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Abnormal tone and abnormal returns: An event study analysis
Team member Keven Bluteau
   
Project partner Kris Boudt
   
Abstract We propose the Cumulative Abnormal Tone (CAT) framework for tracking the normal and abnormal dynamics of textual tone around events. In our framework, we use text-mining techniques to derive the normal tone based on market and sector-wide news. We then focus on corporate events and track the abnormal tone dynamics around that event. This leads to a cumulative abnormal tone chart. We apply the CAT framework to the analysis of cumulative abnormal returns across earnings press releases. We find that the analysis of firmís abnormal tone provides investors with relevant predictive information on the firmís stock performance.
   
Keywords textual tone, abnormal tone, abnormal return, sentiment analysis, event-study
   
Project homepage http://https://ssrn.com/abstract=3192064
   
Type of project Fundamental research project
Research area Finance, machine learning
Status Completed
Start of project 1-7-2018
End of project 31-12-2019
Contact David Ardia