Giving Content to Investor Sentiment: The Role of Media in the Stock Market

By Paul C. Tetlock. 2005

4,257 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Sentiment & Opinion Analysis, Economics & Operations Research

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Citations in the corpus

Sentiment Predictability for Stocks (2017, neutral): “form of the ”pessimism” was the number of negative words in the Wall Street Journal. The second work attempts to explain the relationship between negative words and individual stock pricing, found in [Tet07]. Further attempts under this work using more recent Natural Language Processing Developments”
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction (2017, neutral): “For instance, Tetlock et al. [24] extracts and quantifies the optimism and pessimism of Wall Street Journal reports and observes that trading volume tends to increase after pessimism reports and high pessimism scored reports tend to be followed by a downtrend and a reversion of market prices.”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, neutral): “s based on nancial news are still dicult to obtain due to the complexity of natural language and the eciency of markets where historic prices contribute only marginally to explaining future returns [47]. Both diculties underline the necessity for more complex models in the eld of deep learning. Neve”
Textual Sentiment in Finance: A Survey of Methods and Models (2014, neutral): “Stock returns are found to predict textual sentiment by Tetlock (2007) and Garcia (2012), who use news stories as the information source.”
Good debt or bad debt: Detecting semantic orientations in economic texts (2013, neutral): “One promising approach is to consider media as a source of investor sentiments (Mitra & Mitra, 2011, 2010; Mitra, Mitra, & diBartolomeo, 2009; Dzielinski, Rieger, & Talpsepp, 2011; Tetlock, 2007; Barber & Odean, 2008; Tet-lock, Saar-Tsechansky, & Macskassy, 2008).”
The effect of news and public mood on stock movements (2014, positive): “This hypothesis is further confirmed by Tetlock’s study regarding the interaction between stock movements and daily news, particularly pessimistic news [40].”
Between the hammer and the anvil: The impact of economic sanctions and oil prices on Russia’s ruble (2016, neutral): “Tetlock (2007) look at the interactions between the media and the stock market using daily information from the Abreast of the Market section of The Wall Street Journal.”
Monetary policy uncertainty (2017, neutral): “…by institutional measures of central bank policy actions such as voting behavior and newly-constructed 4For example, Davis, Piger, and Sedor (2006), Tetlock (2007), Engelberg (2008), Tetlock, Saar-Tsechansky and Macskassy (2008), Demers and Vega (2010), Hoberg and Phillips (2010), Feldman,…”
customers as advisors: the role of social media in financial markets (2011, positive): “Following Tetlock et al. (2008), we compute the average fraction of negative words across”
Market-Wide Attention, Trading, and Stock Returns (2015, neutral): “13Studies suggest that some news contains new information and thus has an impact on future prices, e.g., Huberman and Regev (2001), and Tetlock (2007, 2011).”
The Effect of Issuer Conservatism on IPO Pricing and Performance (2013, positive): “For instance, Tetlock (2007) and Tetlock et al. (2008) use the Harvard measure to examine negative tone in newspaper articles.”
Empirical Capital Structure: A Review (2009, neutral): “The dependent variable is book leverage, and the rest of the variables are self-explanatory.4 The main coefficient of interest, b, is on the firm’s “external finance weighted-average” market-to-book ratio, defined as: ( M B ) efwa,t−1 = t−1∑ s=0 es + ds t−1∑ r=0 er + dr · ( M B ) s , (4.1) 2 See Aue”
The Causal Impact of Media in Financial Markets (2011, positive): “Staring with Tetlock (2007) and Tetlock, Tsechansky, and Macskassy (2008), a number of papers have shown that news stories in national newspapers are associated with substantial price responses.”
How Valuable Is FinTech Innovation? (2019, positive): “12 Tetlock (2007) applies large-scale, quantitative content analysis to examine of the effects of media sentiment on stock returns and trading volume.”
Costly Information Processing: Evidence from Earnings Announcements (2008, positive): “To measure the qualitative content of the earnings surprise, I apply a method introduced by Tetlock (2007) that counts the number of negative words as defined by the Harvard Psychological Dictionary in the text of Dow Jones News Service (DJNS) stories about firms' earnings announcements.”