{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:11:23Z","timestamp":1743091883520,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031233869"},{"type":"electronic","value":"9783031233876"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-23387-6_1","type":"book-chapter","created":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T10:32:17Z","timestamp":1674901937000},"page":"3-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Forecasting Stock Excess Returns with SEC 8-K Filings"],"prefix":"10.1007","author":[{"given":"Henry","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Diane","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,29]]},"reference":[{"issue":"4","key":"1_CR1","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1111\/1475-679X.12123","volume":"54","author":"T Loughran","year":"2016","unstructured":"Loughran, T., McDonald, B.: Textual analysis in accounting and finance: a survey. J. Account. Res. 54(4), 1187\u20131230 (2016)","journal-title":"J. Account. Res."},{"key":"1_CR2","unstructured":"Xie, B., et al.: Semantic frames to predict stock price movement. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics,\u00a0pp. 873\u2013883 (2013)"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Ke, Z., Kelly, B., Xiu, D.: Predicting returns with text data\u00a0(No. w26186). National Bureau of Economic Research (2019)","DOI":"10.3386\/w26186"},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1111\/j.1540-6261.2010.01625.x","volume":"66","author":"T Loughran","year":"2011","unstructured":"Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Financ. 66(1), 35\u201365 (2011)","journal-title":"J. Financ."},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Zhai, S., Zhang, Z.: Forecasting firm material events from 8-K reports. In: Proceedings of the Second Workshop on Economics and Natural Language Processing,\u00a0pp. 22\u201330 (2019)","DOI":"10.18653\/v1\/D19-5104"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Kogan, S., et al.: Predicting risk from financial reports with regression. In: Proceedings of human language technologies: the 2009 annual conference of the North American Chapter of the Association for Computational Linguistics,\u00a0pp. 272\u2013280 (2009)","DOI":"10.3115\/1620754.1620794"},{"key":"1_CR7","unstructured":"Lee, H., et al.: On the importance of text analysis for stock price prediction. LREC 2014, 1170\u20131175 (2014)"},{"issue":"5","key":"1_CR8","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.1287\/mnsc.2015.2408","volume":"63","author":"X Zhao","year":"2017","unstructured":"Zhao, X.: Does information intensity matter for stock returns? Evidence from Form 8-K filings. Manage. Sci. 63(5), 1382\u20131404 (2017)","journal-title":"Manage. Sci."},{"key":"1_CR9","doi-asserted-by":"crossref","unstructured":"Engelberg, J.: Costly information processing: evidence from earnings announcements. AFA 2009 San Francisco meetings paper (2008)","DOI":"10.2139\/ssrn.1107998"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Li, F.: The information content of forward\u2010looking statements in corporate filings, a na\u00efve Bayesian machine learning approach.\u00a0J. Account. Res. 48(5), 1049\u20131102 (2010)","DOI":"10.1111\/j.1475-679X.2010.00382.x"},{"key":"1_CR11","doi-asserted-by":"publisher","unstructured":"Aydogdu, M., et al.: Using long short-term memory neural networks to analyze SEC 13D filings: a recipe for human and machine interaction. Intelligent Systems in Accounting, Finance and Management (2020). https:\/\/doi.org\/10.1002\/isaf.1464","DOI":"10.1002\/isaf.1464"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1016\/j.neucom.2022.05.119","volume":"500","author":"H Han","year":"2022","unstructured":"Han, H., et al.: Enhance explainability of manifold learning. Neurocomputing 500, 877\u2013895 (2022)","journal-title":"Neurocomputing"},{"key":"1_CR13","doi-asserted-by":"publisher","unstructured":"Lee, S.: Document vectorization method using network information of words. PLoS ONE 14(7), e0219389 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0219389","DOI":"10.1371\/journal.pone.0219389"},{"key":"1_CR14","unstructured":"Devlin, J., et al.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 (2019)"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Lansing, K., LeRoy, S., Ma, J.: Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency? Federal Reserve Bank of San Francisco Working Paper (2018)","DOI":"10.24148\/wp2018-08"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press (2000)","DOI":"10.1017\/CBO9780511801389"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Han, H.: Hierarchical learning for option implied volatility pricing. In: Hawaii International Conference on System Sciences (2021)","DOI":"10.24251\/HICSS.2021.190"},{"key":"1_CR18","unstructured":"NLP-for-8K-documents. https:\/\/github.com\/hatemr\/NLP-for-8K-documents"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Han H., et al.: Interpretable Machine Learning Assessment (2022). Available at SSRN:\u00a0https:\/\/ssrn.com\/abstract=4146556","DOI":"10.2139\/ssrn.4146556"},{"key":"1_CR20","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE.\u00a0J. Mach. Learn. Res.\u00a09(11) (2008)"},{"key":"1_CR21","unstructured":"McInnes, L., Healy, J., Melville, J.: UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426. (2020)"},{"key":"1_CR22","unstructured":"Vaswani, A., et al.: Attention is All you Need, NIPS (2017)"},{"key":"1_CR23","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. Neural Information Processing Systems, NIPS, pp 2672\u20132680 (2014)"},{"issue":"1","key":"1_CR24","first-page":"145","volume":"13","author":"H Han","year":"2014","unstructured":"Han, H., Jiang, X.: Overcome support vector machine diagnosis overfitting. Cancer Inform. 13(1), 145\u2013158 (2014)","journal-title":"Cancer Inform."},{"key":"1_CR25","doi-asserted-by":"publisher","unstructured":"Gas, R., et al.: Explainable Deep Learning: A Field Guide for the Uninitiated (2021). https:\/\/doi.org\/10.48550\/arXiv.2004.14545","DOI":"10.48550\/arXiv.2004.14545"}],"container-title":["Communications in Computer and Information Science","The Recent Advances in Transdisciplinary Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23387-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T11:33:09Z","timestamp":1674905589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23387-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031233869","9783031233876"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23387-6_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"29 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SDSC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Southwest Data Science Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Waco, TX","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 March 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 March 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sdsc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cs.baylor.edu\/sdsc2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}