China's Non-Performing Bank Loan Crisis: the role of economic rents

Various reasons have been put forward to explain the massive accumulation of non-performing loans in China. This paper applies the financial-restraint model as the analytical framework and argues that failure to create sufficient economic rents is the chief reason underlying the current dismal performance of banks. While the formal financial system is less important than the informal system—especially for financing the private enterprises that are playing a crucial role in economic growth—not addressing the non-performing loan crisis in the formal financial system will likely invite an economic slow-down.     

Fiscal policy and the structure of production in a two-sector developing economy

This paper develops a two-sector model of a developing economy and examines the role of the informal sector in limiting the government's ability to increase tax revenues. A key feature is the introduction of auditing of the informal sector and degree of tax enforcement in that sector. We emphasize the interdependence between tax policy and enforcement in achieving a developing economy's fiscal objectives and show that by judicious policy choices the presence of the informal sector need not hinder its ability to raise tax revenues. We supplement the formal analysis with numerical simulations highlighting the contrasting intertemporal tradeoffs implied by higher tax rates and tax-enforcement levels.     

The Grameen Bank and Poverty alleviation in Bangladesh

A bstract . The Grameen (grameen means rural) Bank of Bangladesh has innovated a mechanism under which credit can be provided to the poorest of the poor on a group liability basis instead of any collateral. Based on this principle, over the last decade, the bank has been successfully operating with an unprecedented loan recovery rate. Although from the point of view of profits, the Grameen Bank is not yet a viable institution, empirical evidence suggests that the bank's credit program has significantly improved the socioeconomic conditions of its borrowers. The Grameen Bank's success story in the alleviation of poverty in Bangladesh has resulted in widespread attempts of its replication in many other countries including the United States and Canada. The spread of the Grameen Bank idea around the world has drawn keen attention from researchers, policy makers and agencies interested in rural development.     

Weakening the Effect of Unfavorable Country of Origin: A Process- and Parameter-Associated Theoretical Framework

ABSTRACT

The consequence of strategic decisions of Western brands to source their products from offshore countries, largely from the Far East, has added a lot of unfavorable origin countries to their products' labels. Thus, scholars and marketing practitioners are becoming aware of the negative downstream consequences of unfavorable countries of origin in consumers' product evaluation. This research work suggests that, depending on the parameters (cues) that consumers consider along with the country of origin cue, their product evaluation can follow either cognitive, affective, or normative processing. This research study offers a unique framework associated with process-specific parameters that are manifested in weakening the effect of unfavorable country of origin in previous research.     

Social responsibility in micro businesses in an African context: Towards a theoretical understanding

Small businesses often engage in social responsibility (SR) without knowing it or without referring to their actions as SR. This is particularly the case in developing countries where the idea of SR is seen as synonymous with philanthropy. While the literature on small business social responsibility is growing, our understanding of the context-specific determinants of responsible practices in microbusinesses (those that employ less than 10 employees) that dominate the business landscape in many developing countries is still limited. In this paper, we address this gap and offer a theoretical understanding of how microbusiness owner-managers understand and approach SR by drawing on rich qualitative data collected from Nigeria—Africa's largest economy. We utilise Social Representations Theory to understand (1) how and why microbusiness owner-managers limit their understanding of SR to philanthropic activities and (2) the wide range of practices, including philanthropic activities, that account for their overall Responsible Business Practices (RBPs)—those practices that enable them to act responsibly towards their stakeholders and/or operating environments. We find that owner-managers' representations of SR are anchored primarily on an idiosyncratic ethical tendency and inclination towards the creation of social/communal harmony by ‘giving back to society’ and objectified via actions deriving from three ‘giving back’ orientations, namely Core Philanthropism, Social Problem Solving, and Empowering. We model microbusiness owner-managers' RBPs, highlighting the intersectional interplay of the voluntary (SR as philanthropy) and non-voluntary (self-regulatory and legal) dimensions of RBPs. Finally, we discuss the key findings in relation to the extant literature and to policymakers and managers.     

Wisdom of the crowd and stock price crash risk: evidence from social media

Review of Quantitative Finance and Accounting - In this study, we investigate the impact of social media on future stock price crash risk. A stock price crash occurs when managers hoard bad news...     

Bank systemic risk: An analysis of the sovereign rating ceiling policy and rating downgrades

We investigate the impact that the sovereign ceiling policy has on financial stability. In the event of a sovereign rating downgrade, we find that the rating agencies' sovereign ceiling policy leads to a disproportionate downgrade of the most creditworthy financial institutions in the economy and results in increased systemic risk. This asymmetric variation in bank ratings also impairs equity growth that further exacerbates bank insolvency. Our results are robust to several matching techniques, such as propensity score matching and entropy balancing, falsification tests, subsample analyses, alternative empirical proxies and model specifications.     

Russia–Ukraine crisis: The effects on the European stock market

We examine the effect of the Russia–Ukraine crisis on the European stock markets. Because of increased political uncertainty, geographic proximity and the ramifications of the fresh sanctions imposed on Russia, the European stock markets tended to react negatively to this crisis. We find that on 21 February 2022, when Russia recognized two Ukrainian states as autonomous regions, European stocks incurred a significant negative abnormal return. Moreover, the negative stock price reactions continued in the post-event period. The magnitude of the stock price reactions to this crisis exhibits considerable variation across industries, countries and size of the company.     

An economic assessment of options for operating within plantation forestry water entitlements and tightening cap and trade policy

The Green Triangle (GT) region of southern Australia is one of only two jurisdictions globally to licence plantation forestry's groundwater use. In response to declines in groundwater resources caused by historical plantation expansion, reductions in forest water allocations (~50%) are likely for some parts of the region, presenting novel challenges for forest managers in maintaining revenues and timber flows. This article presents a mathematical programming model evaluation of water trade opportunities for plantation forest owners to adapt to reduced water entitlements and explores how tightening groundwater policy could affect forestry returns and land use mix for the region. Results suggest that even absent opportunity to sell water, relatively limited 11% reduction in return could be expected for a large (−50%) water entitlement and (−48%) land-use change out of forestry. Results suggest that opportunities for forestry companies to sell water entitlements may allow them to maintain or even increase combined returns from forestry and water sales. Whilst the results highlight the adaptive capacity of the plantation forestry sector to operate within reduced water entitlement, a significant sectoral and regional economy adjustment would be likely. The discussion focusses on the potential to realise optimisation model-identified adaptation opportunities accounting for real-world thin markets, transaction costs and market friction.     

Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.