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The purpose of this study is to investigate the implementation of International Financial Reporting Standard (IFRS) 16 in developing countries to enhance asset pronouncements or the quality of opaque accounting information for listed firms’ leasing transactions.

This study designed ordinary least square (OLS) regression models to examine the hypotheses in two ordered tests. The first-order test ascertained the association between fundamental accounting information and earnings or stock prices. Then, the second-order test was nested to add the instrument variable to the first-order one. In addition, the researchers selected 17 Asia-Pacific countries.

First, this study contributes to the fair value of firms’ asset measurements, and the accounting discipline requires adaptive scalability to produce future potential cash flows. Second, it reduces literature gaps between the pros and cons of the opaqueness of assets. In addition, these research arguments would be the referee for reducing information’s opacity. Finally, this study demonstrates the impact of IFRS 16’s implementation on firms’ conservatism levels and entropy’s information quality, requiring the regulators to accommodate these issues.

Due to the implementation of IFRS 16, the authors are neutral about the impacted financial statements and political consequences for these Asia-Pacific listed firms and countries. First, we propose the uniqueness of problematic elaboration since implementing IFRS 16 results in a more pronounced or opaque information quality due to vulnerable complexities in the financial statements. Second, this implementation is associated with hierarchical information and conservatism, producing accounting information entropy or negentropy. However, the hierarchy theory suggests various levels of conservatism that could increase or decrease the information’s quality.

The study aims to explore how integrating recent fundamental values (RFVs) from conventional accounting studies enhances the accuracy of a machine learning (ML) model for predicting stock return movement in Indonesia.

The study uses multilayer perceptron (MLP) analysis, a deep learning model subset of the ML method. The model utilizes findings from conventional accounting studies from 2019 to 2021 and samples from 10 firms in the Indonesian stock market from September 2018 to August 2019.

Incorporating RFVs improves predictive accuracy in the MLP model, especially in long reporting data ranges. The accuracy of the RFVs is also higher than that of raw data and common accounting ratio inputs.

The study uses Indonesian firms as its sample. We believe our findings apply to other emerging Asian markets and add to the existing ML literature on stock prediction. Nevertheless, expanding to different samples could strengthen the results of this study.

Governments can regulate RFV-based artificial intelligence (AI) applications for stock prediction to enhance decision-making about stock investment. Also, practitioners, analysts and investors can be inspired to develop RFV-based AI tools.

Studies in the literature on ML-based stock prediction find limited use for fundamental values and mainly apply technical indicators. However, this study demonstrates that including RFV in the ML model improves investors’ decision-making and minimizes unethical data use and artificial intelligence-based fraud.

This study aims to investigate the opacity of bank assets because of the International Financial Reporting Standard (IFRS) 9 implementation. It highlights that the Asian-Pacific countries’ banking industries are experiencing economic volatility. In other words, it examines information asymmetries because of the standards requiring a mechanistic treatment. Thus, this focuses on the tragedy of the commons (ToTC) caused by the implementation of the standard.

This research selects a sample of banking firms in the Asia-Pacific region from 2010 to 2021. Furthermore, it examines the impacts of IFRS 9’s implementation on earnings forecasts and share-return conveyances. This research first uses the OLS regression for examining the bank assets’ opacities, which may affect future earnings and information conveyancing. Second, it arranges these opacities, earnings and stock returns with the 2-SLS regression to find the staging associations because of hierarchical relevances.

This study finds that bank assets’ opacity is caused by a standard’s implementation, which is a ToTC, and this study signifies its first occurrence. Simultaneously, it recognises an information asymmetry because of the implemented procedural calculation mandated by the standard. Furthermore, these opacities affect future earnings and information conveyancing that inherited information asymmetries, which have affected them as the second ToTC. Finally, current and future earnings as a consequent impact of asset opacity are recursively associated with stock return conveyancing as the third ToTC.

This study demonstrates hierarchical information about bank asset opacities, starting by recognising and measuring them in financial statements. Then, these recognised and measured asset opacities are associated with current and future earnings, ending on the ordinarily and staged influencing of stock return conveyancing. Moreover, it reveals hierarchical information in the direct-ordinarily and staged associations among bank asset opacities, earnings and return conveyances. Thus, these associations are valid and occur because of the mandates of the standard’s measurement.

The purpose of this study is to examine the impact of managerial ability on corporate tax risk and long-term tax avoidance using the upper echelons theory.

This study uses a quantitative method with regression models, using a sample of listed firms on the Indonesia Stock Exchange from 2011 to 2018.

The regression results report that managerial ability negatively influences tax risk and positively impacts long-run tax avoidance. Companies with more able managers have a relatively lower tax risk and greater long-run tax avoidance. The results reveal that firms with managers that possess greater abilities are more committed to long-run tax avoidance while concurrently maintaining a lower level of their tax risk. The impacts the authors report are statistically significant and robust, as proved by a series of robustness checks and additional tests.

This study only includes firms from one developing country.

The empirical results might be of interest to board members while envisaging the benefits and costs of appointing and hiring managers, as well as to the tax authority and the other stakeholders interested in apprehending how managerial ability influences corporate tax risk and long-run tax avoidance practices simultaneously.

This study proposes and tests an explanation for the impact of managerial ability on corporate tax risk and long-run avoidance simultaneously in the context of an emerging country.