The recent financial network analysis approach reveals that the topologies of financial markets have an important influence on market dynamics. However, the majority of existing Finance Big Data networks are built as undirected networks without information on the influence directions among prices. Rather than understanding the correlations, this research applies the Granger causality test to build the Granger Causality Directed Network for 33 global major stock market indices. The paper further analyzes how the markets influence one another by investigating the directed edges in the different filtered networks. The network topology that evolves in different market periods is analyzed via a sliding window approach and Finance Big Data visualization. By quantifying the influences of market indices, 33 global major stock markets from the Granger causality network are ranked in comparison with the result based on PageRank centrality algorithm. Results reveal that the ranking lists are similar in both approaches where the U.S. indices dominate the top position followed by other American, European, and Asian indices. The lead-lag analysis reveals that there is lag effects among the global indices. The result sheds new insights on the influences among global stock markets with implications for trading strategy design, global portfolio management, risk management, and markets regulation.

To ease the fluctuation of hog prices and maintain the hog market’s stability, the central government of China has issued a series of hog price control policies. This paper, supplemented by co-word analysis and LDA thematic modeling, constructed 9 first-level indicators and 36 second-level indicators and used a PMC index model to conduct quantitative research on the selected 74 policies and regulations of China’s pig price regulation policies from July 2007 to April 2020. The research concludes that the research tool system of China’s hog price control is formed. The overall design of the hog price control policy is relatively reasonable, but there are still the following problems: the subject of China’s pig price control policy is singular, so it is difficult to form a resultant force; the policy pays attention to the price regulation in the short term, but ignores the long-term industrial structure adjustment; it emphasizes market supervision, but insufficient support for slaughtering and processing; it focuses on production and management to improve the development quality and efficiency of the pig industry, but does not take social equity into account. Finally, some policy suggestions are put forward: multi-department division of labor and close cooperation; adjusting the industrial structure of hog and carrying out appropriate large-scale breeding; establishing the operation mode of slaughtering and processing in the producing area to reduce the circulation cost of the pig industry; ensuring the consumption of pork by low-income groups and giving consideration to social efficiency and equity.

(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end.

In many industries, partners are interconnected in project alliances that have limited lifespans and clearly-defined boundaries. The transparency of the movie industry provides a unique opportunity to study how alliance network topologies impact the performance of project alliances from the perspectives of social networks and organization theories. In this work, we compiled a massive movie dataset and constructed alliance networks for both movie production and distribution companies. Using the box office as the proxy for the financial performance of a movie project alliance, this research investigates how the two alliance networks impact the box office. We introduce the social network properties of degrees, centralities, and structural holes as alliance network variables into empirical regression models. The results show that alliance networks have a significant influence on the box office. The degrees of production companies and the structural holes of distribution companies are especially important to achieve success in the box office. The results add new evidence for the study of the movie economy and alliance networks. Meanwhile, this work also provides implications for the movie industry by revealing that it is essential to wisely choose partners that are appropriately embedded in alliance networks for the success of a movie project.