This study examines how differences in national culture, as indicated by financial secrecy, affect the impact of mandatory adoption of IFRS on earnings quality across the countries of Europe. Using 24,034 firm-year observations from 16 European countries over the period 1998-2014, we find that the higher the level of secrecy in a country the lower the level of earnings quality of firms, as measured by signed abnormal accruals. We find that mandatory adoption of IFRS improves earnings quality in all countries. However, our study indicates that the impact of mandatory adoption of IFRS on earnings quality is stronger the higher the level of secrecy in a country. Our evidence thus helps to explain the different impacts of IFRS adoption on earnings quality across different jurisdictions.

Purpose The purpose of this study is to develop a new measure for discriminatory related party transactions (DRPTs). There are currently measures for such discriminatory transactions but the new measure has a strong theoretical basis and is less susceptible to measurement error. Design/methodology/approach This paper develops and tests a new measure for these discriminatory transactions. Type I and Type II error rates and the power of the new measure are compared with an existing measure using computer-simulated and real data. Findings The capital market sensitivity of the new measure is also tested and compared with the existing measure. The new measure is found to be superior. Practical implications The new measure of DRPTs has the potential to contribute to both further research on the impact of related party transactions and policy-making in relation to DRPTs. Originality/value This paper has developed and tested a new measure for DRPTs.

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In today's digital world, automated sentiment analysis from online reviews can contribute to a wide variety of decision-making processes. One example is examining typical perceptions of a product based on customer feedbacks to have a better understanding of consumer expectations, which can help enhance everything from customer service to product offerings. Online review comments, on the other hand, frequently mix different languages, use non-native scripts and do not adhere to strict grammar norms. For a low-resource language like Bangla, the lack of annotated code-mixed data makes automated sentiment analysis more challenging. To address this, we collect online reviews of different products and construct an annotated Bangla-English code mix (BE-CM) dataset (Dataset and other resources are available at https://github.com/fokhruli/CM-seti-anlysis). On our sentiment corpus, we also compare several alternative models from the existing literature. We present a simple but effective data augmentation method that can be utilized with existing word embedding algorithms without the need for a parallel corpus to improve cross-lingual contextual understanding. Our experimental results suggest that training word embedding models (e.g., Word2vec, FastText) with our data augmentation strategy can help the model in capturing the cross-lingual relationship for code-mixed sentences, thereby improving the overall performance of existing classifiers in both supervised learning and zero-shot cross-lingual adaptability. With extensive experimentations, we found that XGBoost with Fasttext embedding trained on our proposed data augmentation method outperforms other alternative models in automated sentiment analysis on code-mixed Bangla-English dataset, with a weighted F1 score of 87%.INDEX TERMS Code mixed, sentiment analysis, Bangla-English corpus, bi-lingual, zero-shot learning.