Nominal exchange rate determination is a puzzling phenomenon throughout the literature. Thus, the study aims to analyze the nominal exchange rate determination with a hybrid approach of macroeconomic and microstructure determinants, i.e., interest rate differential, oil price, order flow, and bid-ask spread over the long- and short-run horizons in the context of Malaysia. The dataset consists of high-frequency daily data from 2010 to 2017, employing a nonlinear ARDL approach. The results indicate that the bid-ask spread and interest rate differential were found to be key determinants of exchange rate dynamics in the long and short run. The findings show strong evidence of long-run asymmetry in the interest rate differential, while short-run asymmetry effects exist between microstructure determinants and the exchange rate. In addition, it indicates that the bid-ask spread holds informative content to explain the dynamics of the exchange rate in Malaysia. Additionally, the negative changes in the oil price could potentially act as macroeconomic news announcements and the bid-ask spread as liquidity determinants in Malaysia, which play a significant role in exchange rate determination. The study concluded that the prominent short-run asymmetry effects captured in cumulative order flow and bid-ask spread While a long-run asymmetry exists between the oil price and exchange rate in Malaysia. The empirical results allow for long-run and short-run asymmetric pricing impacts of a hybrid approach on the nominal exchange rate in Malaysia. This study is helpful in providing policy direction and practical implications for monetary authorities and market dealers. The bid-ask spread and oil price could be considered influential exchange rate determinants in the short run in Malaysia.

Financial risk early warning systems are essential for proactive risk management in volatile markets, particularly for emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) to enhance the accuracy and robustness of financial risk prediction. Using firm-level quarterly financial data from Chinese listed companies, the proposed model is benchmarked against standalone CNN, LSTM, and GRU architectures. Experimental results show that the hybrid CNN–LSTM–GRU model achieves superior performance across all evaluation metrics, with prediction accuracy reaching 93.5%, precision reaching 92.2%, recall reaching 91.8%, and F1-score reaching 92.0%, significantly outperforming individual models. Moreover, the hybrid approach demonstrates faster convergence than LSTM and improved class balance compared to CNN and GRU, reducing false negatives for high-risk firms—a critical aspect for early intervention. These findings highlight the hybrid model’s robustness and real-world applicability, offering regulators, investors, and policymakers a reliable tool for timely financial risk detection and informed decision-making. By combining high predictive power with computational efficiency, the proposed system provides a practical framework for strengthening financial stability in emerging and dynamic markets.

Green human resource management (GHRM) has been receiving more attention recently, although the discipline is still very new, and conceptual clarity has not yet been established. The VOSviewer software application was used to analyse a bibliometric evaluation of GHRM from 1913 to 2022 on Scopus and 574 papers on GHRM were examined. The goal of this study was to evaluate the GHRM field's volume, expanding tendency, global distribution, important journals, trailblazing authors, dominant nations, and dominant industries. In this work, word combinations were combined using logical operators, TITLE-ABS-KEY (“Green human resource management” OR “GHRM” OR “green HRM” OR “green HR” OR “Environmental Human Resource” OR “eco-friendly Practices” OR “Environmental Training” OR “Green Training”) AND (EXCLUDE (OA, “all”)) AND (EXCLUDE (PUBYEAR, 2023)) AND (LIMIT-TO (LANGUAGE, “English”)), thus expanding the gap. The review revealed that the GHRM is still a developing idea which aims to assist modern scholars.