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.
Portfolio optimization, Transaction costs, Learning in financial models, Investment strategy
OS and Networks | Technology and Innovation
Data Management and Analytics
Taylor & Francis (Routledge): SSH Titles
LI, Bin; WANG, Jialei; HUANG, Dingjiang; and HOI, Steven C. H..
Transaction cost optimization for online portfolio selection. (2017). Quantitative Finance. 1-14. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3759
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