AI RESEARCH
Order Optimal Regret Bounds for Sharpe Ratio Optimization under Thompson Sampling
arXiv CS.LG
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ArXi:2508.13749v2 Announce Type: replace In this paper, we investigate the problem of sequential decision-making for Sharpe ratio (SR) maximization in a stochastic bandit setting. We focus on the Thompson Sampling (TS) algorithm, a Bayesian approach celebrated for its empirical performance and exploration efficiency, under the assumption of Gaussian rewards with unknown parameters. Unlike conventional bandit objectives focusing on maximizing cumulative reward, Sharpe ratio optimization instead.