IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i3p92-d1342191.html
   My bibliography  Save this article

Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices

Author

Listed:
  • Chan Kyu Paik

    (Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea)

  • Jinhee Choi

    (Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea)

  • Ivan Ureta Vaquero

    (Department of Business Economics, Health and Social Care, The University of Applied Sciences and Arts of Southern Switzerland, 6928 Manno, Switzerland)

Abstract

Using stochastics in stock market analysis is widely accepted for index estimation and ultra-high-frequency trading. However, previous studies linking index estimation to actual trading without applying low-frequency trading are limited. This study applied William%R to the existing research and used fixed parameters to remove noise from stochastics. We propose contributing to stock market stakeholders by finding an easy-to-apply algorithmic trading methodology for individual and pension fund investors. The algorithm constructed two oscillators with fixed parameters to identify when to enter and exit the index and achieved good results against the benchmark. We tested two ETFs, SPY (S&P 500) and EWY (MSCI Korea), from 2010 to 2022. Over the 12-year study period, our model showed it can outperform the benchmark index, having a high hit ratio of over 80%, a maximum drawdown in the low single digits, and a trading frequency of 1.5 trades per year. The results of our empirical research show that this methodology simplifies the process for investors to effectively implement market timing strategies in their investment decisions.

Suggested Citation

  • Chan Kyu Paik & Jinhee Choi & Ivan Ureta Vaquero, 2024. "Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices," JRFM, MDPI, vol. 17(3), pages 1-18, February.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:3:p:92-:d:1342191
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/3/92/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/3/92/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    2. Henriksson, Roy D & Merton, Robert C, 1981. "On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills," The Journal of Business, University of Chicago Press, vol. 54(4), pages 513-533, October.
    3. Graham, John R. & Harvey, Campbell R., 1996. "Market timing ability and volatility implied in investment newsletters' asset allocation recommendations," Journal of Financial Economics, Elsevier, vol. 42(3), pages 397-421, November.
    4. Ben Ammar, Imen & Hellara, Slaheddine & Ghadhab, Imen, 2020. "High-frequency trading and stock liquidity: An intraday analysis," Research in International Business and Finance, Elsevier, vol. 53(C).
    5. Huang, Jing-Zhi & Huang, Zhijian (James), 2020. "Testing moving average trading strategies on ETFs," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 16-32.
    6. Pankaj Topiwala & Wei Dai, 2022. "Surviving Black Swans: The Challenge of Market Timing Systems," JRFM, MDPI, vol. 15(7), pages 1-25, June.
    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. ChanKyu Paik & Jinhee Choi & Ivan Ureta Vaquero, 2024. "Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator, Williams%R, and Trading Volume for the S&P 500," JRFM, MDPI, vol. 17(11), pages 1-20, November.

    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. Andrew J. Patton & Tarun Ramadorai, 2013. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," Journal of Finance, American Finance Association, vol. 68(2), pages 597-635, April.
    2. Linh Tran Dieu, 2015. "A Comparison of Bank and Non-bank Funds in the French Market," Journal of Financial Services Research, Springer;Western Finance Association, vol. 47(3), pages 273-294, June.
    3. 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.
    4. Rompotis, Gerasimos G., 2011. "Testing weak-form efficiency of exchange traded funds market," MPRA Paper 36020, University Library of Munich, Germany.
    5. Yin, Zhengnan & O’Sullivan, Niall & Sherman, Meadhbh, 2024. "The liquidity timing ability of mutual funds," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    6. Becker, Janis & Leschinski, Christian, 2018. "Directional Predictability of Daily Stock Returns," Hannover Economic Papers (HEP) dp-624, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    8. Nicolosi, Gina & Peng, Liang & Zhu, Ning, 2009. "Do individual investors learn from their trading experience?," Journal of Financial Markets, Elsevier, vol. 12(2), pages 317-336, May.
    9. Jiang, George J. & Yao, Tong & Yu, Tong, 2007. "Do mutual funds time the market? Evidence from portfolio holdings," Journal of Financial Economics, Elsevier, vol. 86(3), pages 724-758, December.
    10. Connie Becker & Wayne Ferson & David Myers & Michael Schill, 1998. "Conditional Market Timing with Benchmark Investors," NBER Working Papers 6434, National Bureau of Economic Research, Inc.
    11. Keith Cuthbertson & Dirk Nitzsche & Niall O'Sullivan, 2010. "Mutual Fund Performance: Measurement and Evidence," Financial Markets, Institutions & Instruments, John Wiley & Sons, vol. 19(2), pages 95-187, May.
    12. Patton, Andrew, 2010. "On the Dynamics of Hedge Fund Risk Exposures," CEPR Discussion Papers 7780, C.E.P.R. Discussion Papers.
    13. Jiang, Wei, 2003. "A nonparametric test of market timing," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 399-425, September.
    14. Wagner, Moritz & Margaritis, Dimitris, 2017. "All about fun(ds) in emerging markets? The case of equity mutual funds," Emerging Markets Review, Elsevier, vol. 33(C), pages 62-78.
    15. Zhengnan Yin & Niall O’Sullivan & Meadhbh Sherman, 2024. "The market timing ability of bond mutual funds," Journal of Asset Management, Palgrave Macmillan, vol. 25(5), pages 508-527, September.
    16. Kee‐Hong Bae & Junesuh Yi, 2008. "The Impact of the Short‐Short Rule Repeal on the Timing Ability of Mutual Funds," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 35(7‐8), pages 969-997, September.
    17. Bala G Arshanapalli & Lorne N Switzer & Karim Panju, 2007. "Equity-style timing: A multi-style rotation model for the Russell large-cap and small-cap growth and value style indexes," Journal of Asset Management, Palgrave Macmillan, vol. 8(1), pages 9-23, May.
    18. William Goetzmann & Jonathan Ingersoll & Zoran Ivkovich, 1998. "Monthly Measurement of Daily Timers," Yale School of Management Working Papers ysm88, Yale School of Management, revised 01 Oct 2000.
    19. Sergey Iskoz & Jiang Wang, 2003. "How to Tell if a Money Manager Knows More?," NBER Working Papers 9791, National Bureau of Economic Research, Inc.
    20. Keith Pilbeam & Hamish Preston, 2019. "An Empirical Investigation of the Performance of Japanese Mutual Funds: Skill or Luck?," IJFS, MDPI, vol. 7(1), pages 1-16, January.

    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:gam:jjrfmx:v:17:y:2024:i:3:p:92-:d:1342191. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.