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

By Thomas M. Arnold. 2011

1,064 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/447f098c2a8618d13b641882b2d473c80ae70bcf

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Citations in the corpus

Earnings conference calls and stock returns: The incremental informativeness of textual tone (2012, positive): “We add to the textual analysis literature by finding out-of-sample support for the assertion of Loughran and McDonald (2011) in that the custom, earnings specific dictionary is much more powerful in detecting relevant conference call tone.”
Big data techniques in auditing research and practice: Current trends and future opportunities (2018, positive): “This is important, because finance-specific language solutions have been shown to perform substantially better than general solutions when used in a finance context (Loughran & McDonald, 2011).”
Offshore activities and financial vs operational hedging (2017, positive): “Regarding SEC disclosures, earlier work includes Hanley and Hoberg (2010), Hoberg and Phillips (2015), Loughran and McDonald (2011) and Garcia and Norli (2012).”
Media Content and Stock Returns: The Predictive Power of Press (2015, positive): “Media articles were downloaded from LexisNexis UK and media tone is determined by using textual analysis to identify words that are either positive or negative according to Loughran and McDonald’s (2011) financial news word lists.”
Sentiment during recessions∗ (2011, positive): “17As mentioned previously, our study uses the Loughran and McDonald (2010) word lists.”
The Use of Word Lists in Textual Analysis (2015, positive): “The 10-K documents were parsed using the methods described in Loughran and McDonald [2014]. The three data screens having the most impact on the sample are requiring the firms to have a Center for Research in Security Prices (CRSP) PERMNO (dropping 103,325 observations); removing firms with a stock ”
Executive Extraversion: Career and Firm Outcomes (2018, positive): “analyzes the text of company disclosures to measure tone (Tetlock, Saar-Tsechansky, and Macskassy, 2008; Loughran and McDonald, 2011; Jegadeesh and Wu, 2013), uncertainty (Loughran and McDonald, 2013), readability (Li, 2008; Loughran and McDonald, 2014), and uncover deception (Larcker and Zakolyukin”
Earnings Conference Call Content and Stock Price: The Case of REITs (2010, positive): “However, the results are significantly stronger for measures which utilize the customized dictionary, providing out of sample support in a context which has been previously untested for the argument of Henry (2008) and Loughran and McDonald (2009) that custom dictionaries may be preferable.”
Quantifying Sentiment with News Media across Local Housing Markets (2018, positive): “Following Loughran and McDonald (2011), I also expand the list with inflections and tenses that retain the original meaning of each word.”
Intraday online investor sentiment and return patterns in the U.S. stock market (2017, positive): “4 For example, Loughran and McDonald (2011) extract all words occurring in at least 5% of 121,217 10-”
The Use of EDGAR Filings by Investors (2017, positive): “For example, Loughran and McDonald [2011] linked the tone of the annual report with 10-K filing returns, trading volume, and postfiling stock market volatility while Feldman, Govindaraj, Livnat, and Segal [2010] related changes in tone in the management discussion and analysis (MD&A) section to cont”
Don't Believe the Hype: Local Media Slant, Local Advertising, and Firm Value (2012, positive): “To quantify slant, we use the financial dictionary of Loughran and McDonald (2011).”