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Double Machine Learning: Explaining the Post-Earnings Announcement Drift

Published online by Cambridge University Press:  20 February 2023

Jacob H. Hansen Open the ORCID record for Jacob H. Hansen [Opens in a new window]  and
Mathias V. Siggaard
Jacob H. Hansen*
Affiliation:
Aarhus University Department of Economics and Business Economics, CREATES
Mathias V. Siggaard
Affiliation:
Aarhus University Department of Economics and Business Economics, CREATES siggaard@econ.au.dk
*
jacob.hald.hansen@gmail.com (corresponding author)
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Abstract

We demonstrate the benefits of merging traditional hypothesis-driven research with new methods from machine learning that enable high-dimensional inference. Because the literature on post-earnings announcement drift (PEAD) is characterized by a “zoo” of explanations, limited academic consensus on model design, and reliance on massive data, it will serve as a leading example to demonstrate the challenges of high-dimensional analysis. We identify a small set of variables associated with momentum, liquidity, and limited arbitrage that explain PEAD directly and consistently, and the framework can be applied broadly in finance.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 59 , Issue 3 , May 2024 , pp. 1003 - 1030
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

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Footnotes

We thank participants of the 2022 Duke Financial Econometrics Seminar, 2022 Aarhus University Econometrics-Finance Seminar Series, and Nordic Finance Network Workshop for their many useful comments and questions. We also thank Tim Bollerslev, Kim Christensen, Tom Engsted, Jonas Nygaard Eriksen, Campbell R. Harvey, Jae Hoon Kim, Stig Vinther Møller, Nicolaj Søndergaard Mühlbach, Thomas Quistgaard Pedersen, Jylhä Petri, and Allan Timmermann for their valuable comments and insightful suggestions. Special thanks are due to Jennifer Conrad (the editor) and Vitaly Meursault (the referee) for detailed comments and helpful suggestions, which helped improve many aspects of this article.

