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 1994-2008, almost three-fourths of the word count identified as negative by the commonly used Harvard Dictionary represents words that typically do not have negative meaning in a financial context. Words like tax, board, foreign, vice, and liability, simply describe company operations. Two potential solutions are explored. First, we develop an alternative negative word list that better reflects the tone of financial text. Second, we show that using a common term weighting scheme reduces the noise introduced by misclassifications. Without term weighting, our list generally outperforms the Harvard list; with weighting the performance appears comparable. However, we also find evidence that some of the power of the Harvard list could be attributable to misclassified words that proxy for other effects. Five other word classifications (positive, uncertain, litigious, strong modal, and weak modal) are also considered. We link the word lists to 10 K filing returns, trading volume, subsequent return volatility, fraud, material weakness, and unexpected earnings.

Keywords: Textual analysis, Harvard Dictionary, negative word counts, term weighting

JEL Classifications: G10, G14

Working Paper Series