Publication Type
Journal Article
Version
publishedVersion
Publication Date
8-2017
Abstract
To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias, empirical evaluations show that the proposed TCO framework may effectively handle reasonable transaction costs and improve existing strategies in the case of non-zero transaction costs.
Keywords
Portfolio optimization, Transaction costs, Learning in financial models, Investment strategy
Discipline
OS and Networks | Technology and Innovation
Publication
Quantitative Finance
First Page
1
Last Page
14
ISSN
1469-7688
Identifier
10.1080/14697688.2017.1357831
Publisher
Taylor & Francis (Routledge): SSH Titles
Citation
LI, Bin; WANG, Jialei; HUANG, Dingjiang; and HOI, Steven C. H..
Transaction cost optimization for online portfolio selection. (2017). Quantitative Finance. 1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/3759
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org./10.1080/14697688.2017.1357831