The study investigates the relationship between monetary policy and bank profitability in New Zealand using the generalized method of moments (GMM) estimator. Our sample comprises 19 banks from New Zealand over the period 2006–2018. Our results suggest that an increase in short-term rate leads to an increase in the profitability of banks, while an increase in long-term interest rates reduces bank profitability. In addition to monetary policy variables, capital adequacy ratio, non-performing loan ratio, and cost to income ratio are also important determinants of the profitability of banks in New Zealand. Capital adequacy ratio has a positive impact on bank profitability, while non-performing loan ratio and cost to income ratio have a negative impact on bank profitability.

The study investigates the factors influencing profitability of banks in India and China. Our results suggest that credit quality, bank size, and cost management are the key factors behind the profitability of banks in India and China. The loan to deposit ratio of the banks is also important. It has a positive impact on the profitability of banks in China but a negative impact on the profitability of banks in India.

A number of studies have investigated the relationship between financial sector development and economic growth; however, the impact of bank profitability on economic growth is still unclear. We investigate the link between bank profitability and economic growth in the Asia-Pacific region over the period 2004–2014. Using the system GMM estimator, our findings suggest that a profitable banking sector is a prerequisite for economic growth in the Asia-Pacific region and that the impact of bank profitability on economic growth is more prominent in small banking sectors. Perhaps surprisingly, we found that the bank size has a negative impact on GDP growth, with the influence of bank profitability on economic growth reducing as the size of the banking sector increases. Our results also show that the impact of profitability on economic growth is much larger in developed economies compared to small emerging and large emerging economies.

This paper examines the effect of firm environmental performance on firm financing during the COVID-19 outbreak. Crises in multiple forms curtail Micro, Small and Medium Enterprises (MSMEs) stability and the livelihood of hundreds of millions of people who derive their living from these activities. The way in which MSMEs deal with crises and the extent to which environmental performance is beneficial when the market suffers a negative shock is relatively unexplored in the literature. We consider three aspects of financing -- firm level liquidity, bank credit and bankruptcy probabilities -- and argue that it pays for firms to show commitment to environmental responsibilities in a global pandemic. Through an examination of 3,356 MSMEs, we find that firms with better environmental performance reduce their probability of bankruptcies and their liquidities decreasing during the COVID-19 pandemic. Furthermore, analysis shows that the impact of a firm’s environmental performance is more pronounced in sensitive industries (hospitality and retail). The results are robust based on a series of robustness checks, including propensity score matching and the Heckman two-stage sample selection model. Our study suggests that the trust between a firm and its stakeholders, if it is grounded on environmental performance, pays off when the overall level of trust in markets suffers a negative shock. JEL Classification: F64; G01; Q14

Sustainable forest management is important to maintain the ability of forests to grow and provide a range of products and environmental services while retaining their natural features. We find that income, radiata pine prices, trade, agricultural expansion, and environmental policies have a significant impact on timber harvesting in New Zealand. Increase in income, radiata pine prices, number of farms, horticulture plantation and bank loans lead to an increase in timber harvesting. On the other hand, environmental policies, especially those to mitigate climate change, decrease timber harvesting.

Corporate Default prediction is a critical research area in finance and risk management, centred around estimation of probability of an event of a firm when it is unable to satisfy its financial commitments when they become due. A robust and accurate predictive model has immense value to all stakeholders including but not limited to lenders, investors, managers and societies. This study develops a robust Artificial Neural Network (ANN)–based machine learning model to enhance default prediction performance relative to traditional statistical approaches, particularly the Altman Z-score model, which is widely regarded as the benchmark among accounting-based prediction models. The proposed model incorporates market-based variables and Environmental, Social, and Governance (ESG) scores in addition to 21 carefully selected accounting-based indicators relevant to firm solvency. Empirical results demonstrate that a well-regularized ANN with optimized hyperparameters significantly outperforms the Altman Z-score model in terms of predictive accuracy, precision, and recall. Furthermore, the findings establish ESG as a moderate yet meaningful predictor of corporate default, with higher ESG scores consistently associated with lower default probabilities.