政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/59248

政大典藏 > College of Commerce > Department of Accounting > Theses > Item 140.119/59248

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/59248

Title:	運用XBRL通用連結與文字探勘技術於財報資訊與附註資訊之整合 Integration of Financial Statement Information and Footnote Information Using Generic Link and Text-mining Technology
Authors:	王譯萱
Contributors:	周濟群王譯萱
Keywords:	通用連結文字探勘財報資訊附註資訊文字性資訊 TFIDF generic link text-mining financial statement information footnote information textual information TFIDF
Date:	2012
Issue Date:	2013-09-02 15:27:57 (UTC+8)
Abstract:	藉由對財務和附註的文字性資訊進行分析，以協助使用者有效率地分類閱讀這些非結構化文字資訊。本研究針對2012年的美國上市半導體公司隨機抽樣40份，使用文字探勘中TFIDF技術分析附註文字性資訊，並結合財務資訊中的五大財務面向，利用SAX Parser將財務資訊和附註文字性資訊取出，實際在eclipse平台中以JAVA開發實作。希望能協助投資人有效降低投資風險，創造更大的利潤。本研究附註文字性資訊被分類的準確度可以達86%，並可有效地使用通用連結建立財務資訊以及附註文字性資訊之關聯性。投資人便可透過想了解的財務數字中，迅速找到相關的附註文字性資訊，藉以減少投資的風險，做出更即時且更正確的決策。 Analyzing financial information and textual information in footnote is able to help users categorize unstructured text information so that make it easy to read. This research randomly samples 40 companies from U.S. listed semiconductor firms in 2012. We use TFIDF to analyze textual information in footnote and combine it with 5 dimensions from financial information, using SAX Praser to abstract financial information and textual information in footnote. Finally apply it by JAVA under the eclispse platform in practical. Via this application, we try helping investors reduce the investing risks, and make more profits. According to this research, the accuracy of correct categorization of textual information in footnote reaches 86%, and generic link is able to effectively establish the relationship between financial information and textual information in footnote. Thus, investors can guickly find out related textual information in footnote from financial subjects they try to understand, so that investors can lower their risks and make faster and correcter decisions.
Reference:	吳娟娟，2002，財務績效指標之重要性分析研究－以台灣化學工業上市公司為例，中原大學會計學系碩士論文。呂國征，2007，應用財務比率分析海運業經營績效之研究，銘傳大學國際企業學系碩士在職專班碩士論文。李昇暾，2011，決策支援與企業智慧系統第9版，華泰文化，頁273-275。周淑芬，2009，應用財務比率評估鋼鐵業之獲利能力，國立高雄第一科技大學之碩士論文。周濟群，2009，利用XBRL技術設計可剖析的開放式企業報告架構，東吳會計學報，第1卷第2期（5月）：1-35。周濟群，2012，投資人應如何運用XBRL資訊服務，會計研究月刊，第323期（10月）：100-111。周濟群，2012，投資人應如何運用XBRL資訊服務，會計研究月刊，第319期（6月）：86-96。胡柏存，2010，公司財務危機預警模型之研究-以美國商業銀行為例，元智大學資訊管理學系碩士論文。馬聖珉，2006，一個監督式學習與非監督式學習技術應用於多國語言文件探勘之比較研究，國立高雄應用科技大學電機工程系碩士班，頁8。楊曜瑋，2007，利用文字探勘技術擷取出蛋白質間交互作用反應，國立成功大學資訊管理研究所碩士論文。趙勃軒，2002，以財務比率分析區別台灣地區上市電子公司經營績效，大同大學事業經營研究所在職專班碩士論文。劉仁嘉，2009，應用於文件分類的字建構是模糊特徵擷取法，國立中山大學電機工程學系，頁6。謝百成，2008，我國上市公司電子股新產業分類財務比率之調查，國立中正大學會計與資訊科技研究所碩士論文。謝松文，2009，以文字探勘技術萃取財務報告書之附註說明，國立中正大學會計與資訊科技研究所碩士論文。譚大純、謝德鑫，2006。知識管理。台北市：滄海書局。饒挺彰，2010，99年版《台灣地區主要行業財務比率刊冊》介紹，台北市：金融聯合徵信中心徵信部。龔碧雲，2007，運用財務比率評估企業經營績效-以台灣造紙產業為例，開南大學企業管理研究所碩士論文。 Alles, M., R. Debreceny and M. Piechocki. 2012. The evolution and future of XBRL research. Journal of International Journal of Accounting Information Systems 13(2)：83-90 Back, B., J. Toivonen, H. Vanharanta, A. Visa. 2001. Comparing numerical data and text information from annual reports using self-organizing maps. Accounting Information Systems：249-269. Chou, C. C. 2012. The Integration of Financial and Non-Financial Information in Financial Reports. AAA 2012 annual conference. Washington, D.C.. Christopher D. Manning, and Hinrich Schütze. 1999. Foundations of statistical natural language processing. Massachusetts, M.A.:MIT Press Clarke, G., and L.W. Murray. 2000. Investor relations: Perspections of the annual statement. Corporate Communications 5(3):144-151 Eccles, R., S. Mavrinac. 1995. Improving the corporate disclosure process. Sloan Management Review 36 (4): 11-25. Frakes W. B. and Ricardo B.Y. 1992. Information Retrieval: Data Structures and Algorithms. New Jersey, NJ: Prentice-Hall. Garshol, L. M. 2002. What are topic maps? Retrieved on Dec. 31, 2012 from http://www.xml.com/pub/a/2002/09/11/topicmaps.html. Hodge, F., J. Kennedy, and L. Maines. 2004. Does information-search technology improve the transparency of financial reporting? The Accounting Review 79(3): 687-703. Kloptchenko, A., B. Back, H. Vanharanta, T. Eklund, J. Karlsson, and A. Visa. 2002. Combining data and text mining techniques for analyzing financial report. Eighth Americas Conference on Information Systems 2002. Texas, T.X. : 20-28. Logan, D., and R. Mogull. 2003. Sarbanes-Oxley: The role of technology. Gartner Research. Penler, P., and M. Schnitzer. 2002. Web-enabled business reporting for the banking industry. White Paper, Ernst & Young LLP and Morgan Stanley Ernst & Young LLP and Morgan Stanley. Salton, G. and C. Buckley. 1988. Term-weighting approaches in automatic text retrieval. Information Processing & Management 24(5): 513–523. Salton, G. and M. J. McGill. 1983. Introduction modern Information retrival. New York: McGill. Spark, J. K. 1972. A statistical interpretation of term specificity and its application in retrival. Journal of Documentation 28(January):11-21. Tuominen, P. 1997. Investor relations: A Nordic school approach. Corporate Communications: An International Journal 2: 46-55. Useem, M., CorporateLeadershiep in Globalizing Equity Market. Academy Management Executive 12(4)：pp.43-59, 1998. Visa, A., J. Toivonen, P. Ruokonen, H. Vanharanta, and B. Back. 2000. Knowledge discovery from text documents based on paragraph maps. Proceedings of the 33rd Hawaii International Conference on System Sciences. Hawaii, H. I..
Description:	碩士國立政治大學會計研究所 100353017 101
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0100353017
Data Type:	thesis
Appears in Collections:	[Department of Accounting] Theses

Files in This Item:

There are no files associated with this item.

社群 sharing

Loading...