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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...
Persistent link: https://www.econbiz.de/10015265577
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...
Persistent link: https://www.econbiz.de/10015212186
Persistent link: https://www.econbiz.de/10014581522
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...
Persistent link: https://www.econbiz.de/10012847181
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...
Persistent link: https://www.econbiz.de/10015222803
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...
Persistent link: https://www.econbiz.de/10015245906
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Persistent link: https://www.econbiz.de/10015359529
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...
Persistent link: https://www.econbiz.de/10015214576