in the NYMEX Henry Hub Natural Gas Futures Market," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 15, pages 193-204, World Scientific Publishing Co. Pte. Ltd..
    7. Michael Grill & Karl Schmedders & Felix Kubler & Johannes Brumm, 2012. "Margin Requirements and Asset Prices," 2012 Meeting Papers 533, Society for Economic Dynamics.
    8. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2021. "A machine learning approach to volatility forecasting," CREATES Research Papers 2021-03, Department of Economics and Business Economics, Aarhus University.
    9. Sascha Füllbrunn & Tibor Neugebauer, 2012. "Margin Trading Bans in Experimental Asset Markets," Jena Economics Research Papers 2012-058, Friedrich-Schiller-University Jena.
    10. Yue Zhuo & Takayuki Morimoto, 2024. "A Hybrid Model for Forecasting Realized Volatility Based on Heterogeneous Autoregressive Model and Support Vector Regression," Risks, MDPI, vol. 12(1), pages 1-16, January.
    11. Stan Miles, 2013. "Constant-collateral pyramiding trading strategies in futures markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 27(4), pages 381-396, December.
    12. Tehseen Mazhar & Rizwana Naz Asif & Muhammad Amir Malik & Muhammad Asgher Nadeem & Inayatul Haq & Muhammad Iqbal & Muhammad Kamran & Shahzad Ashraf, 2023. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods," Sustainability, MDPI, vol. 15(3), pages 1-26, February.

    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:2204.13587. 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.