  Portfolio management (PM) is a fundamental financial trading task, which
explores the optimal periodical reallocation of capitals into different stocks
to pursue long-term profits. Reinforcement learning (RL) has recently shown its
potential to train profitable agents for PM through interacting with financial
markets. However, existing work mostly focuses on fixed stock pools, which is
inconsistent with investors' practical demand. Specifically, the target stock
pool of different investors varies dramatically due to their discrepancy on
market states and individual investors may temporally adjust stocks they desire
to trade (e.g., adding one popular stocks), which lead to customizable stock
pools (CSPs). Existing RL methods require to retrain RL agents even with a tiny
change of the stock pool, which leads to high computational cost and unstable
performance. To tackle this challenge, we propose EarnMore, a rEinforcement
leARNing framework with Maskable stOck REpresentation to handle PM with CSPs
through one-shot training in a global stock pool (GSP). Specifically, we first
introduce a mechanism to mask out the representation of the stocks outside the
target pool. Second, we learn meaningful stock representations through a
self-supervised masking and reconstruction process. Third, a re-weighting
mechanism is designed to make the portfolio concentrate on favorable stocks and
neglect the stocks outside the target pool. Through extensive experiments on 8
subset stock pools of the US stock market, we demonstrate that EarnMore
significantly outperforms 14 state-of-the-art baselines in terms of 6 popular
financial metrics with over 40% improvement on profit.
