Apostolos G. Katsafados

• Adjunct Finance Professor at Athens University of Economics and Business

We combine machine learning algorithms (ML) with textual analysis techniques to forecast bank stock returns. Our textual features are derived from press releases of the Federal Open Market Committee (FOMC). We show that ML models produce more accurate out-of-sample predictions than OLS regressions, and that textual features can be more informative...

Motivated by the successful usage of machine learning around computer science and its wide acceptance from the finance literature, we utilize monthly data spanning the period 2008–2018 for the Euro area peripheral countries, in order to embark on a two-fold mission. First, to construct short-term prediction models for bank deposit flows in the Euro...

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 impact of the COVID‐19 pandemic and the associated government financial support schemes, aiming to protect against financial vulnerabilities, on the probability of default o...

This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observation...

This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observation...

This paper investigates the role of textual information in a U.S. bank merger prediction task. Our intuition behind this approach is that text could reduce bank opacity and allow us to understand better the strategic options of banking firms. We retrieve textual information from bank annual reports using a sample of 9,207 U.S. bank-year observation...

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 sentiment aspects, including communication and investors’ focus of attention to market wide sentiment. We employ quarter-bank level data and various measures for stoc...

Purpose Using textual analysis the authors study the relationship between social media sentiments and stock markets during the COVID-19 pandemic. Design/methodology/approach The study analysis is based on a sample of 1,616,007 tweets over the period January to June 2021 for seven countries. The authors process the tweets via the VADER analyzer the...

In this study, we employ the COVID-19 Twitter sentiment of seven countries to examine the stock market indexes. We conduct our analysis on a sample of 1,616,007 tweets over the period January to June 2021. We process the tweets based on the VADER analyzer, thereby producing both positive and negative sentiment measures. Particularly, we prove that...

Using textual analysis we study the relationship between social media sentiments and stock markets during the COVID-19 pandemic. Our analysis is based on a sample of 1,616,007 tweets over the period January to June 2021 for seven countries. We process the tweets via the VADER analyzer thereby producing both positive and negative sentiment measures....

This study examines empirically 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 sentiment aspects, including communication and investors’ focus of attention to market wide sentiment. We employ quarter-bank level data along with a variety of meas...

We investigate whether the textual sentiment affects European depositors' behavior in withdrawing their deposits. Following Loughran and McDonald's (2011) methodology, we construct two textual sentiments able to capture the perceived uncertainty. Our findings suggest that a high frequency of uncertainty and weak modal words in the ECB president's m...

We investigate whether the textual sentiment affects European depositors' behavior in withdrawing their deposits. We construct two textual sentiments able to capture the perceived uncertainty. Our findings suggest that a high frequency of uncertainty and weak modal words in the European Central Bank (ECB) president's monthly speeches leads both hou...

This study examines the predictive power of textual information from S-1 filings in explaining IPO underpricing. The author’s approach differs from previous research, as they utilize several machine learning algorithms to predict whether an IPO will be underpriced or not, as well as the magnitude of the underpricing. Using a sample of 2,481 U.S. IP...

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 listed U.S. banks over the period 1997 to 2015, using the Loughran and McDonald’s lists of positive and negative words for our textual analysis. We find that a highe...

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 listed U.S. banks over the period 1997 to 2015, using the Loughran and McDonald's lists of positive and negative words for our textual analysis. We find that a highe...