2014 Hillcrest Behavioral Finance Award
The Use of Word Lists in Textual Analysis
Tim Loughran (University of Notre Dame)
Bill McDonald (University of Notre Dame)
The paper utilizes Big Data techniques to study methods of assessing the tone of business documents as part of stock selection analysis. Textual analysis is becoming increasingly common in accounting and finance research. The approach groups words into positive, negative, or other sentiment categories and, based on these categories, the overall tone of a financial document can be determined. Loughran and McDonald’s paper argues that researchers should use only data created specifically for investments when gauging document tone.
This paper was published in the Journal of Behavioral Finance, Volume 16, 2015 – Issue 1