Diversified Adaptive Stock Selection Using Continual Graph Learning and Ensemble Approach

Abstract

Stock selection is essential for portfolio diversification to reduce risks and maximize profits. However, stock selection is challenging 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. Non-continual graph learning-based methods can adapt to abrupt concept drift, while continual graph learning-based 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 the 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 leverages both simple graphs and hypergraphs because they complement each other. 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.

Keywords

• Stock selection
• non-continual learning
• continual learning
• graph learning
• simple graph
• hypergraph