Post-Earnings-Announcement Drift Prediction: Leveraging Postevent Investor Responses with Multitask Learning
References
- (1992) Tests of analysts’ overreaction/underreaction to earnings information as an explanation for anomalous stock price behavior. J. Finance 47(3):1181–1207.Crossref, Google Scholar
- (2020) Can mutual funds profit from post earnings announcement drift? The role of competition. J. Banking Finance 114:105774.Crossref, Google Scholar
- (1968) An empirical evaluation of accounting income numbers. J. Accounting Res. 6(2):159–178.Crossref, Google Scholar
- (1989) Post-earnings-announcement drift: Delayed price response or risk premium? J. Accounting Res. 27:1–36.Crossref, Google Scholar
- (2019) Characteristic-based benchmark returns and corporate events. Rev. Financial Stud. 32(1):75–125.Crossref, Google Scholar
- (2017) Multi-task learning of social psychology assessments and nonverbal features for automatic leadership identification. ICMI ‘17 Proc. 19th ACM Internat. Conf. Multimodal Interaction (Association for Computing Machinery, New York), 451–455.Google Scholar
- (2022) MuIT: An end-to-end multitask learning transformer. Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 12031–12041.Google Scholar
- Boehmer E, Jones CM, Zhang X, Zhang X (2021) Tracking retail investor activity. J. Finance 76(5):2249–2305.Google Scholar
- Bonilla EV, Chai K, Williams C (2007) Multi-task Gaussian process prediction. Adv. Neural Inform. Processing Systems, vol. 20 (Curran Associates, Inc., Red Hook, NY).Google Scholar
- (2020) What are you saying? Using topic to detect financial misreporting. J. Accounting Res. 58(1):237–291.Crossref, Google Scholar
- (2021) Exploring relational context for multi-task dense prediction. Proc. 2021 IEEE/CVF Internat. Conf. Comput. Vision (IEEE, Piscataway, NJ), 15869–15878.Google Scholar
- (1997) On persistence in mutual fund performance. J. Finance 52(1):57–82.Crossref, Google Scholar
- (2020) End-to-end object detection with transformers. Vedaldi A, Bischof H, Brox T, Frahm JM, eds. Comput. Vision – ECCV 2020, Lecture Notes in Computer Science, vol. 12346 (Springer, Cham, Switzerland), 213–229.Google Scholar
- (1997) Multitask learning. Machine Learn. 28(1):41–75.Crossref, Google Scholar
- (1998) Multitask learning. Autonomous Agents Multi-Agent Systems 27(1):95–133.Google Scholar
- (1996) Using the future to “sort out” the present: Rankprop and multitask learning for medical risk evaluation. NIPS’95 Proc. 9th Internat. Conf. Neural Inform. Processing Systems (MIT Press, Cambridge, MA), 959–965.Google Scholar
- (1996) Momentum strategies. J. Finance 51(5):1681–1713.Crossref, Google Scholar
- (2018) Text sentiment’s ability to capture information: Evidence from earnings calls. Preprint, submitted November 12, 2013, http://dx.doi.org/10.2139/ssrn.2352524.Google Scholar
- (2018) Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. Dy JG, Krause A, eds. Proc. 35th Internat. Conf. Machine Learn., vol. 80 (PMLR, New York), 794–803.Google Scholar
- (2016) Wide & deep learning for recommender systems. DLRS 2016 Proc. 1st Workshop Deep Learn. Recommender Systems (Association for Computing Machinery, New York), 7–10.Google Scholar
- (2009) Liquidity and the post-earnings-announcement drift. Financial Anal. J. 65(4):18–32.Crossref, Google Scholar
- (2010) Earnings conference call content and stock price: The case of REITs. J. Real Estate Financial Econom. 45(2):402–434.Crossref, Google Scholar
- (2018) Adapting auxiliary losses using gradient similarity. Preprint, submitted December 5, https://arxiv.org/abs/1812.02224.Google Scholar
- (1993) Common risk factors in the returns on stocks and bonds. J. Financial Econom. 33(1):3–56.Crossref, Google Scholar
- (2015) A five-factor asset pricing model. J. Financial Econom. 116(1):1–22.Crossref, Google Scholar
- (2003) Investment Performance Measurement (John Wiley & Sons, Hoboken, NJ).Google Scholar
- (2021) A review of the post-earnings-announcement drift. J. Behav. Experiment. Finance 29:100446.Crossref, Google Scholar
- (2011) Individual investors and volatility. J. Finance 66(4):1369–1406.Crossref, Google Scholar
