Abstract
This paper presents a novel linguistic information extraction approach exploiting analysts’ stock ratings for statistical decision making. Over a period of one year, we gathered German stock analyst reports in order to determine market trends. Our goal is to provide business statistics over time to illustrate market trends for a user-selected company. We therefore recognize named entities within the very short stock analyst reports such as organization names (e.g. BASF, BMW, Ericsson), analyst houses (e.g. Gartner, Citigroup, Goldman Sachs), ratings (e.g. buy, sell, hold, underperform, recommended list) and price estimations by using lexicalized finite-state graphs, so-called local grammars. Then, company names and their acronyms respectively have to be cross-checked against data the analysts provide. Finally, all extracted values are compared and presented into charts with different views depending on the evaluation criteria (e.g. by time line). Thanks to this approach it will be easier and even more comfortable in the future to pay attention to analysts’ buy/sell signals without reading all their reports.
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Barber, B.M., Lehaby, R., McNichols, M., Trueman, B.: Buys, holds, and sells: The distribution of investment banks’ stock ratings and the implications for the profitability of analysts’ recommendations. Journal of Accounting and Economics 41, 87–117 (2006)
Lin, L., Liotta, A., Hippisley, A.: A method for automating the extraction of specialized information from the web. CIS 1, 489–494 (2005)
Gross, M.: The Construction of Local Grammars. In: Roche, E., Schabés, Y. (eds.) Finite-State Language Processing. Language, Speech, and Communication, pp. 329–354. MIT Press, Cambridge (1997)
Surdeanu, M., Harabagiu, S., Williams, J., Aarseth, P.: Using Predicate-Argument Structures for Informaiton Extraction. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 8–15 (2003)
Baumgartner, R., Frölich, O., Gottlob, G., Harz, P.: Web Data Extraction for Business Intelligence: the Lixto Approach. In: Proc. of BTW 2005 (2005)
Maynard, D., Saggion, H., Yankova, M., Bontcheva, K., Peters, W.: Natural Language Technology for Information Integration in Business Intelligence. In: 10th International Conference on Business Information Systems, Poland (2007)
Saggion, H., Funk, A., Maynard, D., Bontcheva, K.: Ontology-Based Information Extraction for Business Intelligence. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 843–856. Springer, Heidelberg (2007)
Paradis, F., Nie, J.Y., Tajarobi, A.: Discovery of Business Opportunities on the Internet with Informaiton Extraction. In: IJCAI 2005, Edinburgh, pp. 47–54 (2005)
Silva, J., Kozareva, Z., Noncheva, V., Lopes, G.: Extracting Named Entities. A Statistical Approach. In: TALN 2004, Fez, Marroco, ATALA, pp. 347–351 (2004)
Downey, D., Broadhead, M., Etzioni, O.: Locating Complex Named Entities in web Text. In: Proc. of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007), Hyderabad, India (2007)
McDonald, D.: Internal and external evidence in the identification and semantic categorization of proper names. In: Boguraev, B., Pustejovsky, J. (eds.) Corpus Processing for Lexical Acquisition, pp. 21–39. MIT Press, Cambridge (1996)
Mikheev, A., Moens, M., Grover, C.: Named entity recognition without gazetteers. In: Proceedings of the Ninth Conference of the European Chapter of the Association for Computational Linguistics, pp. 1–8 (1999)
Etzioni, O., Cafarella, M., Downey, D., Popescu, A.M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Unsupervised Named-Entity Extraction from the web: An Experimental Study. Artificial Intelligence 165(1), 134–191 (2005)
Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open Information Extraction from the Web. In: Proceedings of IJCAI (2007)
Giuliano, C., Lavelli, A., Romano, L.: Exploiting shallow linguistic information for relation extraction from biomedical literature. In: Proc. EACL 2006 (2006)
Bsiri, S., Geierhos, M., Ringlstetter, C.: Structuring Job Search via Local Grammars. Advances in Natural Language Processing and Applications. Research in Computing Science (RCS) 33, 201–212 (2008)
Geierhos, M., Blanc, O.: BiographIE – Biographical Information Extraction from Business News. In: De Gioia, M. (ed.) Actes du 27e Colloque international sur le lexique et la grammaire, L’Aquila, September 10-13 (2008); Seconde partie. Lingue d’Europa e del Mediterraneo: Grammatica comparata. Aracne, Rome, Italy (2010)
Traboulsi, H.N.: Named Entity Recognition: A Local Grammar-based Approach. PhD thesis, University of Surrey (2006)
Woods, W.A.: Transition network grammars for natural language analysis. Commun. ACM 13(10), 591–606 (1970)
Paumier, S.: Unitex User Manual 2.1. (2010), http://igm.univ-mlv.fr/~unitex/UnitexManual2.1.pdf
Chi, C.H., Ding, C.: Word Segmentation and Recognition for Web Document Framework. In: Proceedings of Conference on Information and Knowledge Management, CIKM (1999)
Lee, Y.S.: Website-Klassifikation und Informationsextraktion aus Informationsseiten einer Firmenwebsite. PhD thesis, University of Munich, Germany (2008)
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Lee, Y.S., Geierhos, M. (2011). Buy, Sell, or Hold? Information Extraction from Stock Analyst Reports. In: Beigl, M., Christiansen, H., Roth-Berghofer, T.R., Kofod-Petersen, A., Coventry, K.R., Schmidtke, H.R. (eds) Modeling and Using Context. CONTEXT 2011. Lecture Notes in Computer Science(), vol 6967. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24279-3_19
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DOI: https://doi.org/10.1007/978-3-642-24279-3_19
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