Environmental sustainability is a major concern of contemporary societies, businesses, and governments. However, there is a lack of knowledge as to how countries can achieve the goal to end poverty, whilst protecting the planet. It is the objective of our study to examine the moderating role of institutional quality on the financial development and environmental quality nexus in South Asia. Our sample consists of panel data of five South Asian countries (India, Bangladesh, Nepal, Sri Lanka and Pakistan) from 1984 to 2018. We find that financial development increases CO₂ emissions in this region, implying that countries in South Asia have utilized financial development for capitalization, instead of improving production technology. Institutional quality moderates the negative impact of financial development on environmental sustainability. An implication of our findings is that efforts to improve institutional quality may help to promote sustainable development in South Asia.

(2010). Does size matter? A genetic programming approach to technical trading. Quantitative Finance: Vol. 10, No. 2, pp. 131-140.

We examine the mediating role of capital structure in the perceived relationship of uncertainty, corporate social responsibility (CSR), stakeholder interest and financial performance. We collect data through questionnaires, and survey the Chief Financial Officers (CFOs) of the service sector of Pakistan. We apply Structure Equation Modeling (SEM) for data analysis. We find that CFOs perceive uncertainty, CSR and stakeholder interest to have both direct and indirect impacts on financial performance. In particular, we find evidence of the mediating effect of capital structure in the relationship. Our findings imply that firms screen out uncertain situations while making capital structure decision and pursuing CSR-related activities.

This paper proposes a conceptual modeling framework based on category theory that serves as a tool to study common structures underlying diverse approaches to modeling credit default that at first sight may appear to have nothing in common. The framework forms the basis for an entropy-based stacking model to address issues of inconsistency and bias in classification performance. Based on the Lending Club’s peer-to-peer loans dataset and Taiwanese credit card clients dataset, relative to individual base models, the proposed entropy-based stacking model provides more consistent performance across multiple data environments and less biased performance in terms of default classification. The process itself is agnostic to the base models selected and its performance superior, regardless of the models selected.

The purpose of this study is to address the critical issue of optimal credit allocation. Predicting a borrower’s probability of default is a key requirement of any credit allocation system but turning it into labeled classes leads to problems in performance measurement. In this paper the connection between the probability of default and optimal credit allocation is established through a conceptual construct called the Kelly criterion. Conflicting performance measures in dichotomous classification are replaced with coherent criteria for judging the performance of credit allocation decisions. Extensive testing on peer-to-peer lending data shows that the Kelly strategy enables consistent outperformance and efficiency in processing information relative to alternative credit allocation approaches.