Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31245
Title: Optimal market timing strategies under transaction costs
Authors: Li, W
Lam, K
Keywords: Autoregressive models
Growth-optimal investment strategy
Market timing
Stochastic dynamic programming
Issue Date: 2002
Publisher: Pergamon Press
Source: Omega, 2002, v. 30, no. 2, p. 97-108 How to cite?
Journal: Omega 
Abstract: In this paper, we consider optimal market timing strategies under transaction costs. We assume that the asset's return follows an auto-regressive model and use long-term investment growth as the objective of a market timing strategy which entails the shifting of funds between a risky asset and a riskless asset. We give the optimal trading strategy for a finite investment horizon, and analyze its limiting behavior. For a finite horizon, the optimal decision in each step depends on two threshold values. If the return today is between the two values, nothing needs to be done, otherwise funds will be shifted from one asset to another, depending on which threshold value is being exceeded. When investment horizon tends to infinity, the optimal strategy converges to a stationary policy, which is shown to be closely related to a well-known technical trading rule, called Momentum Index trading rule. An integral equation of the two threshold values is given. Numerical results for the limiting stationary strategy are presented. The results confirm the obvious guess that the no-transaction region increases as the transaction cost increase. Finally, the limiting stationary strategy is applied to data in the Hang Seng Index Futures market in Hong Kong. The out-of-sample performance of the limiting stationary strategy is found to be better than the simple strategy used in literature, which is based on an 1-step ahead forecast of return.
URI: http://hdl.handle.net/10397/31245
ISSN: 0305-0483
EISSN: 1873-5274
DOI: 10.1016/S0305-0483(01)00060-3
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

5
Last Week
0
Last month
0
Citations as of Sep 10, 2017

WEB OF SCIENCETM
Citations

5
Last Week
0
Last month
0
Citations as of Sep 21, 2017

Page view(s)

42
Last Week
2
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.