Hassan Raza

This paper examines the impact of climate change, climate finance and climate uncertainty on sustainable development while considering the moderating role of ESG factors. Using data ranging between 2000 and 2023 for South Asian countries facing global warming challenges, this study employs multiple diagnostic tests leading to the application of PMG‐ARDL short‐ and long‐term models. Additionally, ESG indicators are measured using constructed indices through PCA analysis based on unsupervised machine learning approach. The finding reveals that climate change and climate uncertainty negatively affect sustainable development, showing that environmental degradation and climate unpredictability undermine long‐term development. Despite having climate funds to cope with the challenges posed by climatic changes, South Asian countries fail to achieve sustainable development targets due to bad governance. Policy makers can streamline the use of climate finance for attainment of SDGs targets. New structural reforms in terms of utilizing climate finance and implementing ESG frameworks are indispensable to deal with climatic changes and uncertainties. This work is a progressive study, which uniquely examines climatic changes and climate finance effect on sustainable development of South Asian countries. It also distinctively analyzes the role of ESG indicators between the climate‐sustainability nexus. Methodologically, it is novel in construction of ESG indices for sample countries.

Market uncertainty and recurring financial crises have intensified scholarly attention on bankruptcy and financial distress. While existing bibliometric studies have examined aspects of this domain, they typically rely on small datasets from single databases, employ limited analytical techniques, and lack a comprehensive content review. This study addresses these gaps by conducting the largest bibliometric and content review analysis of bankruptcy research to date, examining 69,104 publications retrieved from the OpenAlex database spanning the period 1960–2025. Using science mapping techniques including keyword co-occurrence networks, Latent Dirichlet Allocation (LDA) topic modeling, thematic mapping, and thematic evolution analysis, this study identifies the intellectual structures, thematic trajectories, and knowledge clusters shaping the field. The corpus, authored by 89,226 unique researchers and disseminated across 13,343 unique sources, has accumulated 680,273 total citations with an aggregate h-index of 292. Edward I. Altman emerges as the most impactful author (h-index = 40, TC = 26,913), while the Journal of Finance ranks as the leading outlet (h-index = 82, TC = 75,662). Geographically, the United States dominates research output (10,177 publications), followed by Indonesia (4,853) and China (2,570). LDA topic modeling reveals ten distinct thematic clusters spanning finance, law, health, economics, and computer science, reflecting the field’s genuinely interdisciplinary character. Thematic evolution analysis demonstrates a clear shift from classical statistical approaches toward machine learning, corporate governance, and ESG-oriented research. This study integrates performance analysis, science mapping, and content review within a unified methodological framework to develop a comprehensive intellectual cartography of bankruptcy research. The analysis connects thematic clusters to their underexplored areas, identifies research gaps, and proposes directions for future inquiry. The findings carry practical implications for researchers, practitioners, and policymakers engaged with financial distress, credit risk assessment, and insolvency regulation.

This paper examines the application of econometric models, deep learning architectures, and hybrid combinations of both methods for volatility forecasting in cryptocurrency markets. Using daily data on 10 major cryptocurrencies from 2020–2025, this work benchmarks GARCH-family models, deep learning models, and hybrid frameworks against a uniform 30-day realized volatility target under a strict walk-forward evaluation protocol. The findings indicate that traditional GARCH models perform adequately for relatively stable assets, whereas deep learning models improve accuracy by capturing nonlinear dynamics. Hybrid models, particularly those combining GARCH models with TDNN and GRU components, achieve the most accurate and statistically significant volatility forecasts. These findings highlight the added value of hybrid methodologies in cryptocurrency risk modeling.

