IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v6y2020i1d10.1186_s40854-020-00191-4.html
   My bibliography  Save this article

Cost-benefit analysis of trading strategies in the stock index futures market

Author

Listed:
  • Xiong Xiong

    (Tianjin University)

  • Yian Cui

    (Research Institute, Shenzhen Stock Exchange)

  • Xiaocong Yan

    (Tianjin University)

  • Jun Liu

    (Tianjin University)

  • Shaoyi He

    (California State University)

Abstract

With the introduction of many derivatives into the capital market, including stock index futures, the trading strategies in financial markets have been gradually enriched. However, there is still no theoretical model that can determine whether these strategies are effective, what the risks are, and how costly the strategies are. We built an agent-based cross-market platform that includes five stocks and one stock index future, and constructed an evaluation system for stock index futures trading strategies. The evaluation system includes four dimensions: effectiveness, risk, occupation of capital, and impact cost. The results show that the informed strategy performs well in all aspects. The risk of the technical strategy is relatively higher than that of the other strategies. Moreover, occupation of capital and impact cost are both higher for the arbitrage strategy. Finally, the wealth of noise traders is almost lost.

Suggested Citation

  • Xiong Xiong & Yian Cui & Xiaocong Yan & Jun Liu & Shaoyi He, 2020. "Cost-benefit analysis of trading strategies in the stock index futures market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-17, December.
    • Handle: RePEc:spr:fininn:v:6:y:2020:i:1:d:10.1186_s40854-020-00191-4
    DOI: 10.1186/s40854-020-00191-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-020-00191-4
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-020-00191-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. J. Lussange & A. Belianin & S. Bourgeois-Gironde & B. Gutkin, 2018. "A bright future for financial agent-based models," Papers 1801.08222, arXiv.org.
    3. Shu-Heng Chen & Ragupathy Venkatachalam, 2017. "Agent-based modelling as a foundation for big data," Journal of Economic Methodology, Taylor & Francis Journals, vol. 24(4), pages 362-383, October.
    4. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
    5. Lee, Hsiang-Tai & Tsang, Wei-Lun, 2011. "Cross hedging single stock with American Depositary Receipt and stock index futures," Finance Research Letters, Elsevier, vol. 8(3), pages 146-157, September.
    6. Laws, Jason & Thompson, John, 2005. "Hedging effectiveness of stock index futures," European Journal of Operational Research, Elsevier, vol. 163(1), pages 177-191, May.
    7. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    8. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    9. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    10. W. Brian Arthur & Paul Tayler, "undated". "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Computing in Economics and Finance 1997 57, Society for Computational Economics.
    11. Martin T. Bohl & Christian A. Salm & Michael Schuppli, 2011. "Price discovery and investor structure in stock index futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(3), pages 282-306, March.
    12. Antoniou, Antonios & Koutmos, Gregory & Pericli, Andreas, 2005. "Index futures and positive feedback trading: evidence from major stock exchanges," Journal of Empirical Finance, Elsevier, vol. 12(2), pages 219-238, March.
    13. 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.
    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. Yongli Li & Tianchen Wang & Baiqing Sun & Chao Liu, 2022. "Detecting the lead–lag effect in stock markets: definition, patterns, and investment strategies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-36, December.
    2. Yu, Xing & Li, Yanyan & Gong, Xue & Zhang, Nan, 2022. "Evaluating the performance of futures hedging using factors-driven realized volatility," International Review of Financial Analysis, Elsevier, vol. 84(C).

    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. Anufriev, Mikhail & Panchenko, Valentyn, 2009. "Asset prices, traders' behavior and market design," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1073-1090, May.
    2. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    3. Amilon, Henrik, 2008. "Estimation of an adaptive stock market model with heterogeneous agents," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 342-362, March.
    4. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Post-Print hal-02084910, HAL.
    5. Mellár, Tamás & Hau, Orsolya & Sebestyén, Tamás, 2013. "Láthatóvá tehető-e a láthatatlan kéz? Egy ágensalapú piaci modell tapasztalatai [Can the invisible hand be rendered visible? Experiences of an agent-based market model]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(9), pages 992-1024.
    6. Pyo, Dong-Jin, 2014. "A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market," Staff General Research Papers Archive 37358, Iowa State University, Department of Economics.
    7. Chia-Hsuan Yeh, 2007. "The role of intelligence in time series properties," Computational Economics, Springer;Society for Computational Economics, vol. 30(2), pages 95-123, September.
    8. 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.
    9. Baosheng Yuan & Kan Chen, 2006. "Impact of investor’s varying risk aversion on the dynamics of asset price fluctuations," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 1(2), pages 189-214, November.
    10. Gaunersdorfer, Andrea & Hommes, Cars H. & Wagener, Florian O.O., 2008. "Bifurcation routes to volatility clustering under evolutionary learning," Journal of Economic Behavior & Organization, Elsevier, vol. 67(1), pages 27-47, July.
    11. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.
    12. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 991-1020, April.
    13. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-21.
    14. Ya-Chi Huang & Chueh-Yung Tsao, 2018. "Discovering Traders’ Heterogeneous Behavior in High-Frequency Financial Data," Computational Economics, Springer;Society for Computational Economics, vol. 51(4), pages 821-846, April.
    15. Hafner, Sarah & Anger-Kraavi, Annela & Monasterolo, Irene & Jones, Aled, 2020. "Emergence of New Economics Energy Transition Models: A Review," Ecological Economics, Elsevier, vol. 177(C).
    16. Alexandru Mandes & Peter Winker, 2017. "Complexity and model comparison in agent based modeling of financial markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 469-506, October.
    17. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    18. Stefano Balbi & Carlo Giupponi, 2009. "Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability," Working Papers 2009_15, Department of Economics, University of Venice "Ca' Foscari".
    19. Yu, Song-min & Fan, Ying & Zhu, Lei & Eichhammer, Wolfgang, 2020. "Modeling the emission trading scheme from an agent-based perspective: System dynamics emerging from firms’ coordination among abatement options," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1113-1128.
    20. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.

    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:spr:fininn:v:6:y:2020:i:1:d:10.1186_s40854-020-00191-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.