More than Words: Quantifying Language to Measure Firms' Fundamentals
By Paul C. Tetlock, Maytal Saar-Tsechansky, Sofus A. Macskassy. 2008
2,183 citations recorded by the source corpus.
Subfields: Accounting, Finance, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
- Artyom Durnev
- Diego García
- Gerard Hoberg
- Kenneth R. Ahern
- Thomas Renault
- C. Mangen
- Camila C. Pereira
- Timothy G. Pollock
In Finance
- Diego García
- Joseph E. Engelberg
- Thomas Renault
- D. Sosyura
- Stefan Feuerriegel
- Kenneth R. Ahern
- Paul C. Tetlock
- Bill McDonald
In Management Science and Operations Research
- Stefan Feuerriegel
- Erik Cambria
- Frank Xing
- Roy E. Welsch
- Vadlamani Ravi
- B. Shravan Kumar
- Qing Li
- Jaywon Lee
Citations in the corpus
- Improving stock market prediction via heterogeneous information fusion (2017, background): “Various studies have found that financial news can dramatically affect the stock price [14, 15, 16, 17, 18, 19].”
- Textual Sentiment in Finance: A Survey of Methods and Models (2014, neutral): “Tetlock et al. (2008) label all news stories with a fraction of negative words (Neg) in the previous year’s top (bottom) quartile as negative (positive) stories.”
- Good debt or bad debt: Detecting semantic orientations in economic texts (2013, methodology): “Previous research on textual analysis in a financial context has primarily relied on the use of word categorization (“bag of words”) methods to measure tone (Tetlock, 2007; Tetlock et al., 2008; Engelberg, 2008).”
- Text mining for market prediction: A systematic review (2014, methodology): “Some of these dictionaries are specially put together by a market expert like the one used by Wuthrich et al. (1998) or Peramunetilleke and Wong (2002) or they are more specific to a specific field like psychology in the case of Harvard-IV-4 which has been used in the work of Tetlock et al. (2008).”
- Barron's Red Flags: Do They Actually Work? (2011, neutral): “Tetlock, Saar-Tsechansky, and Macskassy [2008] link the tone of newspaper articles to future firm earnings.”
- Shocking language: understanding the macroeconomic effects of central bank communication (2016, positive): “…in the finance literature, where word lists are chosen to reflect positive and negative tone and applied to media text or company results releases; see, for example, Tetlock (2007), Tetlock, Saar-Tsechansky, and Macskassy (2008), Loughran and McDonald (2011) and Loughran and McDonald (2014).”