Using Textual Analysis to Identify Merger Participants: Evidence from the U.S. Banking Industry

Katsafados, Apostolos G.; Androutsopoulos, Ion; Chalkidis, Ilias; Fergadiotis, Emmanouel; Leledakis, George N.; Pyrgiotakis, Emmanouil G.

doi:10.2139/ssrn.3474583

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Pre-processing and Bag-of-words1

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2020

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“…As a first step, we remove all acronyms, abbreviations, single letter words, numbers, punctuation marks, and stop words (Gandhi et al, 2019;Katsafados et al, 2020). This filtering procedure has the benefit of reducing the informational opaqueness of the textual inputs, which contributes to superior prediction performance.…”

Section: Pre-processing and Bag-of-wordsmentioning

confidence: 99%

Textual Information and IPO Underpricing: A Machine Learning Approach

et al. 2020

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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.

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Section: Pre-processing and Bag-of-wordsmentioning

confidence: 99%