Big data and artificial intelligence (AI) have transformed the finance industry by altering the
way data and information are generated, processed, and incorporated into decision-making …

With the increasing complexity of financial statement manipulation, relying solely on
quantitative financial data may not effectively detect financial fraud. While textual analysis …

This study examines whether internal and external perceptions and perception differences
can significantly improve the effectiveness of financial fraud detection. Specifically, we …

Using text-mining analyses, we find that firms are more concerned about systemic shocks,
leading to a subsequent decrease in systemic risk exposure. This finding is robust to …

This study introduces multimodal data machine learning framework to predict stock crashes.
It encapsulates market data, graph data cultivated from industry affiliations through …

We utilise measures of economic policy uncertainty (EPU) and geopolitical risk (GPR), as
well as commitments of traders (COTs), to investigate the influence of uncertainty on …

Sentiment analysis has emerged as a prominent research domain within the realm of natural
language processing, garnering increasing attention and a growing body of literature. While …

We construct a sentiment-based index of global financial stress (s-GFS index) for the period
January 2004-December 2020. It builds on a novel methodological approach, which …

Human capital (HC) is increasingly important to corporate value creation. Unlike other
assets, however, HC is not currently subject to well-defined measurement or disclosure …

We propose sentiment-based indicators of real estate market stress for the USA, the UK,
Canada, Australia, India, and on the global scale. The global and country-level indicators …