In this study, we address the ambiguity in portfolio optimization, particularly focusing on the uncertainty related to the statistical parameters governing asset returns. We propose a novel method that combines robust optimization with artificial neural networks (ANNs). Our approach effectively...

This study investigates systemic risk, return patterns, and diversification within the Johannesburg Stock Exchange (JSE) during the COVID-19 pandemic, utilizing data-centric approaches and the ARMA-GARCH vine copula-based conditional value-at-risk (CoVaR) model. By comparing three investment...

This study aims to explore the potential association between the performance of the stock market and the growth of the rural economy in China. It examines the impact of regional market volatility on rural macroeconomic indicators, which functions through the equity price fluctuations of locally...

This study investigates whether cryptocurrency investments have a distinct impact on corporate liquidity depending on when they are held and the stage of a firm's life cycle at the time of holding, using a sample of Korean companies. The empirical findings first show that cryptocurrency...

This study evaluates the effectiveness of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) in comparison to the traditional Mean-Variance optimization method for financial portfolio management. Leveraging a dataset of global financial assets, we applied both approaches to optimize...