Cost-Sensitive Online Window Size Selection for Portfolio Management
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 s...
| Authors | Yi-Chen Liu, Chung-Han Hsieh |
|---|---|
| Primary category | math.OC |
| Published | 24 September 2026 |
| Last updated at source | 28 September 2026 |
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