A Cross-Cultural Multi-Country Analysis of Unfavorable News Announcements in Public Companies

Value-relevant bad news leads to declining stockholder wealth. Various factors moderate the decline. We explore the moderating effect of culture (country), economic development levels, and insider trading laws. To this effect, we compile rich bad news announcement data from the US, Japan, China, and India. Ours is the first such study to cover comprehensive data from multiple countries. Stock markets in the US, Japan, and India experience a significant stock decline following the public announcement of bad news. In contrast, companies traded in the Chinese stock market experienced a positive stock impact. Companies in countries with high long-term orientation (Japan and China) perform better than those with low long-term orientation (the US and India). Economic development levels also play a significant mediating role. Countries with stronger trading laws do not experience stock decline before the public announcement of disruptions. Our study enriches the current state of the art by performing a multi-country analysis of stock impact from bad news announcements. The results are of interest to investors and policymakers

National Culture\u27s Impact on Effectiveness of Supply Chain Disruption Management

The purpose of this research is to understand the national cultural antecedents that may help explain differences in supply chain disruptions mitigation abilities of companies from different countries. An analysis of survey data on disruption planning and response collected from various organizations worldwide was performed using weighted least square regression and factor analysis. We find that culture influences disruption planning and response. Statistical findings suggest that differences in disruption planning and response abilities between companies from different countries could be partly attributed to national culture. All five Hofstede’s dimensions of national culture, i.e., Power Distance, Individualism, Masculinity, Uncertainty Avoidance, and Long-term Orientation were shown to have a significant positive effect on disruption planning and response. National cultural dimensions and economic status of a country could be effectively used to predict disruption planning and response abilities of companies in various countries. Managers could benefit from our research as it could help them assess disruptions mitigation abilities of their partners located in other countries. Increasing international trade and globalization of supply chains accentuate the importance of our research

Culture and Stock Market Impact From Bad News Announcements

Bad news causes a decline in the stockholder’s wealth. However, the magnitude of the impact varies between studies. Intending to explain the different impacts observed, we explore the factors affecting the extent of stock impact from bad news announcement. Event Study Methodology is used to analyze data from the US, India, and Japan. The rich multinational data allows the comparison of stock impact between countries. We find that disruptions cause stock decline; however, the magnitude of reduction varies between countries. We argue that national culture plays a vital role in planning and management strategies, affecting mitigation and continuity strategies. Modern companies are multinational and operate in multiple countries. Despite this, national culture is ingrained in their management styles. To explore this, we also study companies traded on stock markets outside their domicile country. We find that national culture has a strong influence on planning and preparedness. Cultural orientation impacts resiliency. We argue that investors realize the importance of culture as company domicile affects the stock impact from bad news

THE APPLICATION OF METABOLIC NETWORK ANALYSIS IN UNDERSTANDING AND TARGETING METABOLISM FOR DRUG DISCOVERY

Metabolic networks provide a vital framework for understanding the cellular metabolism in both physiological and pathophysiological states, which will ultimately facilitate network analysis-based drug discovery. In this thesis, we aim to employ a metabolic network analysis approach to study cancer metabolism (a pathophysiological state) and the metabolism of the bacterial pathogen, S. aureus (a physiological state), in order to understand, predict, and ultimately target cell metabolism for drug discovery. Cancer cells have distinct metabolism that highly depend on glycolysis instead of mitochondrial oxidative phosphorylation alone, even in the presence of oxygen, also called aerobic glycolysis or the Warburg effect, which may offer novel therapeutic opportunities. However, the origin of the Warburg effect is only partially understood. To understand the origin of cancer metabolism, our theoretical collaborator, Prof. Alexei Vazquez, developed a reduced flux balance model of human cell metabolism incorporating the macromolecular crowding (MC) constraint and the maximum glucose uptake constraint. The simulations successfully captured the main characteristics of cancer metabolism (aerobic glycolysis), indicating that MC constraint may be a potential origin of the Warburg effect. Notably, when we experimentally tested the model with mammalian cells from low to high growth rates as a proxy of MC alteration, we find that, consistent with the model, faster growing cells indeed have increased aerobic glycolysis. Moreover, the metabolic network analysis approach has also been shown to be capable of predicting the drug targets against pathogen metabolism when completely reconstructed metabolic networks are available. We deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and demonstrated experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. Our results indicate that the metabolic network analysis approach is able to facilitate the understanding of cellular metabolism by identifying potential constraints and predicting as well as ultimately targeting the metabolism of the organisms whose complete metabolic networks are available through the seamless integration of virtual screening with experimental validation

Empirical Research on the Relation Between Shares Reduction of Senior Executives and Earnings Management

Using listed companies with a presence of shares reduction of senior executives after the split share structure reform as research objects, we systematically study whether there are changes in earnings management behavior of senior executives’ shares reduction, as well as the relationship between the shares reduction degree and earnings management degree. Our analysis reveals that companies with a presence of shares reduction of senior executives have significantly positive controls over accounting earnings in the years of 2008 and 2009. However, there is no significant correlation between the level of earnings management of listed companies in China and the scale of shares reduction of senior executives

Contrastive Cross-Domain Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.Comment: This paper has been accepted by CIKM 202

Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck

This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of MNER and MRE lies in incorporating evident visual information to enhance textual semantics, where two issues inherently demand investigations. The first issue is modality-noise, where the task-irrelevant information in each modality may be noises misleading the task prediction. The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE by Multi-Modal representation learning with Information Bottleneck (MMIB). For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction. For the second issue, an alignment-regularizer is proposed, where a mutual information-based item works in a contrastive manner to regularize the consistent text-image representations. To our best knowledge, we are the first to explore variational IB estimation for MNER and MRE. Experiments show that MMIB achieves the state-of-the-art performances on three public benchmarks