TITLE

Recursive Algorithms for Trailing Stop: Stochastic Approximation Approach

AUTHOR(S)
Yin, G.; Zhang, Q.; Zhuang, C.
PUB. DATE
July 2010
SOURCE
Journal of Optimization Theory & Applications;Jul2010, Vol. 146 Issue 1, p209
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
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.
ACCESSION #
51313006

 

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