A fuzzy compromise solution framework for assessing the early effect of COVID-19 on global stock indices - Sanjib Biswas (Computational Algorithms and Numerical Dimensions)
COVID-19 is undoubtedly the major crisis that the world has witnessed in the last many decades. The pandemic spread its effect on the socio-economic and cultural environment across the nations. The present study aims to introspect the performance of leading global indices vis-à-vis the recent pandemic. In this regard, the ongoing research demonstrates a Fuzzy Multi-Criteria Decision-Making (F-MCDM) framework to assess the early effects of the COVID-19 pandemic on nine leading global stock indices. The underlying intention is to examine whether the concern for lives and livelihoods affected the relative ranking of leading global indices. To this end, in this paper, the researchers put forth a fuzzy entropy (for determining criteria weights) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) methodology utilizing time series data for the performance analysis of global indices during July 01, 2019, to May 31, 2020, distributed in two phases such as phase 1: July 01, 2019, to December 31, 2019 (pre-crisis) and phase 2: January 01, 2020, to May 31, 2020 (crisis). During the study period, daily data has been collected to indicate variables (criteria) such as momentum, volume, spread, and historical and implied volatility. It is observed that global indices could maintain their positions without much variation. Singapore and Hong Kong stock exchanges show consistent top performance, while Brazil and the Indian market record poor performance. Surprisingly, despite being the country of origin of COVID-19, the Chinese market showed better resilience and improved its position. The findings also reveal that no significant herding exists. The reliability and stability of the fuzzy-entropy-MARCOS framework are demonstrated through comparison with other MCDM models and sensitivity analysis.