Corporate voluntary disclosure becomes a burning issue in the literature of accounting throughout the last two decades. The study aims to explore the most crucial determinants that influence corporate voluntary disclosure in a transition economy. A cross-sectional study based on the pharmaceutical and chemical companies listed in the Dhaka Stock Exchange is conducted to reconnoiter the crucial determinants affecting the voluntary disclosure. Based on the agency theory, stakeholder theory, and previous literature, the determinants are selected. An unweighted disclosure index is used to measure the extent of voluntary disclosure; after that, a multivariate analysis is steered to reconnoiter the key determinants of voluntary disclosure. It is found that firm leverage and firm liquidity are the key determinants that significantly influence the corporate voluntary disclosure in a transition economy. In contrast, no significant positive association is found between voluntary disclosure and board size. In additon, it is also found that market category significantly influences voluntary disclosure with an inverse direction. This study has important implications for both the corporate people and the regulatory bodies of the transition economy. The study also helps various stakeholders of the transition economy – Bangladesh, in designing their strategies regarding the most significant determinants of voluntary disclosure. Acknowledgment We are very thankful to the Institute of Advanced Research (IAR), United International University, Bangladesh, to grant us the fund by mobilizing which we generate our required data for the study and complete this empirical study.

The study aims to investigate the impact of ownership structure on corporate voluntary disclosure in the listed companies of Bangladesh. While many studies on the impact of ownership structure on voluntary disclosure have looked at developed and developing countries, few studies have been carried out in a transition economy. Using a three-step relative voluntary disclosure index, the study applies a multivariate analysis on the cross-sectional data for the year 2018. The findings indicate that the quality of voluntary disclosure in transition economy is still below average but has improved compared to findings from the previous literature. We found a significant inverse relationship between corporate voluntary disclosure and public ownership, while no significant relationships between voluntary disclosure and institutional ownership, director ownership, and foreign ownership have been found. The empirical findings of the study will provide evidence to promote the voluntary disclosure characterized by the ownership structures. The findings have important implications for both local and foreign investors as they make their investment decisions especially related to a transition economy. Besides, the findings will assist, not only the corporate executives in rearranging their reporting paradigm, but also the regulators and governments in similar transition economy in adopting and formulating their corporate policies and strategies.

This study assesses the differences between Islamic and conventional bank’s productivity. Earlier studies on bank productivity focused on conventional banks, but few have been done on Islamic banks. Therefore, the present study attempts to close the gap in the literature by investigating the productivity of Islamic and conventional banks in the context of the Middle East, Southeast Asia and South Asia regions. The sample is comprised of 385 banks (66 Islamic banks and 319 conventional banks) from 18 countries with data observations from 2008 to 2017. Panel data techniques with DEA-based MPI will be employed to investigate the impact of selected important factor and bank productivity as indicated by total factor productivity changes (TFPCH). Based on the results, Islamic banks are more productive than conventional banks and the results from t test are further confirmed by the results from nonparametric tests. These results are attributed to the progress in EFFCH. However, the mean difference between Islamic and conventional banks TFPCH is not statistically significant in all regions. The main benefit is that this work will hopefully provide additional insight and complement the existing studies on bank productivity of Islamic and conventional banks that are important to the banks, regulations, investors and researchers.

Sentiment classification of financial news deals with the identification of positive and negative news so that they can be applied in decision support systems for stock trend predictions. This paper explores several types of feature spaces as different data spaces for sentiment classification of the news article. Experiments are conducted using [Formula: see text]-gram models unigram, bigram and the combination of unigram and bigram as feature extraction with traditional feature weighting methods (binary, term frequency (TF), and term frequency-document frequency (TF-IDF)), while document frequency (DF) was used in order to generate feature spaces with different dimensions to evaluate [Formula: see text]-gram models and traditional feature weighting methods. We performed some experiments to measure the classification accuracy of support vector machine (SVM) with two kernel methods of Linear and Gaussian radial basis function (RBF). We concluded that feature selection and feature weighting methods can have a substantial role in sentiment classification. Furthermore, the results showed that the proposed work which combined unigram and bigram along with TF-IDF feature weighting method and optimized RBF kernel SVM produced high classification accuracy in financial news classification.

PurposeGlobalisation has influenced many countries, over the last few decades with financial globalisation and liberalisation bringing regulatory reforms in the banking sector. Thus, this study aims to fill a gap in the literature by examining the influence of globalisation on Islamic and conventional bank productivity in Southeast Asia.Design/methodology/approachThe sample comprised 155 banks (23 Islamic and 132 conventional) from 4 countries from 2008 to 2017. Panel data techniques will be used, together with data envelopment analysis (DEA)-based Malmquist productivity index (MPI), to investigate the impact of chosen main determinants on bank productivity. A panel regression analysis will be performed after generating the productivity index from the DEA-based MPI frontier.FindingsAccording to the findings, Islamic banks are statistically significantly more productive than conventional banks, and the findings of the t-test are corroborated by the findings of nonparametric tests. Furthermore, the findings of the panel regression model reveal that bank specific factors and macroeconomic variables are significant determinants to bank productivity. Surprisingly, the findings also show that the influence of social globalisation elements tends to be negatively related to conventional bank productivity.Originality/valueThis study adds to the existing literature by bridging the globalisation gap in the productivity of the dual banking industry, particularly in the specific context of Southeast Asia, given that the area is representative of Islamic and finance globally.