Purpose: The purpose of this paper is fourfold: first, to provide the first systematic study on the ethics of blockchain, mapping its main socio-technical challenges in technology and applications; second, to identify ethical issues of blockchain; third, to propose a conceptual framework of blockchain ethics study; fourth, to discuss ethical issues for stakeholders. Design/methodology/approach: The paper employs literature research, research agenda and framework development. Findings: Ethics of blockchain and its applications is essential for technology adoption. There is a void of research on blockchain ethics. The authors propose a first theoretical framework of blockchain ethics. Research agenda is proposed for future search. Finally, the authors recommend measures for stakeholders to facilitate the ethical adequacy of blockchain implementations and future Information Systems (IS) research directions. This research raises timely awareness and stimulates further debate on the ethics of blockchain in the IS community. Originality/value: First, this work provides timely systematic research on blockchain ethics. Second, the authors propose the first research framework of blockchain ethics. Third, the authors identify key research questions of blockchain ethics. Fourth, this study contributes to the understanding of blockchain technology and its societal impacts.

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.

Policy makings and regulations of financial markets rely on a good understanding of the complexity of financial markets. There have been recent advances in applying data‐driven science and network theory into the studies of social and financial systems. Financial assets and institutions are strongly connected and influence each other. It is essential to study how the topological structures of financial networks could potentially influence market behaviors. Network analysis is an innovative method to enhance data mining and knowledge discovery in financial data. With the help of complex network theory, the topological network structures of a market can be extracted to reveal hidden information and relationships among stocks. In this study, two major markets of the most influential economies, China and the United States, are systematically studied from the perspective of financial network analysis. Results suggest that the network properties and hierarchical structures are fundamentally different for the two stock markets. The patterns embedded in the price movements are revealed and shed light on the market dynamics. Financial policymakers and regulators can gain inspiration from these findings for applications in policy making, regulations design, portfolio management, risk management, and trading.

Background: Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images. Methods: 443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU). Results: The deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively. Conclusions: Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.