References

Amihud, Y., and Mendelson, H.. “The Effects of Beta, Bid–Ask Spread, Residual Risk, and Size on Stock Returns.” Journal of Finance, 44 (1989), 479–486.Google Scholar
Athey, S.; Imbens, G. W.; and Wager, S.. “Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions.” Journal of the Royal Statistical Society. Series B (Statistical Methodology), 80 (2018), 597–623.CrossRefGoogle Scholar
Ball, R., and Brown, P.. “An Empirical Evaluation of Accounting Income Numbers.” Journal of Accounting Research, 6 (1968), 159–178.CrossRefGoogle Scholar
Bartov, E.; Radhakrishnan, S.; and Krinsky, I.. “Investor Sophistication and Patterns in Stock Returns After Earnings Announcements.” Accounting Review, 75 (2000), 43–63.CrossRefGoogle Scholar
Belloni, A.; Chen, D.; Chernozhukov, V.; and Hansen, C.. “Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain.” Econometrica, 80 (2012), 2369–2429.Google Scholar
Belloni, A., and Chernozhukov, V.. “Least Squares After Model Selection in High-Dimensional Sparse Models.” Bernoulli, 19 (2013), 521–547.CrossRefGoogle Scholar
Belloni, A.; Chernozhukov, V.; Fernández-Val, I.; and Hansen, C.. “Program Evaluation and Causal Inference with High-Dimensional Data.” Econometrica, 85 (2017), 233–298.CrossRefGoogle Scholar
Belloni, A.; Chernozhukov, V.; and Hansen, C.. “High-Dimensional Methods and Inference on Structural and Treatment Effects.” Journal of Economic Perspectives, 28 (2014a), 29–50.CrossRefGoogle Scholar
Belloni, A.; Chernozhukov, V.; and Hansen, C.. “Inference on Treatment Effects After Selection Among High-Dimensional Controls.” Review of Economic Studies, 81 (2014b), 608–650.CrossRefGoogle Scholar
Benjamini, Y., and Hochberg, Y.. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society. Series B (Methodological), 57 (1995), 289–300.CrossRefGoogle Scholar
Bernard, V. L., and Thomas, J. K.. “Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium?” Journal of Accounting Research, 27 (1989), 1–36.CrossRefGoogle Scholar
Bernard, V. L., and Thomas, J. K.. “Evidence that Stock Prices Do Not Fully Reflect the Implications of Current Earnings for Future Earnings.” Journal of Accounting and Economics, 13 (1990), 305–340.CrossRefGoogle Scholar
Bhushan, R. “An Informational Efficiency Perspective on the Post-Earnings Announcement Drift.” Journal of Accounting and Economics, 18 (1994), 45–65.CrossRefGoogle Scholar
Breiman, L. “Random Forests.” Machine Learning, 45 (2001), 5–32.CrossRefGoogle Scholar
Carhart, M. M. “On Persistence in Mutual Fund Performance.” Journal of Finance, 52 (1997), 57–82.CrossRefGoogle Scholar
Chernozhukov, V.; Chetverikov, D.; Demirer, M.; Duflo, E.; Hansen, C.; Newey, W.; and Robins, J.. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” Econometrics Journal, 21 (2018), C1–C68.CrossRefGoogle Scholar
Chordia, T.; Goyal, A.; Sadka, G.; Sadka, R.; and Shivakumar, L.. “Liquidity and the Post-Earnings-Announcement Drift.” Financial Analysts Journal, 65 (2009), 18–32.CrossRefGoogle Scholar
Chordia, T.; Subrahmanyam, A.; and Anshuman, V. R.. “Trading Activity and Expected Stock Returns.” Journal of Financial Economics, 59 (2001), 3–32.CrossRefGoogle Scholar
Christensen, K.; Siggaard, M.; and Veliyev, B.. “A Machine Learning Approach to Volatility Forecasting.” Journal of Financial Econometrics, forthcoming (2023).CrossRefGoogle Scholar
Cochrane, J. H. “Presidential Address: Discount Rates.” Journal of Finance, 66 (2011), 1047–1108.CrossRefGoogle Scholar
DellaVigna, S., and Pollet, J. M.. “Investor Inattention and Friday Earnings Announcements.” Journal of Finance, 64 (2009), 709–749.CrossRefGoogle Scholar
Fama, E. F. “Market Efficiency, Long-Rerm Returns, and Behavioral Finance.” Journal of Financial Economics, 49 (1998), 283–306.CrossRefGoogle Scholar
Feng, G.; Giglio, S.; and Xiu, D.. “Taming the Factor Zoo: A Test of New Factors.” Journal of Finance, 75 (2020), 1327–1370.CrossRefGoogle Scholar
Fink, J. “A Review of the Post-Earnings-Announcement Drift.” Journal of Behavioral and Experimental Finance, 29 (2020), 100446.CrossRefGoogle Scholar
Foster, G.; Olsen, C.; and Shevlin, T.. “Earnings Releases, Anomalies, and the Behavior of Security Returns.” Accounting Review, 59 (1984), 574–603.Google Scholar
Francis, J.; Lafond, R.; Olsson, P.; and Schipper, K.. “Information Uncertainty and Post-Earnings Announcement Drift.” Journal of Business Finance & Accounting, 34 (2007), 403–433.CrossRefGoogle Scholar
Freyberger, J.; Neuhierl, A.; and Weber, M.. “Dissecting Characteristics Nonparametrically.” Review of Economic Studies, 33 (2020), 2326–2377.Google Scholar
Garfinkel, J. A., and Sokobin, J.. “Volume, Opinion Divergence, and Returns: A Study of Post-Earnings Announcement Drift.” Journal of Accounting Research, 44 (2006), 85–112.CrossRefGoogle Scholar
Giglio, S., and Xiu, D.. “Asset Pricing with Omitted Factors.” Journal of Political Economy, 129 (2021), 1947–1990.CrossRefGoogle Scholar
Gow, I. D.; Larcker, D. F.; and Reiss, P. C.. “Causal Inference in Accounting Research.” Journal of Accounting Research, 54 (2016), 477–523.CrossRefGoogle Scholar
