Internet Appendix for "When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks" *

By Tim Loughran, Bill McDonald. 2011

4,203 citations recorded by the source corpus.

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Citations in the corpus

Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network (2018, background): “Loughran and McDonald [14] provided a manually made ﬁnancial sentiment dictionary which contains 6 sentiment dimensions.”
Textual Sentiment in Finance: A Survey of Methods and Models (2014, methodology): “Loughran and McDonald (2011a) find that almost three-fourths (73.8 percent) of the negative word counts in the GI/Harvard list are attributable to words that are typically not negative in a financial context.”
Good debt or bad debt: Detecting semantic orientations in economic texts (2013, methodology): “To accommodate the domain speciﬁc requirements, we augmented the MPQA-based dictionary with the ﬁnance-speciﬁc lists compiled by Loughran and McDonald (2011).”
Large Sample Evidence on the Informativeness of Text in Analyst Reports (2012, background): “Recent studies have developed dictionaries specifically for financial contexts (Loughran and McDonald 2011; Henry 2006).”
Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures (2017, background): “Sentiment Dictionary [26] No negation recognition Full content vs.”
Quantifying Sentiment with News Media across Local Housing Markets (2018, methodology): “Following Loughran and McDonald (2011), I also expand the list with inflections and tenses that retain the original meaning of each word.”
Disclosure Tone and Shareholder Litigation (2011, background): “sentiment of Loughran and McDonald (2009). Just as they conclude that “textual analysis is not”
Can Investors Detect Managers' Lack of Spontaneity? Adherence to Predetermined Scripts during Earnings Conference Calls (2016, unknown): “to Mayew (2008). For each call in my study, I obtain a list of all analyst participants and match”
A Corporate Culture Channel: How Increased Shareholder Governance Reduces Firm Value (2013, background): “, Antweiler and Frank (2004); Tetlock (2007); Loughran and McDonald (2011); Hoberg, Phillips, and Prabhala (2013)).”
The Economics of Disclosure and Financial Reporting Regulation: Evidence and Suggestions for Future Research (2015, background): “20 and natural language processing allow us to construct new measures for narrative disclosures, some of which have quality dimensions (e.g., Li, 2008, 2010; Loughran and McDonald, 2011; Lang and Stice-Lawrence, 2014).”
Intraday online investor sentiment and return patterns in the U.S. stock market (2017, methodology): “4 For example, Loughran and McDonald (2011) extract all words occurring in at least 5% of 121,217 10-”
Using 10-K Text to Gauge Financial Constraints (2015, methodology): “Like the Loughran and McDonald (2011) creation of six word lists (negative, positive, uncertain, litigious, strong modal, and weak modal), we examine tens of thousands of words that appear in at least 5% of all 10-”
The Structure of Voluntary Disclosure Narratives: Evidence from Tone Dispersion: the structure of voluntary disclosure narratives (2015, unknown): “These studies often use the “bag-of-words” approach to quantify tone (Henry 2008; Tetlock 2007; Loughran and McDonald 2011), ignoring the underlying narrative structure in which tone words are used.”