All the News That's Fit to Reprint: Do Investors React to Stale Information?
Open accessAbstract
This paper tests whether stock market investors appropriately distinguish new and old information about firms. The staleness of a news story is its textual similarity to the previous ten stories about the same firm. The tests show that firms' stock returns are less responsive to stale news. Even so, a firm's return on the day of stale news negatively predicts its return in the following week. Individual investors increase their tendencies to aggressively trade on news when news is stale. The return reversal after stale news is significantly larger in stocks with above-average individual investor trading activity on the day of the news. These results and others support the idea that individual investors overreact to stale information, leading to temporary movements in firms' stock prices. The findings are inconsistent with alternative interpretations of the empirical measure of staleness and the return reversal.
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NR_G's return predictability is more substantial when the news is less similar to prior stories.
“Our findings are consistent with the notion that 𝑁𝑅 𝐺 's return predictability is more substantial when the news is less similar to prior stories, thus supporting our hypothesis.”
From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Tetlock (2011)
Cites
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Giving content to investor sentiment: The role of media in the stock market
TETLOCK, P. C. (2007) · The Journal of Finance · doi:10.1111/j.1540-6261.2007.01232.x · Cited by 4,827 · see citing works
Self-citationTrading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors
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Can ChatGPT forecast stock price movements? Return predictability and large language models
Lopez-Lira, A., & Tang, Y. (2026) · Journal of Financial Economics · doi:10.1016/j.jfineco.2026.104335 · Cited by 425 · see citing works
News or Noise? The Stock Market Reaction to Different Types of Company-Specific News Events
Sprenger, T. O., & Welpe, I. M. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1734632 · Cited by 28 · see citing works
ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy?
Chen, J., Tang, G., Zhou, G., & Zhu, W. (2025) · arXiv · doi:10.48550/arxiv.2502.10008 · Cited by 6 · see citing works
- Authors
- Tetlock, P. C.
- Corresponding author
- Tetlock, Paul C.
- Venue
- Review of Financial Studies
- Publisher
- Oxford University Press (OUP)
- Published
- 2011-01-24
- Volume
- 24
- Issue
- 5
- Pages
- 1481-1512
- ISSN
- 0893-9454 (ISSN-L)
- 1465-7368
- OpenAlex
- W3125011952
- Cited by
- 677
- References
- 6
- Citations by year
- 2026: 30 · 2025: 47 · 2024: 72 · 2023: 55 · 2022: 48 · 2021: 86 · 2020: 42 · 2019: 44 · 2018: 39 · 2017: 35 · 2016: 36 · 2015: 34 · 2014: 32 · 2013: 28 · 2012: 17
- FWCI
- 55.64
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Review of Financial Studies (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Keywords
- investor overreaction, stock returns, return reversal
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