Publication Type

Journal Article

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

Research Areas

Data Management and Analytics

Publication

Quantitative Finance

First Page

1

Last Page

14

ISSN

1469-7688

Identifier

10.1080/14697688.2017.1357831

Publisher

Taylor & Francis (Routledge): SSH Titles

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1080/14697688.2017.1357831

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