Investment is a common economics task in which investors maximize future profits by continuously reallocating their current assets. A large number of studies are based on specifying stocks and constantly adjusting the ratio between these stocks to gain more benefits. However, the question of which stocks should be included in the portfolio is not addressed, while some investment strategies only select stocks and buy them without portfolio optimization, which may also cause unexpected loss owing to market oscillation. We try to integrate stock selection and portfolio optimization as a complete process to address this problem using hierarchical reinforcement learning. The high-level policy selects stocks with a high profitable probability, and then the low-level policy makes portfolio optimization on the selected stocks to gain more profit. The performance in China market demonstrates that our hierarchical agents can over performance a single stock selection agent.

Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e.g., real estate agents, appraisers, lenders, and buyers). However, it is a non-trivial task for accurate real estate appraisal because of three major challenges: (1) The complicated influencing factors for property value; (2) The asynchronously spatiotemporal dependencies among real estate transactions; (3) The diversified correlations between residential communities. To this end, we propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. Specifically, by acquiring and integrating multi-source urban data, we first construct a rich feature set to comprehensively profile the real estate from multiple perspectives (e.g., geographical distribution, human mobility distribution, and resident demographics distribution). Then, an evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronously spatiotemporal dependencies among real estate transactions. Moreover, to further incorporate valuable knowledge from the view of residential communities, we devise a hierarchical heterogeneous community graph convolution module to capture diversified correlations between residential communities. Finally, an urban district partitioned multi-task learning module is introduced to generate differently distributed value opinions for real estate. Extensive experiments on two real-world datasets demonstrate the effectiveness of MugRep and its components and features.