Understanding the Chinese stock market: international comparison and policy implications

The definitions of the bear, sidewalk and bull markets are ambiguous in the existing literature. This makes it difficult for practitioners to distinguish between different market conditions. In this paper, we propose statistical definitions of the bear, sidewalk and bull markets, which correspond to the three states in our hidden semi-Markov model. We apply this analysis to the daily returns of the Chinese stock market and seven developed markets. Using the Viterbi algorithm to globally decode the most likely sequence of the market conditions, we systematically find the precise timing of the bear, sidewalk and bull markets for all the eight markets. Through the comparison of the estimation and decoding results, many unique characteristics of the Chinese stock market are revealed, such as ‘crazy bull’, ‘frequent and quick bear’ and ‘no buffer zone’. In China, the bull market is more volatile than in developed markets, the bear market occurs more frequently than in developed markets, and the sidewalk market has not functioned as a buffer zone since 2005. Possible causes of these unique characteristics are also discussed and implications for policy-making are suggested

Decoding Chinese stock market returns: three-state hidden semi-Markov model

In this paper, we employ a three-state hidden semi-Markov model (HSMM) to explain the time-varying distribution of the Chinese stock market returns since 2005. Our results indicate that the time-varying distribution depends on the hidden states, which are represented by three market conditions, namely the bear, sidewalk, and bull markets. We find that the inflation, the PMI, and the exchange rate are significantly related to the market conditions in China. A simple trading strategy based on expanding window decoding shows profitability with a Sharpe ratio of 1.14

Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm

The Expressive Power of Graph Neural Networks: A Survey

Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement

Structures of an intramembrane vitamin K epoxide reductase homolog reveal control mechanisms for electron transfer

The intramembrane vitamin K epoxide reductase (VKOR) supports blood coagulation in humans and is the target of the anticoagulant warfarin. VKOR and its homologs generate disulfide bonds in organisms ranging from bacteria to humans. Here, to better understand the mechanism of VKOR catalysis, we report two crystal structures of a bacterial VKOR captured in different reaction states. These structures reveal a short helix at the hydrophobic active site of VKOR that alters between wound and unwound conformations. Motions of this “horizontal helix” promote electron transfer by regulating the positions of two cysteines in an adjacent loop. Winding of the helix separates these “loop cysteines” to prevent backward electron flow. Despite these motions, hydrophobicity at the active site is maintained to facilitate VKOR catalysis. Biochemical experiments suggest that several warfarin-resistant mutations act by changing the conformation of the horizontal helix. Taken together, these studies provide a comprehensive understanding of VKOR function

Reliability analysis for automobile engines: conditional inference trees

The reliability model with covariates for machinery parts has been extensively studied by the proportional hazards model (PHM) and its variants. However, it is not straightforward to provide business recommendations based on the results of the PHM. We use a novel method, namely the Conditional Inference Tree, to conduct the reliability analysis for the automobile engines data, provided by a UK fleet company. We find that the reliability of automobile engines is significantly related to the vehicle age, early failure, and repair history. Our tree-structured model can be easily interpreted, and tangible business recommendations are provided for the fleet management and maintenance