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Recursive Algorithms for Trailing Stop: Stochastic Approximation Approach

Author

Listed:
  • G. Yin

    (Wayne State University)

  • Q. Zhang

    (University of Georgia)

  • C. Zhuang

    (University of Southern California)

Abstract

Trailing stops are often used in stock trading to limit the maximum of a possible loss and to lock in a profit. This work develops stochastic approximation algorithms to estimate the optimal trailing stop percentage. A stochastic optimization approach is proposed to recursively estimate the desired trailing stop percentage. A modification using projection is developed to ensure that the approximation sequence constructed stays in a reasonable range. Convergence of the algorithm is obtained. Moreover, interval estimates are constructed. Simulation examples are presented to compare our algorithm with Monte Carlo methods. Finally, we use real market data to demonstrate the algorithms.

Suggested Citation

  • G. Yin & Q. Zhang & C. Zhuang, 2010. "Recursive Algorithms for Trailing Stop: Stochastic Approximation Approach," Journal of Optimization Theory and Applications, Springer, vol. 146(1), pages 209-231, July.
    • Handle: RePEc:spr:joptap:v:146:y:2010:i:1:d:10.1007_s10957-010-9662-9
    DOI: 10.1007/s10957-010-9662-9
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    References listed on IDEAS

    as
    1. G. Yin & Q. Zhang & F. Liu & R. H. Liu & Y. Cheng, 2006. "Stock Liquidation Via Stochastic Approximation Using Nasdaq Daily And Intra‐Day Data," Mathematical Finance, Wiley Blackwell, vol. 16(1), pages 217-236, January.
    2. Peter W. Glynn & Donald L. Iglehart, 1995. "Trading Securities Using Trailing Stops," Management Science, INFORMS, vol. 41(6), pages 1096-1106, June.
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    Citations

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    Cited by:

    1. Hongzhong Zhang, 2018. "Stochastic Drawdowns," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 10078.
    2. Tim Leung & Hongzhong Zhang, 2017. "Optimal Trading with a Trailing Stop," Papers 1701.03960, arXiv.org, revised Mar 2019.

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