The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Lazy Prices
Abstract
Using the complete history of regular quarterly and annual filings by U.S. corporations, we show that changes to the language and construction of financial reports have strong implications for firms’ future returns and operations. A portfolio that shorts “changers” and buys “nonchangers” earns up to 188 basis points per month in alpha (over 22% per year) in the future. Moreover, changes to 10‐Ks predict future earnings, profitability, future news announcements, and even future firm‐level bankruptcies. Unlike typical underreaction patterns, we find no announcement effect, suggesting that investors are inattentive to these simple changes across the universe of public firms.
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Cites
When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks
LOUGHRAN, T., & MCDONALD, B. (2011) · The Journal of Finance · doi:10.1111/j.1540-6261.2010.01625.x · Cited by 6,015
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Oster, E. (2019) · Journal of Business & Economic Statistics · doi:10.1080/07350015.2016.1227711 · Cited by 5,367
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TETLOCK, P. C. (2007) · The Journal of Finance · doi:10.1111/j.1540-6261.2007.01232.x · Cited by 4,827
Selection on observed and unobserved variables: Assessing the effectiveness of Catholic schools
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DA, Z., ENGELBERG, J., & GAO, P. (2011) · The Journal of Finance · doi:10.1111/j.1540-6261.2011.01679.x · Cited by 3,067
Annual report readability, current earnings, and earnings persistence
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More Than Words: Quantifying Language to Measure Firms' Fundamentals
TETLOCK, P. C., SAAR‐TSECHANSKY, M., & MACSKASSY, S. (2008) · The Journal of Finance · doi:10.1111/j.1540-6261.2008.01362.x · Cited by 2,304
Textual Analysis in Accounting and Finance: A Survey
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Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings?
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Cited by
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
How to talk when a machine is listening: Corporate disclosure in the age of AI
Cao, S., Jiang, W., Yang, B., & Zhang, A. L. (2023) · The Review of Financial Studies · doi:10.1093/rfs/hhad021 · Cited by 222
Discretionary Information in ESG Investing: A Text Analysis of Mutual Fund Prospectuses
Andrikogiannopoulou, A., Krueger, P., Mitali, S. F., & Papakonstantinou, F. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4082263 · Cited by 18
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
Empirical Asset Pricing via Machine Learning: The Role of Research Design Choices
Lalwani, V., Meshram, V., & Jindal, V. (2026) · European Financial Management · doi:10.1111/eufm.70033
Metadata
- Authors
- COHEN, L., MALLOY, C., & NGUYEN, Q.
- Venue
- The Journal of Finance
- Publisher
- Wiley
- Published
- 2020-02-22
- Volume
- 75
- Issue
- 3
- Pages
- 1371-1415
- ISSN
- 0022-1082 (ISSN-L)
- 1540-6261
- OpenAlex
- W4252402383
- Cited by
- 380
- References
- 32
- FWCI
- 66.57
- Type
- journal article
- Language
- en
- Open access
- closed
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