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Purpose The purpose of this study is to empirically investigate the impact of intellectual capital (IC) on the financial performance of Islamic banks operating in the Gulf Cooperation Council (GCC) countries. Design/methodology/approach The study measures IC by the value added intellectual coefficient model. A regression analysis was used to assess the impact of IC on financial performance. The research sample consisted of Islamic banks operating in the GCC countries during the years 2011, 2012 and 2013. Data originated from the annual reports of Islamic banks. Findings The results support the thesis that IC has a positive impact on the financial performance of Islamic banks. Even though the average IC is lower than that reported in other studies, the positive effect on financial performance is obvious. The findings also show that human capital (HC) is higher than capital employed (CE) and structural capital (SC). The study reveals that SC has an insignificant impact on the financial performance of the Islamic banks compared to CE and HC. Practical implications The findings provide empirical evidence that IC affects the Islamic banks’ financial performance. It helps Islamic banks in the GCC countries to understand how to use their IC efficiently, especially SC as it is yet to be used efficiently. Also, the findings benefit the relevant authorities (e.g. legislators and central banks) who could use them to emphasise strategic policy reforms whenever required. Originality/value The current research adds to the empirical studies in the GCC countries as it views the region as a collective as opposed to individual countries. It also extends the IC and performance measurement literature of Islamic banks in the GCC countries. Moreover, the current study enriches the limited literature on IC in the context of Islamic banking.

This study examines the relationships and interrelationships between carbon disclosure and carbon performance, and between carbon performance and financial performance. It also examines the relationship between carbon disclosure and financial performance. Additionally, it investigates the relationship between agency cost and carbon disclosure, and between agency cost and carbon performance. Finally, this research investigates the trends in improvement of carbon disclosure and carbon performance of the companies, over the study period. The interrelationships between carbon disclosure and carbon performance, and between carbon performance and financial performance, have not been investigated by any study before. Similarly, no study has yet investigated the relationship between carbon disclosure and financial performance. The relationships between carbon disclosure/carbon performance and agency cost, have not been studied either by any previous research. Whilst a couple of studies have conducted trend analysis of carbon disclosure previously, no study has yet undertaken trend analysis of carbon performance. These examinations are performed by using a cross-sectional sample of the world's largest 500 firms, drawn from most major industry sectors, who participated in the Carbon Disclosure Project (CDP) questionnaire survey over the five-year period from 2011 to 2015. Both full sample and country-wise analysis have been done, to test the hypotheses of this study.Carbon disclosure and carbon performance scores for the sample companies are taken from the CDP database. Data for financial performance indicators, agency costs and relevant control variables, are collected from Thomson Reuters Datastream database.iii Findings of this study indicate that there is a significant positive relationship between a firm's carbon disclosure, and its carbon performance. They also indicate that carbon disclosure and carbon performance of business, influence each other positively.Country-wise analysis shows that carbon disclosure is significantly positively related to carbon performance in all of the four regions of this study -namely North America, EU, UK and Asia-Pacific. Both way positive interrelationship between carbon disclosure and carbon performance, holds true in all regions except the UK.The study also finds that carbon performance of a business is significantly negatively related to both accounting-based measure as well as market-based measure of a firm's financial performance. It also finds that there is no significant interrelationship between carbon performance and accounting-based measure of a firm's financial performance.However, carbon performance and market-based measure of a firm's financial performance, influence each other negatively -this relationship might vary across industries. Carbon performance is negatively related to both accounting-based measure as well as market-based measure of a firm's financial performance, in all regions except the UK. There is no significant and consistent interrelationship between car...

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We identify the core topics of research applying machine learning to finance. We use a probabilistic topic modeling approach to make sense of this diverse body of research spanning across multiple disciplines. Through a latent Dirichlet allocation topic modeling technique, we extract 15 coherent research topics that are the focus of 5942 academic studies from 1990 to 2020. We find that these topics can be grouped into four categories: Priceforecasting techniques, financial markets analysis, risk forecasting and financial perspectives. We first describe and structure these topics and then further show how the topic focus has evolved over the last three decades. A notable trend we find is the emergence of text-based machine learning, for example, for sentiment analysis, in recent years. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic EUROPEAN FINANCIAL MANAGEMENTWe thank John A. Doukas, the editor, and an anonymous referee of European Financial Management as the study has enormously benefited from their comments. We also thank Muhammad Farooq Ahmad and participants of IFABS 2019, Angers France for their valuable comments. Saqib Aziz and Michael Dowling acknowledge financial assistance from the B<>COM project: Prospect 2030. The views expressed in this article are those of the authors and all errors are our own. modeling of topics for deep comprehension of a body of literature.