Recursive algorithms for trailing stop: Stochastic approximation approach
type: Item
instance of: scholarly article
title: Recursive algorithms for trailing stop: Stochastic approximation approach
zbMATH Open document ID: 1205.90212
author: D. Kharzeev
published in: Journal of Optimization Theory and Applications
publication date: 2010-10-27T00:00:00Z
review text: The purpose of this paper is to study optimal trailing stops. Trailing stops are often used in stock trading to limit the maximum of a possible loss and to lock in a profit. Decision in selling a stock is crucial in successful investing in equity markets where the selling strategy can be determined by either a target level or a stop-loss limit. The authors focus on the stop-loss side of the problem. They develop stochastic approximation algorithms to recursively estimate the optimal trailing stop percentage. Convergence of the algorithm is obtained and interval estimates are constructed. In order to compare this algorithm with Monte Carlo methods, the authors use simulation examples. Finally, to demonstrate the feasibility and efficiency of the algorithms, numerical experiments and real market data are given.
Mathematics Subject Classification ID: 90C15
zbMATH DE Number: 5806827
zbMATH Keywords: stochastic optimization
zbMATH Keywords: trailing stops
reviewed by: I. M. Stancu-Minasian
MaRDI profile type: Publication
full work available at URL: s10957-010-9662-9
OpenAlex ID: W1989012362
cites work: Trading Securities Using Trailing Stops
cites work: A stopping rule for the Robbins-Monro method
cites work: Q4421713
cites work: Recursive Algorithms for Stock Liquidation: A Stochastic Optimization Approach
cites work: Q5560061
cites work: STOCK LIQUIDATION VIA STOCHASTIC APPROXIMATION USING NASDAQ DAILY AND INTRA‐DAY DATA
cites work: Q3721531
DOI: 10.1007/S10957-010-9662-9
description: scientific article; zbMATH DE number 5806827