Cost-Sensitive Online Window Size Selection for Portfolio Management
Abstract
This paper investigates cost-sensitive online window size selection for portfolio management under changing market conditions. Specifically, we propose a two-level framework that constructs portfolios using candidate window sizes and dynamically aggregates them through online learning. By treating candidate window sizes as ``experts,'' we dynamically update their aggregation weights using turnover-inclusive losses. Moreover, we derive finite-horizon cost-sensitive tracking-regret bounds that account for turnover of the aggregated portfolio, with static regret as a special case. Under bounded losses and cost rates, suitably tuned Fixed Share achieves asymptotically no tracking regret for sublinear switching budgets, with Hedge covering the static case.
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BibTeX
@misc{liu2026costsensitive,
title = {{Cost-Sensitive Online Window Size Selection for Portfolio Management}},
author = {Yi-Chen Liu and Chung-Han Hsieh},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.29887},
eprint = {2609.29887},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.29887},
}
RIS
TY - UNPB
TI - Cost-Sensitive Online Window Size Selection for Portfolio Management
AU - Yi-Chen Liu
AU - Chung-Han Hsieh
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.29887
UR - https://arxiv.org/abs/2609.29887
AB - This paper investigates cost-sensitive online window size selection for portfolio management under changing market conditions. Specifically, we propose a two-level framework that constructs portfolios using candidate window sizes and dynamically aggregates them through online learning. By treating candidate window sizes as ``experts,'' we dynamically update their aggregation weights using turnover-inclusive losses. Moreover, we derive finite-horizon cost-sensitive tracking-regret bounds that account for turnover of the aggregated portfolio, with static regret as a special case. Under bounded losses and cost rates, suitably tuned Fixed Share achieves asymptotically no tracking regret for sublinear switching budgets, with Hedge covering the static case.
ER -