Publication Type
Journal Article
Version
publishedVersion
Publication Date
8-2018
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
Databases and Information Systems | Portfolio and Security Analysis
Research Areas
Data Science and Engineering
Publication
Quantitative Finance
Volume
18
Issue
8
First Page
1411
Last Page
1424
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. (2018). Quantitative Finance. 18, (8), 1411-1424.
Available at: https://ink.library.smu.edu.sg/sis_research/3777
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/14697688.2017.1357831