Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp , a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.

Knowledge graph entity typing (KGET) aims to infer missing entity typing instances in KGs, which is a significant subtask of KG completion. Despite of its progress, however, it still faces two non-trivial challenges: (i) most existing KGET methods extract features by encoding the existing entity typing tuples, while ignoring rich relational knowledge. (ii) they typically treat each entity typing tuple in KGs independently, and thus inevitably fail to take account of the inherent and valuable neighborhood information surrounding a tuple. To address these challenges, we build a novel Heterogeneous Relational Graph (HRG), and propose a Multiplex Relational Graph Attention Networks (MRGAT) to learn on HRG, and then utilize a Connecting Embeddings model (ConnectE) to make entity type inference. Specifically, the framework contains three components. Firstly, to effectively integrate the entity typing tuples and entity relation triples in KGs, we construct a HRG that consists of three semantic subgraphs. Secondly, we employ MRGAT to learn embeddings on HRG. In MRGAT, each subgraph of HRG is fed to its corresponding model that is capable of capturing neighborhood information. Finally, given the learned embeddings, we make entity type prediction by ConnectE. Experimental results validate the superiority of our model against various state-of-the-art baselines.

In the realm of deep graph learning, our study uniquely addresses the under-explored area of vague graph learning. While the effectiveness of deep graph learning is recognized across various disciplines, the nuances of vague graph learning-whether its inherent vagueness should be incorporated or disregarded and its influence on deep graph learning efficiency-remain largely unexamined. We fill this gap by introducing a novel decoupled graph learning framework. This is achieved by proposing a matrix-based or a tensor-based fusion module to estimate unobservable node attributes, a hybrid attention mechanism to bridge nodes with both explicit and implicit relationships, and a message-passing mechanism for feature-sensitive transporting. The design principle of decoupling allows it to accommodate ambiguities in any or all of these aspects of node representation, linking, and message passing. Furthermore, we leverage an extensive stock dataset spanning 64 years across the entire U.S. market to assess our framework. This real-world data not only adds a practical dimension to our study but also highlights the effectiveness of vague graph learning. Remarkably, our framework demonstrates superiority over state-of-the-art algorithms, marking performance enhancements of at least 6.73%, 7.25%, and 11.39% in terms of Rank IC, $R^{2}$ , and Rank ICIR, respectively.

Stock volatility is influenced by the release, dissemination, and acceptance of information. Rumor clarification is expected to reduce asymmetric information and abnormal stock returns by increasing information transparency. However, investors are irrational, and modern behavioral finance studies attribute non-random stock movements to investors’ cognitive and emotional biases. The verification of rumor authenticity may cause fluctuations in investor sentiment, which increases impulsive investing behaviors and stock movements. Due to the widespread and fast accessibility of social media, many electronic information platforms have been established to clarify rumors. It is critical to understand the effects of digitalized rumor clarification on stock markets. In this study, we extracted 12,663 rumor-clarification pairs from 1,804,520 social media posts. We quantified the language used in these messages via sentiment analysis, along with online firm behaviors, to study the effect of clarifications on stock markets. Our findings are as follows: (1) Digitalized rumor-clarification messages affect the abnormal returns of relevant stocks. (2) This influence can be quantified and measured by the emotion polarity of rumor clarification. (3) Firms’ online clarification behaviors, including information disclosure frequency, response time, and wording, have limited to no influence on abnormal returns.

Bi-typed multi-relational heterogeneous graph (BMHG) is one of the most common graphs in practice, for example, academic networks, e-commerce user behavior graph and enterprise knowledge graph. It is a critical and challenge problem on how to learn the numerical representation for each node to characterize subtle structures. However, most previous studies treat all node relations in BMHG as the same class of relation without distinguishing the different characteristics between the intra-type relations and inter-type relations of the bi-typed nodes, causing the loss of significant structure information. To address this issue, we propose a novel D ual H ierarchical A ttention N etworks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-type and inter-type attention-based encoder under a hierarchical mechanism. Specifically, the former encoder aggregates information from the same type of nodes, while the latter aggregates node representations from its different types of neighbors. Moreover, to sufficiently model node multi-relational information in BMHG, we adopt a newly proposed hierarchical mechanism. By doing so, the proposed dual hierarchical attention operations enable our model to fully capture the complex structures of the bi-typed multi-relational heterogeneous graphs. Experimental results on various tasks against the state-of-the-arts sufficiently confirm the capability of DHAN in learning node representations on the BMHGs.