When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks

Abstract

Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10-Ks during 1994 to 2008, almost three-fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10-K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings. * Loughran and McDonald are with University of Notre Dame. We are indebted to Paul Tetlock for comments on a previous draft. We also thank an anonymous referee, an anonymous associate editor, and seminar participants at the 2009 FMA meeting, University of Notre Dame, and York University for helpful comments. We thank Hang Li for research assistance.

Media Content and Stock Returns: The Predictive Power of Press

“…5 Some previous studies use the Harvard psychosocial dictionary to categorize the words featured in financial news articles. Loughran and McDonald (2011) argue, however, that many words that appear in negative categories in the Harvard psychosocial dictionary are not negative in a financial sense: they are merely descriptive terms. These are words such as depreciation, liability, foreign, and mine.…”

“…4 The content of the media articles is analysed to determine the number of positive and negative words they contain. The words in each article are compared to Loughran and McDonald's (2011) positive and negative financial word lists to identify the number of positive and negative words in a financial context. 5 Some previous studies use the Harvard psychosocial dictionary to categorize the words featured in financial news articles.…”

“…Previous studies, such as those of Tetlock (2007) and Tetlock, Saar-Tsechansky, and Macskassy (2008), use the Harvard psychosocial dictionary to identify words of different categories in news articles. However, Loughran and McDonald (2011) create a new word list of financial-news-specific words that have greater explanatory power over stock returns than the Harvard psychosocial dictionary categories. Tetlock, Saar-Tsechansky, and Macskassy (2008), among others, only consider the effect of negative words in news stories on stock returns.…”

“…However, in recent times researchers have begun to analyse linguistic data contained in media articles using textual analysis in an attempt to capture hard-to-quantify firm-specific information in news media data and determine the impact on stock prices (for example, Tetlock, 2007;Tetlock, Saar-Tsechansky, and Macskassy, 2008;Garcia, 2013;Loughran and McDonald, 2011;among others). By using a quantitative measure of the semantics in the language used in news articles, it is possible to measure the effects of investor reaction to such news events and identify common patterns concerning the way asset prices react to news in general, whether positive or negative.…”

“…We extend the existing literature in several aspects. We first consider both positive as well as negative news media content, constructed from Loughran and McDonald's (2011) financial-news-specific word lists, to study the predictability of stock returns. 2 Previous studies, such as that of Tetlock (2007) and 1.…”