Costly Information Processing: Evidence from Earnings Announcements

By Joseph E. Engelberg. 2008

312 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/20c1909492638f6a5f716673d6fefa796bb5ff8c

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Citations in the corpus

How are shorts informed (2012, methodology): “Tetlock, Saar-Tsechansky, and Macskassy (2008) and Engelberg (2008) show that, indeed, the qualitative content of the information contained in news stories can predict both earnings surprises and short-term returns.”
On the Importance of Text Analysis for Stock Price Prediction (2014, methodology): “We also tried bigram and dependency-based features which were shown to be effective for similar tasks in previous works (Wang and Manning, 2012; Kogan et al., 2009; Engelberg, 2008).”
customers as advisors: the role of social media in financial markets (2011, unknown): “…contributes to the growing body of literature that examines the media’s effect on stock markets (e.g., Barber and Loeffler (1993); Busse and Green (2002); Antweiler and Frank (2006); Tetlock (2007); Engelberg (2008); Tetlock et al. (2008); Fang and Peress (2009); Engelberg and Parsons (2011)).”
Management’s tone change, post earnings announcement drift and accruals (2010, unknown): “Engelberg (2008) is another extension of the Tetlock (2007) and Tetlock et al. (2008) papers.”
Predicting Risk from Financial Reports with Regression (2009, background): “Some papers relate news articles to earning forecasts, stock returns, volatility, and volume (Koppel and Shtrim-berg, 2004; Tetlock, 2007; Tetlock et al., 2008; Gaa, 2007; Engelberg, 2007).”
Earnings Conference Call Content and Stock Price: The Case of REITs (2010, unknown): “Engelberg (2008) applies the method outlined in Tetlock (2007) to measure the qualitative content of Dow Jones News Service stories about firms’ earnings announcements to analyze the link between “soft” information and equity prices.”
Quantifying Sentiment with News Media across Local Housing Markets (2018, background): “Additional studies have further applied textual analysis techniques to capture the sentiment of earnings announcements, investor chat rooms, corporate 10-K reports, and initial public o↵ering (IPO) prospectuses and linked to outcomes such as firm earnings, stock returns, and trading volume (Engelber”
Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research (2016, methodology): “Smales (2014) and Engelberg (2008) used data from the Dow Jones News Service.”
DeepClue: Visual Interpretation of Text-Based Deep Stock Prediction (2019, methodology): “On using news articles for the prediction, Engelberg [22] and Tetlock et al.”