Exploring the relationship between bank liquidity risk and the media sentiment index via big data technology: a study during the COVID-19 pandemic and the Russia–Ukraine conflict

Abstract

This study assesses the influence of media sentiment on liquidity risk during financial crises within the context of the Russian financial market. Trade sanctions, especially the suspension of the SWIFT network, have profoundly impacted Russian banking sector liquidity. Given this vulnerable situation, the study utilizes media sentiment data to analyze banking sector performance via monthly data from January 2012 to December 2024. This study applies big data modeling techniques to construct positive, negative, and mixed media sentiment indices to evaluate their relationship with bank liquidity risk. The findings, which are based on GMM estimation, reveal that the media sentiment index is significantly and positively associated with bank liquidity risk. Moreover, negative and mixed media sentiment indices have a detrimental effect on bank performance, leading to an appreciation of liquidity risk. Additionally, the study finds that interbank networks play a vital role in mitigating liquidity risk. Overall, media sentiment significantly impacts banking performance. The findings provide practical implications for banking sector policymakers, financial market regulators, and investors in making informed decisions during both normal and crisis periods.