Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-15T15:31:14.996Z Has data issue: false hasContentIssue false
  • English
  • Français

PEAD.txt: Post-Earnings-Announcement Drift Using Text

Published online by Cambridge University Press:  17 October 2022

Vitaly Meursault Open the ORCID record for Vitaly Meursault [Opens in a new window] ,
Pierre Jinghong Liang Open the ORCID record for Pierre Jinghong Liang [Opens in a new window] ,
Bryan R. Routledge Open the ORCID record for Bryan R. Routledge [Opens in a new window]  and
Madeline Marco Scanlon Open the ORCID record for Madeline Marco Scanlon [Opens in a new window]
Vitaly Meursault*
Affiliation:
Federal Reserve Bank of Philadelphia Research Department
Pierre Jinghong Liang
Affiliation:
Carnegie Mellon University Tepper School of Business liangj@andrew.cmu.edu
Bryan R. Routledge
Affiliation:
Carnegie Mellon University Tepper School of Business routledge@cmu.edu
Madeline Marco Scanlon
Affiliation:
University of Pittsburgh Katz School of Business mms238@pitt.edu
*
vitaly.meursault@phil.frb.org (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD. The magnitude of PEAD.txt is considerable even in recent years when the classic PEAD is close to 0. We explore our text-based empirical model to show that the calls’ news content is about details behind the earnings number and the fundamentals of the firm.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 58 , Issue 6 , September 2023 , pp. 2299 - 2326
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We are grateful for the helpful comments of the conference and seminar participants at the 2021 SFS Cavalcade North America, 2021 FARS, ETH Zurich, Bocconi University, Indiana University, and Brown University. We thank Andrew Gross for his research assistance with the annotation task. Meursault is grateful for the 2020 William W. Cooper Doctoral Dissertation Award and PNC Presidential Fellowship for Outstanding Research on the Future of Financial Services that supported this project while he was a Ph.D. student at the Tepper School of Business, Carnegie Mellon University. We also thank an anonymous referee, Sean Cao (a referee), and Jarrad Harford (the editor) for their guidance throughout the revision and publication process. The views expressed in this article are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. Any errors or omissions are the responsibility of the authors. No statements here should be treated as legal advice.