Research background: This study examined the critical intersection between corporate profitability and adherence to Sustainable Development Goal (SDG) 13 - Climate Action, highlighting the importance of environmental management training within organizations. As the global business landscape evolves, it is essential to understand how internal sustainability practices influence financial outcomes. This research addressed the gap in understanding the dynamic interplay between corporate financial performance and strategic commitment to environmental sustainability. Purpose of the article: The primary objective of this research was to analyze the moderating role of environmental management training on the relationship between firm profitability and support for SDG 13. It attempted to elucidate how structured training initiatives can improve corporate financial performance while promoting robust climate action strategies. Methods: This study used a comprehensive panel dataset of 31,346 firms in 67 countries from 2013 to 2022. Advanced machine learning algorithms, including decision tree, random forest, gradient boosting, and logistic regression, were employed to analyze the impact of environmental management training on corporate profitability and sustainability initiatives. These methods allow for a nuanced understanding of the complex, non-linear interactions among the variables studied, providing deep insights into the dynamics at play. Findings & Value added: The results indicate that certain predictive models, such as Decision Tree and Random Forest, initially faced challenges like overfitting. However, incorporating environmental management training variables improved their robustness, highlighting the importance of variable selection in sustainability analytics. Among the models tested, Gradient Boosting demonstrated a strong balance between precision and recall, making it particularly effective for predicting corporate engagement in climate initiatives. The incorporation of machine learning provides a novel methodological perspective that deepens our understanding of how profitability metrics can influence and enhance corporate sustainability efforts. This research adds value to the discourse on sustainable business practices by providing robust empirical evidence and methodological innovations that can guide policymakers and business leaders in crafting strategies that promote sustainable development. Moreover, this study aligns with the journal’s focus by offering innovative approaches to economic policy and enhancing understanding of the intersections between corporate strategy and sustainable business practices.

In recent years, the issue of worldwide uncertainty has gained more attention in academic literature. Therefore, the current study examines how the United States (U.S.) economic policy uncertainty (EPU) affects various stock indices, commodities and cryptocurrencies. This study takes data on stock indices and commodities from February 2005 to December 2023 and data on cryptocurrency from October 2017 to December 2023. For estimations, we employ the Quantile-on-Quantile regression (QQR) approach to investigate the impact and to understand how changes in EPU affect stock indices, commodities, and cryptocurrency returns at different levels of quantiles. The findings reveal that EPU has a negative impact on the stock indices and cryptocurrencies. For stocks, high uncertainty leads to more volatility, while EPU exhibits higher volatility for cryptocurrencies, indicating sensitivity to policy changes. Similarly, commodities react differently to the U.S. EPU, while gold tends to appreciate in uncertain times. Furthermore, we employ quantile regression for robustness check, and the findings validate the outcome of QQR at various levels of quantiles from lower to higher. Moreover, the findings of this study are helpful for investors, portfolio managers, and policymakers to develop better investment strategies and effectively manage risks across different asset classes.

The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.

The aim of this study is to explore the influence of Environmental, Social, and Governance (ESG) factor on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted Principal Component Analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of 7 variables that predict business failure. The results indicate that ESG factor significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.

Cryptocurrencies are quickly becoming a key tool in investment decisions. The volatile nature of bitcoin prices has spurred the demand for robust predictive models. The primary objective of this study is to evaluate and compare the effectiveness of different machine learning models with the combination of technical indicators in predicting bitcoin prices. The study used 27 critical technical indicators to evaluate four machine learning techniques, namely Artificial Neural Network (ANN), a Hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), Support Vector Machine (SVM), and Random Forest. The results showed that ANN and SVM achieve a significant prediction accuracy of 81% and 82%, respectively, which is higher than the results of traditional models such as standard ARIMA. In practical applications, these methods often improve prediction accuracy by 20-30% over traditional models. The novelty of the analysis lies in the use of temporal and spatial trends via momentum, ROC, and %K features, making for a holistic approach to cryptocurrency market forecasting. This study underscores the critical importance of specific technical indicators and the imperative role of data mining in revolutionizing cryptocurrency market navigation. The research results highlight opportunities to improve investment strategies and risk management policies in the bitcoin market using machine learning models, making the latter valuable to investors and financial experts.

This study uses advanced machine learning models to predict stock prices in the Pakistani stock market using 27 technical indicators. It evaluates the predictive capabilities of four techniques, SVM, LSTM, and Random Forest for binary classification of stock price movements. ANN and SVM show the highest accuracy at 85%, followed by Random Forest at 84% and LSTM at 78%. Key indicators such as %R, Momentum, and Disparity 5 are critical across all models. The research provides valuable insights for investors and analysts to improve decision-making. It underscores the importance of technical indicators and establishes a data-driven approach to navigating the complexities of the Pakistani stock market. The study further emphasizes the importance of technical indicators and suggests exploring hybrid models that incorporate real-time data, sentiment analysis, and external factors for better stock price prediction.