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 higher frequency of positive (negative) words in a bank's annual report relates to a higher probability of becoming a bidder (target). Our results remain robust to the inclusion of bank-specific control variables in our logistic regressions.

This study examines the predictive power of textual information from S-1 filings in explaining IPO underpricing. Our empirical approach differs from previous research, as we utilize several machine learning algorithms to predict whether an IPO will be underpriced, or not. We analyze a large sample of 2,481 U.S. IPOs from 1997 to 2016, and we find that textual information can effectively complement traditional financial variables in terms of prediction accuracy. In fact, models that use both textual data and financial variables as inputs have superior performance compared to models using a single type of input. We attribute our findings to the fact that textual information can reduce the ex-ante valuation uncertainty of IPO firms, thus leading to more accurate estimates.

PurposeUsing textual analysis the authors study the relationship between social media sentiments and stock markets during the COVID-19 pandemic.Design/methodology/approachThe 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 thereby producing both positive and negative sentiment measures.FindingsParticularly, the authors prove that higher positivism is associated with a short-term increase in stock prices. On the other side, negativism relates inversely to stock prices with long-term impact, in the case of English-spoken countries. Notably, the study results remain robust to the inclusion of various control variables, including virtual fear and Google vaccine indexes. Finally, the authors prove that positivism is associated with higher returns and lower volatility in the short-run, while negativism is linked with lower returns in the short run.Practical implicationsThe study analysis also has significant policy implications for researchers, investors and policymakers. First, researchers can employ our measures to quantify market sentiments and expand their research arsenal to incorporate social media trends, thus providing better explanatory power. Second, during times of severe uncertainty such as in a pandemic period, investors could beneficially take into account our textual measures and empirical results when using asset pricing models or constructing their portfolios. Third, the finding that the stock market is heavily governed by sentimental behaviors, especially during crisis periods, implies that policymakers including central banks, governments and capital market commissions must consider these sentiments before exerting their policies. In this regard, governments can effectively develop policy tools and approaches to manage recovery from the pandemic, which translates to greater long-term economic resilience. Moreover, central banks should accordingly adjust their monetary policy measures in order to stabilize financial markets, and by extension, to stop the pandemic from turning into a renewed financial crisis. For example, asset purchase program is considered the main instrument of this kind of intervention.Originality/valueThe authors confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. The paper should be of interest to readers in the areas of finance.

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Engineering and Banking Society (FEBS) for their valuable comments and suggestions. Apostolos Katsafados acknowledges financial support from the project "Strengthening Human Resources Research Potential via Doctorate Research" (MIS-5000432), implemented by the State Scholarships Foundation (ΙΚΥ) of Greece. George Leledakis greatly acknowledges financial support received from the Research Center of the Athens University of Economics and Business (EP-2256-01). All remaining errors and omissions are our own.

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 households and non‐financial corporations to withdraw their bank deposits. We also find that non‐financial corporations' deposits are more sensitive than households' deposits to these textual sentiments. These findings suggest that regulators and policymakers could expand the already existing early‐warning systems for the banking sector by considering the frequency of uncertainty and weak modal words in the ECB president's speeches.

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 observations during the period 1994-2016. To predict bidders and targets, we use textual information along with financial variables as inputs to several machine learning models. Our key findings suggest that: (1) when textual information is used as a single type of input, the predictive accuracy of our models is similar, or even better, compared to the models using only financial variables as inputs, and (2) when we jointly use textual information and financial variables as inputs, the predictive accuracy of our models is substantially improved compared to models using a single type of input. Therefore, our findings highlight the importance of textual information in a bank merger prediction task.

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