Purpose The goal of the study is to examine the effects of news sentiment and topics dominating in the news field prior to the initial public offering (IPO) on the IPO underpricing. Design/methodology/approach The authors’ approach has several steps. The first is textual analysis. To detect the dominating topics in the news, the authors use Latent Dirichlet allocation. The authors use bidirectional encoder representations from transformers (BERT) pretrained on financial news corpus to evaluate the tonality of articles. The second is evaluation of feature importance. To this end, a linear regression with robust estimators and Classification and Regression Tree and Random Forest are used. The third is data. The text data consists of 345,731 news articles from Thomson Reuters related to the USA in the date range from 1 January 2011 to 31 May 2018. The data contains all the possible topics from the website, excluding anything related to sports. The sample of 386 initial public offerings completed in the USA from 1 January 2011 to 31 May 2018 was collected from Bloomberg Database. Findings The authors found that sentiment of the media regarding the companies going public influences IPO underpricing. Some topics, namely, the climate change and environmental policies and the trade war between the US and China, have influence on IPO underpricing if they appear in the media prior to the IPO day. Originality/value The puzzle of IPO underpricing is studied from the point of Narrative Economics theory for the first time. While most of the works cover only some specific news segment, we use Thomson Reuters news aggregator, which uses such sources The New York Post, CNN, Fox, Atlantic, The Washington Post ? Buzzfeed. To evaluate the sentiment of the articles, a state-of-the-art approach BERT is used. The hypothesis that some common narratives or topics in the public discussion may impose influence on such example of biased behaviour like IPO underpricing has also found confirmation.

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