In this paper, we use the sentiment of annual reports to gauge the likelihood of a bank to
participate in a merger transaction. We conduct our analysis on a sample of annual reports of …

Purpose Using textual analysis the authors study the relationship between social media
sentiments and stock markets during the COVID-19 pandemic. Design/methodology/approach …

This study examines the predictive power of textual information from S-1 filings in explaining
initial public offering (IPO) underpricing. The authors’ approach differs from previous …

We investigate whether the textual sentiment affects European depositors’ behavior to
withdraw their deposits. Following Loughran and McDonald (2011) methodology, we construct …

This paper investigates the role of textual information in a US bank merger prediction task.
Our intuition behind this approach is that text could reduce bank opacity and allow us to …

This paper develops a logistic regression model in an in‐house credit assessment system (ICAS)
framework for predicting corporate defaults in the Greek economy. We consider the …

The objective of this study is to introduce new term-weighting schemes and to investigate
whether these schemes outperform the existing ones in IPO tasks. Specifically, various textual …

The purpose of this study is twofold. First, to construct short-term prediction models for bank
deposit flows in the Euro area peripheral countries, employing machine learning techniques. …

We release EDGAR-CORPUS, a novel corpus comprising annual reports from all the publicly
traded companies in the US spanning a period of more than 25 years. To the best of our …

This study empirically examines whether the stock price crash risk of euro area banks is
affected by crisis sentiment during the period 2004–2020. We introduce a diverse set of crisis …