Measuring and explaining competition in the financial sector

The first part of this paper provides a systematic discussion of the structural problems of competition on financial markets as observed from the demand and from the supply side, using a diagnostic framework. Potential impediments to competition are concentration, entry barriers, lack of transparency, product complexity, switching and search costs, financial illiteracy, lack of consumer power and weak intermediaries. In response to such financial market failures, we suggest a number of possible policy reactions. The second part of the paper investigates ways to measure competition and provides empirical figures on banking competition in 101 separate countries and assesses the market structure as monopolistic (or a perfect cartel), perfectly competitive or monopolistic competitive. Also, banking competition is explained, using explanatory variables of market structure, contestability, inter-industry competition, and institutional and macro economic conditions. This analysis provides possible instruments for reform in order to help promote competition. Next, the impact of banking consolidation is examined. Finally, developments in competition are observed over time, generally pointing to a downward trend.competition, concentration, entry barriers, transparency, consolidation, contestability, institutional conditions, restrictions on activities or investment, regulation, Panzar-Rosse model.

Elhauge on Tying: Vindicated by History

This video of this paper being presented is also available

Empowering remittance management in the digitised landscape: A real-time Data-Driven Decision Support with predictive abilities for financial transactions

The advent of Blockchain technology (BT) revolutionised the way remittance transactions are recorded. Banks and remittance organisations have shown a growing interest in exploring blockchain's potential advantages over traditional practices. This paper presents a data-driven predictive decision support approach as an innovative artefact designed for the blockchain-oriented remittance industry. Employing a theory-generating Design Science Research (DSR) approach, we have uncovered the emergence of predictive capabilities driven by transactional big data. The artefact integrates predictive analytics and Machine Learning (ML) to enable real-time remittance monitoring, empowering management decision-makers to address challenges in the uncertain digitised landscape of blockchain-oriented remittance companies. Bridging the gap between theory and practice, this research not only enhances the security of the remittance ecosystem but also lays the foundation for future predictive decision support solutions, extending the potential of predictive analytics to other domains. Additionally, the generated theory from the artifact's implementation enriches the DSR approach and fosters grounded and stakeholder theory development in the information systems domain.Comment: Ppaper has been accepted for presenting in the Australasian Conference on Information Systems 2023, Dec 6 to 8, Wellington, N

Autism research : An objective quantitative review of progress and focus between 1994 and 2015

The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders. Despite this re-conceptualization, research initiatives, including the NIMH's Research Domain Criteria and Precision Medicine, highlight the need to bridge psychiatric and psychological classification methodologies with biomedical techniques. Combining traditional bibliometric co-word techniques, with tenets of graph theory and network analysis, this article provides an objective thematic review of research between 1994 and 2015 to consider evolution and focus. Results illustrate growth in Autism research since 2006, with nascent focus on physiology. However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives.Peer reviewedFinal Published versio

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section

Empowering remittance management in the digitised landscape: A real-time Data-Driven Decision Support with predictive abilities for financial transactions

Blockchain technology (BT) revolutionised remittance transactions recording, banks and remittance institutes have shown growing interest in exploring blockchain\u27s potential advantages over traditional practices. This paper presents a data-driven predictive decision support approach as an innovative artefact designed for blockchain-oriented remittance industry. Employing theory-generating Design Science Research (DSR) approach, the transaction Big Data (BD) driven predictive emerged. The artefact integrates Predictive Analytics (PA) and Machine Learning (ML) to enable real-time transactions monitoring, empowering management decision-makers to address challenges in the uncertain digitized landscape of blockchain-oriented remittance companies. Bridging the gap between theory and the practice, this research safeguards the remittance ecosystem while fostering future predictive decision support solution with its PA advancement in other domains. Additionally, the generation of theory from the artifact\u27s implementation enriches the DSR approach and fosters grounded and stakeholder theory development in the Information Systems (IS) domain

Dealing with Uncertainty: An Empirical Study on the Relevance of Renewable Energy Forecasting Methods

The increasing share of fluctuating renewable energy sources on the world-wide energy production leads to a rising public interest in dedicated forecasting methods. As different scientific communities are dedicated to that topic, many solutions are proposed but not all are suited for users from utility companies. We describe an empirical approach to analyze the scientific relevance of renewable energy forecasting methods in literature. Then, we conduct a survey amongst forecasting software providers and users from the energy domain and compare the outcomes of both studies