Purpose -The purpose of this paper is to investigate the determinants of intellectual capital performance in the UK banks over the period 1999-2005. Design/methodology/approach -Multiple regression analysis is used to test the relationship between the intellectual capital performance as a dependent variable and certain independent variables. Findings -Results indicate that the standard variables, bank profitability and bank risk, are important. The results also show that investment in information technology (IT) systems, bank efficiency, barriers to entry and efficiency of investment in intellectual capital variables, which have not been considered in previous studies, have a significant impact on intellectual capital performance. Research limitations/implications -More evidence is needed on the determinants of intellectual capital performance before any generalisation of the results can be made. In addition, the empirical tests were conducted only on the Major British Banks Group over the period 1999-2005 and hence the results of the study cannot be assumed to extend beyond this group of banks or to different study periods. Practical implications -The study might help the banking regulators in addressing the factors affecting intellectual capital performance to take actions towards developing their performance and in turn maximise their value creation. Originality/value -This paper adds to the literature on the determinants of intellectual capital performance in banks. In particular, it tests the theories that investment in IT systems, bank efficiency, barriers to entry and efficiency of investment in intellectual capital have impact on intellectual capital performance.

Cryptocurrencies are decentralized electronic counterparts of government-issued money. The first and best-known cryptocurrency example is bitcoin. Cryptocurrencies are used to make transactions anonymously and securely over the internet. The decentralization behavior of a cryptocurrency has radically reduced central control over them, thereby influencing international trade and relations. Wide fluctuations in cryptocurrency prices motivate the urgent requirement for an accurate model to predict its price. Cryptocurrency price prediction is one of the trending areas among researchers. Research work in this field uses traditional statistical and machine-learning techniques, such as Bayesian regression, logistic regression, linear regression, support vector machine, artificial neural network, deep learning, and reinforcement learning. No seasonal effects exist in cryptocurrency, making it hard to predict using a statistical approach. Traditional statistical methods, although simple to implement and interpret, require a lot of statistical assumptions that could be unrealistic, leaving machine learning as the best technology in this field, being capable of predicting price based on experience. This article provides a comprehensive summary of the previous studies in the field of cryptocurrency price prediction from 2010 to 2020. The discussion presented in this article will help researchers to fill the gap in existing studies and gain more future insight.

This paper investigates the determinants of the social disclosure level in UK banks over the period 1981-1996. Content analysis is used to measure the social disclosure level. The regression results show that market structure, investment in information technology and risk factors, which have not been considered in previous studies, have a significant impact on the social disclosure level. In addition, bank size and profitability are significant but the listing status and the age of the bank are insignificant.

This paper investigates whether investment in information technology systems affects bank profitability in the UK during the period 1976-1996. The results show that, when the other factors used in the literature are included, the number of automated teller machines installed by a bank has a positive impact on bank profitability.

Purpose -The purpose of this paper is to investigate the determinants of the intellectual capital performance of UAE banks over the period 2004 to 2010. Design/methodology/approach -Multiple regression analysis was used to test the relationship between the intellectual capital performance as a dependent variable and certain independent variables. Findings -The results indicate that standard variables, namely investment in information technology systems, barriers to entry, bank risk, bank size, bank age and bank listing age, are important. The results also show that the global financial crisis and market structure as measured by concentration ratio variables, which have not been considered in previous studies, have a significant impact on intellectual capital performance. Research limitations/implications -More evidence is needed regarding the determinants of intellectual capital performance before any generalisation of the results can be made. In addition, the empirical tests were conducted only for UAE banks between 2004 and 2010. Therefore, it cannot be assumed that the results of the study extend beyond this group of banks or to different periods. Practical implications -The paper might help the banking regulators address the factors affecting intellectual capital performance and also help banks to take action to developing their performance, in turn maximising their value creation. Originality/value -The paper adds to the literature discussing determinants of intellectual capital performance in banks. In particular, it tests the theory that the global financial crisis and market structure, as measured by concentration ratio, have an impact on intellectual capital performance.