Research by Notre Dame Finance Department Professors Tim Loughran and Bill McDonald was featured on a March 21st segment of CNBC's Squawk on the Street. Guest Adam Parker, Trivariate Research CEO, references Loughran and McDonald's 2011 Journal of Finance article, "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks." The discussion starts at the 3:00 minute mark in video clip below:
The paper's abstract outlines the strategy used and the outcomes that led to Loughran and McDonald's findings.
Please contact NDIGI@nd.edu with further inquiries about this or any other research by the Finance Department.
When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks
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, uncertainty, 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.
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, uncertainty, 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.