Recursive Algorithms for Trailing Stop: Stochastic Approximation Approach

G. Yin (), Q. Zhang () and C. Zhuang ()
Additional contact information
G. Yin: Wayne State University
Q. Zhang: University of Georgia
C. Zhuang: University of Southern California

Journal of Optimization Theory and Applications, 2010, vol. 146, issue 1, No 13, 209-231

Abstract: 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.

Keywords: Trailing stops; Stochastic approximations; Stochastic optimization (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-010-9662-9

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