Stock selection is essential for portfolio diversification to reduce risks and maximize profits. However, stock selection is difficult owing to the non-stationary nature of stock markets. In fact, stock markets experience abrupt or gradual concept drift because of their inherent volatility. Concept drift is a phenomenon in which the statistical characteristics of data change over time. Recent stock selection methods have adopted graph neural networks to capture the relational dependencies between stocks. These methods perform non-continual learning that uses a fixed set of stocks without knowledge retention. Noncontinual graph learning-based methods can adapt to abrupt concept drift, while continual graph learningbased methods can adapt to gradual concept drift because they involve knowledge retention. To adapt to both abrupt and gradual concept drifts, we propose a stock selection framework called DASS, which combines non-continual and continual models for diversified adaptation. For both models, we employ graph learning to extract both temporal and relational dependencies. Our graph learning method relies on three main components: (1) low-level temporal modeling, which extracts temporal dependencies of individual stocks, (2) relational modeling, which extracts relational dependencies between stocks, and (3) high-level temporal modeling, which extracts temporal dependencies from the learned relational dependencies. Furthermore, DASS constructs simple graphs and hypergraphs based on dynamic time warping of stock prices and volume data. The performance of DASS is compared with that of state-of-the-art stock selection methods. Experimental results for stocks included in the Standard & Poor's 500 index reveal that DASS achieves a compounded annual growth rate of 83.2%, outperforming the second-best method by 23.0%P.