面向投资组合管理的成本敏感在线窗口大小选择

中文摘要 AI 辅助

本文研究了市场条件变化下投资组合管理的成本敏感在线窗口大小选择问题。具体而言，我们提出了一个两层框架，使用候选窗口大小构建投资组合，并通过在线学习动态聚合这些组合。通过将候选窗口大小视为“专家”，我们使用包含换手率的损失动态更新其聚合权重。此外，我们推导了考虑聚合投资组合换手率的有限时域成本敏感跟踪遗憾界，其中静态遗憾作为特例。在有界损失和成本率下，适当调整的Fixed Share对于次线性切换预算渐近地实现无跟踪遗憾，而Hedge覆盖静态情况。

英文摘要

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.

发表机构

• National Tsing Hua University（国立清华大学）

机构由 AI 辅助整理，请以论文原文为准。