References

Ali, A.; Chen, X.; Yao, T.; and Yu, T.. “Can Mutual Funds Profit from Post Earnings Announcement Drift? The Role of Competition.” Journal of Banking & Finance, 114 (2020), 105774.Google Scholar
Ball, R., and Brown, P.. “An Empirical Evaluation of Accounting Income Numbers.” Journal of Accounting Research, 6 (1968), 159.Google Scholar
Bartov, E.; Radhakrishnan, S.; and Krinsky, I.. “Investor Sophistication and Patterns in Stock Returns After Earnings Announcements.” Accounting Review, 75 (2000), 43–63.Google Scholar
Beltagy, I.; Peters, M. E.; and Cohan, A.. “Longformer: The Long-Document Transformer.” ArXiv Working Paper, available at https://arxiv.org/abs/2004.05150 (2020).Google Scholar
Bernard, V. L., and Thomas, J. K.. “Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium?” Journal of Accounting Research, 27 (1989), 1.Google Scholar
Brandt, M. W.; Kishore, R.; Santa-Clara, P.; and Venkatachalam, M.. “Earnings Announcements are Full of Surprises.” Working Paper, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=909563 (2008).Google Scholar
Brown, N. C.; Crowley, R. M.; and Elliott, W. B.. “What are You Saying? Using Topic to Detect Financial Misreporting.” Journal of Accounting Research, 58 (2020), 237–291.Google Scholar
Cao, S.; Jiang, W.; Wang, J. L.; and Yang, B.. “From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses.” Technical Report, National Bureau of Economic Research (2021a).Google Scholar
Cao, S.; Jiang, W.; Yang, B.; and Zhang, A. L.. “How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI.” Technical Report, National Bureau of Economic Research (2020).Google Scholar
Cao, S.; Kim, Y.; Wang, A.; and Xiao, H.. “From Words to Syntax: Identifying Context-Specific Information in Textual Analysis.” Working Paper, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3568504 (2021b).Google Scholar
Cao, S.; Yang, B.; and Zhang, A. L.. “Deep Learning Mutual Fund Disclosure: Risk Sentiment, Risk Taking, and Performance.” Working Paper (2021c).Google Scholar
Cao, S. S., and Narayanamoorthy, G.. “Earnings Volatility, Post-Earnings Announcement Drift, and Trading Frictions.” Journal of Accounting Research, 50 (2012), 41–74.Google Scholar
Carhart, M. M. “On Persistence in Mutual Fund Performance.” Journal of Finance, 52 (1997), 57–82.Google Scholar
Chordia, T.; Subrahmanyam, A.; and Tong, Q.. “Have Capital Market Anomalies Attenuated in the Recent Era of High Liquidity and Trading Activity?” Journal of Accounting and Economics, 58 (2014), 41–58.Google Scholar
Coleman, B.; Merkley, K. J.; and Pacelli, J.. “Human Versus Machine: A Comparison of Robo-Analyst and Traditional Research Analyst Investment Recommendations.” Accounting Review, 95 (2022), 221–244.Google Scholar
Cox, J. “Market Fragmentation and Post-Earnings Announcement Drift.” Journal of Economics and Finance, 44 (2020), 587–610.Google Scholar
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K., “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, MN: Association for Computational Linguistics (2019), 4171–4186.Google Scholar
Engelberg, J. “Costly Information Processing: Evidence from Earnings Announcements.” Working Paper, University of California San Diego (2008).Google Scholar
Fama, E. F., and French, K. R.. “A Five-Factor Asset Pricing Model.” Journal of Financial Economics, 116 (2015), 1–22.Google Scholar
Fink, J. “A Review of the Post-Earnings-Announcement Drift.” Journal of Behavioral and Experimental Finance, 29 (2021), 100446.Google Scholar
Frankel, R.; Jennings, J.; and Lee, J.. “Using Unstructured and Qualitative Disclosures to Explain Accruals.” Journal of Accounting and Economics, 62 (2016), 209–227.Google Scholar
Garcia, D.; Hu, X.; and Rohrer, M.. “The Colour of Finance Words.” Working Paper, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3630898 (2021).Google Scholar
Grennan, J., and Michaely, R.. “Artificial Intelligence and High-Skilled Work: Evidence from Analysts.” Working Paper, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3681574 (2020).Google Scholar
Hou, K.; Mo, H.; Xue, C.; and Zhang, L.. “An Augmented q-Factor Model with Expected Growth.” Review of Finance, 25 (2020), 1–41.Google Scholar
Huang, A.; Wang, H.; and Yang, Y.. “FinBERT – A Deep Learning Approach to Extracting Textual Information.” Working Paper, available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3910214 (2021).Google Scholar
Jegadeesh, N., and Livnat, J.. “Revenue Surprises and Stock Returns.” Journal of Accounting and Economics, 41 (2006), 147–171.Google Scholar
Ke, Z. T.; Kelly, B.; and Xiu, D.. “Predicting Returns with Text Data.” NBER Working Paper No. 26186 (2020).Google Scholar
Kogan, S.; Levin, D.; Routledge, B. R.; Sagi, J. S.; and Smith, N. A.. “Predicting Risk from Financial Reports with Regression.” In Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Boulder, CO: Association for Computational Linguistics (2009), 272–280.Google Scholar
Lee, Y.-J. “The Effect of Quarterly Report Readability on Information Efficiency of Stock Prices.” Contemporary Accounting Research, 29 (2012), 1137–1170.Google Scholar
Li, F. “The Information Content of Forward-Looking Statements in Corporate Filings – A Naïve Bayesian Machine Learning Approach.” Journal of Accounting Research, 48 (2010), 1049–1102.Google Scholar
Livnat, J., and Mendenhall, R. R.. “Comparing the Post-Earnings Announcement Drift for Surprises Calculated from Analyst and Time Series Forecasts.” Journal of Accounting Research, 44 (2006), 177–205.Google Scholar
Loughran, T., and McDonald, B.. “When is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks.” Journal of Finance, 66 (2011), 35–65.Google Scholar
Lundberg, S. M., and Lee, S.-I., “A Unified Approach to Interpreting Model Predictions.” In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Red Hook, NY: Curran Associates Inc. (2017), 4768–4777.Google Scholar
Martineau, C. “Are There (Still) Post-Earnings Announcement Drifts?” SSRN Working Paper (2021).Google Scholar
Mendenhall, R. R. “Arbitrage Risk and Post-Earnings-Announcement Drift.” Journal of Business, 77 (2004), 875–894.Google Scholar
Meursault, V. “The Language of Earnings Announcements.” SSRN Working Paper (2019).Google Scholar
Milian, J. A. “Unsophisticated Arbitrageurs and Market Efficiency: Overreacting to a History of Underreaction?” Journal of Accounting Research, 53 (2015), 175–220.Google Scholar
Muslu, V.; Radhakrishnan, S.; Subramanyam, K. R.; and Lim, D.. “Forward-Looking MD&A Disclosures and the Information Environment.” Management Science, 61 (2015), 931–948.Google Scholar
Narayanamoorthy, G. “Conservatism and Cross-Sectional Variation in the Post-Earnings Announcement Drift.” Journal of Accounting Research, 44 (2006), 763–789.Google Scholar
Ou, J. A., and Penman, S. H.. “Financial Statement Analysis and the Prediction of Stock Returns.” Journal of Accounting and Economics, 11 (1989), 295–329.Google Scholar
Richardson, S.; Tuna, I.; and Wysocki, P.. “Accounting Anomalies and Fundamental Analysis: A Review of Recent Research Advances.” Journal of Accounting and Economics, 50 (2010), 410–454.Google Scholar
Routledge, B. R.; Sacchetto, S.; and Smith, N. A.. “Predicting Merger Targets and Acquirers from Text.” SSRN Working Paper (2013).Google Scholar
Yan, X., and Zheng, L.. “Fundamental Analysis and the Cross-Section of Stock Returns: A Data-Mining Approach.” Review of Financial Studies, 30 (2017), 1382–1423.Google Scholar
Yano, T.; Smith, N. A.; and Wilkerson, J. D., “Textual Predictors of Bill Survival in Congressional Committees.” In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Montréal, QC: Association for Computational Linguistics (2012), 793–802.Google Scholar
Zou, H., and Hastie, T.. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society: Series B, 67 (2005), 301–320.Google Scholar
Supplementary material: PDF

Meursault et al. supplementary material

Meursault et al. supplementary material

Download Meursault et al. supplementary material(PDF)
PDF 338.5 KB