Cost-Sensitive Online Window Size Selection for Portfolio Management
A two-level framework that constructs portfolios using candidate window sizes and dynamically aggregates them through online learning is proposed and finite-horizon cost-sensitive tracking-regret bounds are derived that account for turnover of the aggregated portfolio, with static regret as a special case.