Green, J.; Hand, J. R.; and Zhang, X. F.. “The Characteristics that Provide Independent Information About Average U.S. Monthly Stock Returns.” Review of Economic Studies, 30 (2017), 4389–4436.Google Scholar
Gu, S.; Kelly, B.; and Xiu, D.. “Empirical Asset Pricing via Machine Learning.” Review of Economic Studies, 33 (2020), 2223–2273.Google Scholar
Harvey, C. R.; Liu, Y.; and Zhu, H.. “… and the Cross-Section of Expected Returns.” Review of Economic Studies, 29 (2016), 5–68.Google Scholar
Hirshleifer, D.; Lim, S. S.; and Teoh, S. H.. “Driven to Distraction: Extraneous Events and Underreaction to Earnings News.” Journal of Finance, 64 (2009), 2289–2325.CrossRefGoogle Scholar
Hoerl, A. E., and Kennard, R. W.. “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” Technometrics, 12 (1970), 55–67.CrossRefGoogle Scholar
Javanmard, A., and Montanari, A.. “Confidence Intervals and Hypothesis Testing for High-Dimensional Regression.” Journal of Machine Learning Research, 15 (2014), 2869–2909.Google Scholar
Jegadeesh, N., and Livnat, J.. “Post-Earnings-Announcement Drift: The Role of Revenue Surprises.” Financial Analysts Journal, 62 (2006), 22–34.CrossRefGoogle Scholar
Jiang, G.; Lee, C. M.; and Zhang, Y.. “Information Uncertainty and Expected Returns.” Review of Accounting Studies, 10 (2005), 185–221.CrossRefGoogle Scholar
Keuzenkamp, H. A., and Magnus, J. R.. “On Tests and Significance in Econometrics.” Journal of Econometrics, 67 (1995), 5–24.CrossRefGoogle Scholar
Kim, J. H. “Stock Returns and Investors’ Mood: Good Day Sunshine or Spurious Correlation?” International Review of Financial Analysis, 52 (2017), 94–103.CrossRefGoogle Scholar
Kim, J. H., and Ji, P. I.. “Significance Testing in Empirical Finance: A Critical Review and Assessment.” Journal of Empirical Finance, 34 (2015), 1–14.CrossRefGoogle Scholar
Kormendi, R., and Lipe, R.. “Earnings Innovations, Earnings Persistence, and Stock Returns.” Journal of Business, 60 (1987), 323–345.CrossRefGoogle Scholar
Lee, J. D.; Sun, D. L.; Sun, Y.; and Taylor, J. E.. “Exact Post-Selection Inference, with Application to the Lasso.” Annals of Statistics, 44 (2016), 907–927.CrossRefGoogle Scholar
Lei, J.; G’Sell, M.; Rinaldo, A.; Tibshirani, R. J.; and Wasserman, L.. “Distribution-Free Predictive Inference for Regression.” Journal of the American Statistical Association, 113 (2018), 1094–1111.CrossRefGoogle Scholar
Lin, M.; Lucas, H. C. Jr.; and Shmueli, G.. “Research Commentary – Too Big to Fail: Large Samples and the p-Value Problem.” Information Systems Research, 24 (2013), 906–917.CrossRefGoogle 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.CrossRefGoogle Scholar
MacKinlay, A. C. “Event Studies in Economics and Finance.” Journal of Economic Literature, 35 (1997), 13–39.Google Scholar
McCloskey, D. N., and Ziliak, S. T.. “The Standard Error of Regressions.” Journal of Economic Literature, 34 (1996), 97–114.Google Scholar
Mendenhall, R. R. “Evidence on the Possible Underweighting of Earnings-Related Information.” Journal of Accounting Research, 29 (1991), 170–179.CrossRefGoogle Scholar
Mendenhall, R. R. “Arbitrage Risk and Post-Earnings-Announcement Drift.” Journal of Business, 77 (2004), 875–894.CrossRefGoogle Scholar
Narayanamoorthy, G. “Conservatism and Cross-Sectional Variation in the Post-Earnings Announcement Drift.” Journal of Accounting Research, 44 (2006), 763–789.CrossRefGoogle Scholar
Nickl, R., and Van De Geer, S.. “Confidence Sets in Sparse Regression.” Annals of Statistics, 41 (2013), 2852–2876.CrossRefGoogle Scholar
Petersen, M. A. “Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches.” Review of Economic Studies, 22 (2009), 435–480.Google Scholar
Roberts, M. R., and Whited, T. M., “Endogeneity in Empirical Corporate Finance.” In Constantinides, George M., and Harris, Milton, eds. Handbook of the Economics of Finance, Vol. 2. Amsterdam: Elsevier (2013), 493–572.CrossRefGoogle Scholar
Shivakumar, L. “Accruals, Cash Flows and the Post-Earnings-Announcement Drift.” Journal of Business Finance & Accounting, 33 (2006), 1–25.CrossRefGoogle Scholar
Van de Geer, S.; Bühlmann, P.; Ritov, Y.; and Dezeure, R.. “On Asymptotically Optimal Confidence Regions and Tests for High-Dimensional Models.” Annals of Statistics, 42 (2014), 1166–1202.CrossRefGoogle Scholar
Whited, R. L.; Swanquist, Q. T.; Shipman, J. E.; and Moon, J. R.. “Out of Control: The (over) Use of Controls in Accounting Research.” Accounting Review, 97 (2022), 395–413.CrossRefGoogle Scholar
Yang, J.; Chuang, H.; and Kuan, C.. “Double Machine Learning with Gradient Boosting and its Application to the Big N Audit Quality Effect.” Journal of Econometrics, 216 (2020), 268–283.CrossRefGoogle Scholar
Zhang, C., and Zhang, S. S.. “Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models.” Journal of the Royal Statistical Society. Series B (Statistical Methodology), 76 (2014), 217–242.CrossRefGoogle Scholar
Zou, H. “The Adaptive Lasso and its Oracle Properties.” Journal of the American Statistical Association, 101 (2006), 1418–1429.CrossRefGoogle Scholar
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