- (2007) Information uncertainty and post-earnings-announcement-drift. J. Bus. Finance Accounting 34(3–4):403–433.Crossref, Google Scholar
- (1999) An empirical examination of conference calls as a voluntary disclosure medium. J. Accounting Res. 37(1):133–150.Crossref, Google Scholar
- (2013) Empirical mergers and acquisitions research: A review of methods, evidence and managerial implications. Bell AR, Brooks C, Prokopczuk M, eds. Handbook of Research Methods and Applications in Empirical Finance (Edward Elgar Publishing Limited, Northampton, MA), 287–313.Crossref, Google Scholar
- (2013) Positioning and presenting design science research for maximum impact. MIS Quart. 37(2):337–355.Crossref, Google Scholar
- (2015) Information, analysts, and stock return comovement. Rev. Financial Stud. 28(11):3153–3187.Crossref, Google Scholar
- (2021) FinTech as a game changer: Overview of research frontiers. Inform. Systems Res. 32(1):1–17.Link, Google Scholar
- (2017) Intellectual control of complexity in design science research. Editor’s Comments Divers. Des. Sci. Res. (pp. iii–vi), MIS Quart. 41(1):iii–xviii.Google Scholar
- (2010) Does silence speak? An empirical analysis of disclosure choices during conference calls. J. Accounting Res. 48(3):531–563.Crossref, Google Scholar
- (2015) Post-earnings-announcement drift in global markets: Evidence from an information shock. Rev. Financial Stud. 28(4):1242–1283.Crossref, Google Scholar
- (2017) Extractive summarization using multi-task learning with document classification. Palmer M, Hwa R, Riedel S, eds. Proc. 2017 Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 2101–2110.Google Scholar
- (2008) Clustered multi-task learning: A convex formulation. NIPS’08 Proc. 22nd Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Inc., Red Hook, NY), 745–752.Google Scholar
- Jalali A, Sanghavi S, Ruan C, Ravikumar P (2010) A dirty model for multi-task learning. Adv. Neural Inform. Processing Systems, vol. 23 (Curran Associates, Inc., Red Hook, NY).Google Scholar
- (2020) AdaMT-Net: An adaptive weight learning based multi-task learning model for scene understanding. 2020 IEEE/CVF Conf. Comput. Vision Pattern Recognition Workshops (IEEE, Piscataway, NJ), 706–707.Google Scholar
- (2016) Short selling meets hedge fund 13F: An anatomy of informed demand. J. Financial Econom. 122(3):544–567.Crossref, Google Scholar
- (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. 2018 IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 7482–7491.Google Scholar
- (2008) Which money is smart? Mutual fund buys and sells of individual and institutional investors. J. Finance 63(1):85–118.Crossref, Google Scholar
- (2019) Local versus non-local effects of Chinese media and post-earnings announcement drift. J. Banking Finance 106:82–92.Crossref, Google Scholar
- (2019) Predicting taxi demand based on 3D convolutional neural network and multi-task learning. Remote Sensing 11(11):1265.Crossref, Google Scholar
- (1989) Generalization and network design strategies. Pfeifer R, Schreter Z, Fogelman F, Steels L, eds. Connectionism in Perspective, vol. 19 (Elsevier/North-Holland Publishers, Amsterdam), 143–155.Google Scholar
- (2016) Can investors detect managers’ lack of spontaneity? Adherence to predetermined scripts during earnings conference calls. Accounting Rev. 91(1):229–250.Crossref, Google Scholar
- (2022) Active funds and bundled news. Accounting Rev. 97(1):315–339.Crossref, Google Scholar
- (2004) Order imbalances and market efficiency: Evidence from the Taiwan stock exchange. J. Financial Quant. Anal. 39(2):327–341.Crossref, Google Scholar
- (2019) Adaptive auxiliary task weighting for reinforcement learning. Lee DD, Sugiyama M, von Luxburg U, Guyon I, Garnett R, eds. Internat. Conf. Neural Inform. Processing Systems, vol. 32 (Curran Associates, Inc., Red Hook, NY), 4773–4784.Google Scholar
- (2017) Healthcare predictive analytics for risk profiling in chronic care: A Bayesian multitask learning approach. MIS Quart. 41(2):473–495.Crossref, Google Scholar
- (2019) End-to-end multi-task learning with attention. 2019 IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE, Piscataway, NJ), 1871–1880.Google Scholar
