Stock movement prediction has always been a tough but attractive task for researchers in machine learning and data mining. Generally speaking, two challenges for stock time series prediction remain not well-explored. One is the overfitting of deep learning models due to the data shortage and the other one is the potential domain shift that may happen during the evolution of stock time series. In this paper, we present \textit{\textbf{M}eta-\textbf{A}daptive \textbf{S}tock movement prediction with two-\textbf{S}tag\textbf{E} \textbf{R}epresentation learning (\textbf{MASSER})}, a novel framework for stock movement prediction based on self-supervised learning and meta-learning. Specifically, we first build up a two-stage representation learning framework, the first-stage representation learning aims for unified embedding learning for the data. And the second-stage learning, which is based on the first stage, is used for temporal domain shift detection via self-supervised learning. Then, we formalize the problem of stock movement prediction into a standard meta-learning setting. Inspired by importance sampling, we estimate sampling probability for tasks to balance the domain discrepancy caused by evolving temporal domains. Extensive experiment results on two open source datasets show that our framework with two simple but classical architectures (GRU and ResNet) as model achieves improvements of 5\% - 9.5\% on average accuracy, compared to state-of-the-art baselines.