IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2010.13036.html
   My bibliography  Save this paper

Trading Strategies of a Leveraged ETF in a Continuous Double Auction Market Using an Agent-Based Simulation

Author

Listed:
  • Isao Yagi
  • Shunya Maruyama
  • Takanobu Mizuta

Abstract

A leveraged ETF is a fund aimed at achieving a rate of return several times greater than that of the underlying asset such as Nikkei 225 futures. Recently, it has been suggested that rebalancing trades of a leveraged ETF may destabilize the financial markets. An empirical study using an agent-based simulation indicated that a rebalancing trade strategy could affect the price formation of an underlying asset market. However, no leveraged ETF trading method for suppressing the increase in volatility as much as possible has yet been proposed. In this paper, we compare different strategies of trading for a proposed trading model and report the results of our investigation regarding how best to suppress an increase in market volatility. As a result, it was found that as the minimum number of orders in a rebalancing trade increases, the impact on the market price formation decreases.

Suggested Citation

  • Isao Yagi & Shunya Maruyama & Takanobu Mizuta, 2020. "Trading Strategies of a Leveraged ETF in a Continuous Double Auction Market Using an Agent-Based Simulation," Papers 2010.13036, arXiv.org.
    • Handle: RePEc:arx:papers:2010.13036
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2010.13036
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
    2. Yoshio Iihara & Hideaki Kiyoshi Kato & Toshifumi Tokunaga, 2001. "Investors' Herding on the Tokyo Stock Exchange," International Review of Finance, International Review of Finance Ltd., vol. 2(1&2), pages 71-98.
    3. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    4. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    5. Yoshio Iihara & Hideaki Kiyoshi Kato & Toshifumi Tokunaga, 2001. "Investors’ Herding on the Tokyo Stock Exchange," International Review of Finance, International Review of Finance Ltd., vol. 2(1‐2), pages 71-98.
    6. Ivanov, Ivan T. & Lenkey, Stephen L., 2018. "Do leveraged ETFs really amplify late-day returns and volatility?," Journal of Financial Markets, Elsevier, vol. 41(C), pages 36-56.
    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. Isao Yagi & Mahiro Hoshino & Takanobu Mizuta, 2020. "Analysis of the impact of maker-taker fees on the stock market using agent-based simulation," Papers 2010.08992, arXiv.org.
    2. Fotini Economou & Konstantinos Gavriilidis & Bartosz Gebka & Vasileios Kallinterakis, 2022. "Feedback trading: a review of theory and empirical evidence," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 15(4), pages 429-476, February.
    3. Berna Kirkulak & Çagnur Kaytmaz Balsari, 2009. "Inflation Accounting and Stock Returns: Evidence From Istanbul Stock Exchange (ISE)," Istanbul Stock Exchange Review, Research and Business Development Department, Borsa Istanbul, vol. 11(42), pages 19-34.
    4. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    5. Yamamoto, Ryuichi & Hirata, Hideaki, 2013. "Strategy switching in the Japanese stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 37(10), pages 2010-2022.
    6. Chiarella, Carl & Ladley, Daniel, 2016. "Chasing trends at the micro-level: The effect of technical trading on order book dynamics," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 119-131.
    7. Saki Kawakubo & Kiyoshi Izumi & Shinobu Yoshimura, 2014. "Analysis Of An Option Market Dynamics Based On A Heterogeneous Agent Model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 21(2), pages 105-128, April.
    8. Fischer, Thomas & Riedler, Jesper, 2014. "Prices, debt and market structure in an agent-based model of the financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 95-120.
    9. Tedeschi, Gabriele & Recchioni, Maria Cristina & Berardi, Simone, 2019. "An approach to identifying micro behavior: How banks’ strategies influence financial cycles," Journal of Economic Behavior & Organization, Elsevier, vol. 162(C), pages 329-346.
    10. Gaffeo, Edoardo & Molinari, Massimo, 2017. "Taxing financial transactions in fundamentally heterogeneous markets," Economic Modelling, Elsevier, vol. 64(C), pages 322-333.
    11. Blaurock, Ivonne & Schmitt, Noemi & Westerhoff, Frank, 2018. "Market entry waves and volatility outbursts in stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 19-37.
    12. repec:bor:iserev:v:11:y:2011:i:42:p:1-18 is not listed on IDEAS
    13. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.
    14. Schmitt, Noemi & Westerhoff, Frank, 2014. "Speculative behavior and the dynamics of interacting stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 262-288.
    15. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
    16. Schmitt, Noemi & Westerhoff, Frank, 2021. "Trend followers, contrarians and fundamentalists: Explaining the dynamics of financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 117-136.
    17. Mahiro Hoshino & Takanobu Mizuta & Yasuhiro Sudo & Isao Yagi, 2022. "Impact of maker-taker fees on stock exchange competition from an agent-based simulation," Journal of Computational Social Science, Springer, vol. 5(2), pages 1323-1342, November.
    18. Recchioni, Maria Cristina & Tedeschi, Gabriele & Berardi, Simone, 2014. "Bank's strategies during the financial crisis," FinMaP-Working Papers 25, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    19. repec:bor:iserev:v:11:y:2011:i:42:p:19-34 is not listed on IDEAS
    20. Yosra Mefteh Rekik & Younes Boujelbene, 2015. "Price Dynamics and Market Volatility: Behavioral Heterogeneity under Switching Trading Strategies on Artificial Financial Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 33-43, April.
    21. Hazem Krichene & Mhamed-Ali El-Aroui, 2018. "Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 511-535, October.
    22. Alessio Emanuele Biondo, 2019. "Order book modeling and financial stability," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 469-489, September.

    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:2010.13036. 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.