- (2006) Comparing the post–earnings announcement drift for surprises calculated from analyst and time series forecasts. J. Accounting Res. 44(1):177–205.Crossref, Google Scholar
- (2019) Decoupled weight decay regularization. 7th Internat. Conf. Learn. Representations (International Society of the Learning Sciences, Ann Arbor, MI).Google Scholar
- (2020) Towards earnings call and stock price movement. Preprint, submitted August 23, https://arxiv.org/abs/2009.01317.Google Scholar
- (1997) Event studies in economics and finance. J. Econom. Literature 35(1):13–39.Google Scholar
- (2021) Rest in peace post-earnings announcement drift. Critical Finance Rev. 11(3–4):613–646.Crossref, Google Scholar
- (2021) PEAD.txt: Post-earnings-announcement drift using text. J. Financial Quant. Anal. 58(6):2299–2326.Crossref, Google Scholar
- (1990) Systems development in information systems research. J. Management Inform. Systems 7(3):89–106.Crossref, Google Scholar
- (2022) Machine learning in information systems research. MIS Quart. 46(1):iii–xix.Crossref, Google Scholar
- (2012) Earnings conference calls and stock returns: The incremental informativeness of textual tone. J. Banking Finance 36(4):992–1011.Crossref, Google Scholar
- (2017) Editor’s comments: Diversity of design science research. MIS Quart. 41(1):iii–xviii.Crossref, Google Scholar
- (2019) Sentence-BERT: Sentence embeddings using Siamese BERT-Networks. Proc. 2019 Conf. Empirical Methods Natural Language Processing 9th Internat. Joint Conf. Natural Language Processing (Association for Computational Linguistics, Kerrville, TX), 3982–3992.Google Scholar
- (1998) International momentum strategies. J. Finance 53(1):267–284.Crossref, Google Scholar
- (2011) Predictive analytics in information systems research. MIS Quart. 35(3):553–572.Crossref, Google Scholar
- (2008) More than words: Quantifying language to measure firms’ fundamentals. J. Finance 63(3):1437–1467.Crossref, Google Scholar
- (1997) Numerical Linear Algebra (Society for Industrial and Applied Mathematics, Philadelphia).Crossref, Google Scholar
- (2020) MTI-Net: Multi-scale task interaction networks for multi-task learning. Vedaldi A, Bischof H, Brox T, Frahm JM, eds. Comput. Vision – ECCV 2020, Lecture Notes in Computer Science, vol. 12349 (Springer, Cham, Switzerland), 527–543.Google Scholar
- (2018) A multi-task learning approach for improving product title compression with user search log data. Proc. AAAI Conf. Artificial Intelligence (Association for the Advancement of Artificial Intelligence, Washington, DC), 451–458.Google Scholar
- (2023a) DeMT: Deformable mixer transformer for multi-task learning of dense prediction. Proc. AAAI Conf. Artificial Intelligence (Association for the Advancement of Artificial Intelligence, Washington, DC), 3072–3080.Google Scholar
- (2023b) Multi-task learning with multi-query transformer for dense prediction. IEEE Trans. Circuits Systems Video Tech. 34(2):1228–1240.Crossref, Google Scholar
- (2022) MTFormer: Multi-task learning via transformer and cross-task reasoning. Avidan S, Brostow G, Cissé M, Farinella GM, Hassner T, eds. Comput. Vision – ECCV 2022, Lecture Notes in Computer Science, vol. 13687 (Springer Nature, Cham, Switzerland), 304–321.Google Scholar
- (2016) Deep multi-task representation learning: A tensor factorisation approach. Internat. Conf. Learn. Representation (ICLR, Appleton, MI).Google Scholar
- (2020) HTML: Hierarchical transformer-based multi-task learning for volatility prediction. WWW’20 Proc. Web Conf. 2020 (Association for Computing Machinery, New York), 441–451.Google Scholar
- (2023) Unlocking the power of voice for financial risk prediction: A theory-driven deep learning design approach. MIS Quart. 47(1):63–96.Crossref, Google Scholar
- (2022) Wearable sensor-based chronic condition severity assessment: An adversarial attention-based deep multisource multitask learning approach. MIS Quart. 46(3):1355–1394.Google Scholar
- (2021) A survey on multi-task learning. IEEE Trans. Knowledge Data Engrg. 34(12):5586–5609.Crossref, Google Scholar

