  As the cornerstone of modern portfolio theory, Markowitz's mean-variance
optimization is considered a major model adopted in portfolio management.
However, due to the difficulty of estimating its parameters, it cannot be
applied to all periods. In some cases, naive strategies such as
Equally-weighted and Value-weighted portfolios can even get better performance.
Under these circumstances, we can use multiple classic strategies as multiple
strategic arms in multi-armed bandit to naturally establish a connection with
the portfolio selection problem. This can also help to maximize the rewards in
the bandit algorithm by the trade-off between exploration and exploitation. In
this paper, we present a portfolio bandit strategy through Thompson sampling
which aims to make online portfolio choices by effectively exploiting the
performances among multiple arms. Also, by constructing multiple strategic
arms, we can obtain the optimal investment portfolio to adapt different
investment periods. Moreover, we devise a novel reward function based on users'
different investment risk preferences, which can be adaptive to various
investment styles. Our experimental results demonstrate that our proposed
portfolio strategy has marked superiority across representative real-world
market datasets in terms of extensive evaluation criteria.
