請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86267完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧信銘 | zh_TW |
| dc.contributor.advisor | Hsin-Min Lu | en |
| dc.contributor.author | 余俊廷 | zh_TW |
| dc.contributor.author | Chun-Ting Yu | en |
| dc.date.accessioned | 2023-03-19T23:45:51Z | - |
| dc.date.available | 2023-12-29 | - |
| dc.date.copyright | 2022-09-02 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | Allee, K. D., and M. D. DeAngelis, The structure of voluntary disclosure narratives: Evidence from tone dispersion, Journal of Accounting Research, 53(2), 241–274, doi:10.2139/ssrn.2375898, 2014. Bommarito, M., D. Katz, and E. M. Detterman, Openedgar: Open source software for sec edgar analysis, SSRN Electronic Journal, doi:10.2139/ssrn.3194754, 2018. Brown, N. C., R. M. Crowley, and W. B. Elliott, What are you saying? using topic to detect financial misreporting, Journal of Accounting Research, 58(1), 237–291, doi:10.1111/1475-679x.12294, 2020. Cannon, J. N., Z. Ling, Q. Wang, and O. V. Watanabe, 10-k disclosure of corporate social responsibility and firms＇ competitive advantages, European Accounting Review, 29(1), 85–113, doi:10.1080/09638180.2019.1670223, 2019. Cohen, L., C. J. Malloy, and Q. Nguyen, Lazy prices, The Journal of Finance, 75(3), 1371–1415, doi:10.2139/ssrn.1658471, 2020. Drake, M. S., D. T. Roulstone, and J. R. Thornock, What investors want: Evidence from investors’ use of the EDGAR database, SSRN Electronic Journal, doi:10.2139/ssrn. 1932315, 2012. Du, C.-H., Y.-S. Chiang, K.-C. Tsai, L.-C. Liu, M.-F. Tsai, and C.-J. Wang, Fridays: A financial risk information detecting and analyzing system, Proceedings of the AAAI Conference on Artificial Intelligence, 33, 9853–9854, doi:10.1609/aaai.v33i01.33019853, 2019. Duarte-Silva, T., H. Fu, C. F. Noe, and K. Ramesh, How do investors interpret announcements of earnings delays?, Journal of Applied Corporate Finance, 25(1), 64–71, doi: 10.1111/j.1745-6622.2013.12007.x, 2013. Efendi, J., J. Park, and L. Smith, Do xbrl filings enhance informational efficiency? early evidence from post-earnings announcement drift, Journal of Business Research, 67(6), 1099–1105, doi:10.1016/J.JBUSRES.2013.05.051, 2014. Ege, M., J. L. Glenn, and J. R. Robinson, Unexpected sec resource constraints and comment letter quality, Contemporary Accounting Research, 37(1), 33–67, doi:10.1111/1911-3846.12505, 2019. Feldman, R., S. Govindaraj, J. Livnat, and B. Segal, Management’s tone change, post earnings announcement drift and accruals, Review of Accounting Studies, 15(4), 915–953, doi:10.1007/s11142-009-9111-x, 2009. Feng, F., C. Luo, X. He, Y. Liu, and T.-S. Chua, Finir 2020: The first workshop on information retrieval in finance, in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, doi:10.1145/3397271.3401462, 2020. Gandhi, P., T. Loughran, and B. McDonald, Using annual report sentiment as a proxy for financial distress in u.s. banks, Journal of Behavioral Finance, 20(4), 424–436, doi:10.1080/15427560.2019.1553176, 2019. Han, M., Y. Liang, Z. Duan, and Y. Wang, Mining public business knowledge: A case study in sec’s edgar, doi:10.1109/BDCloud-SocialCom-SustainCom.2016.65, 2016. Hasan, M., Readability of narrative disclosures in 10-k reports: Does managerial ability matter?, European Accounting Review, 29(1), 147–168, doi:10.1080/09638180.2018.1528169, 2018. Henselmann, K., D. Ditter, and E. Scherr, Irregularities in accounting numbers and earnings management a novel approach based on SEC XBRL filings, SSRN Electronic Journal, doi:10.2139/ssrn.2297355, 2014. Kim, C., K. Wang, and L. Zhang, Readability of 10-k reports and stock price crash risk, Contemporary Accounting Research, 36(2), 1184–1216, doi:10.1111/1911-3846.12452, 2019. Kim, J. W., J.-H. Lim, and W. No, The effect of first wave mandatory xbrl reporting across the financial information environment, Journal of Information Systems, 26(1), 127–153, doi:10.2308/isys-10260, 2012. Lee, C. M., P. Ma, and C. C. Wang, Search-based peer firms: Aggregating investor perceptions through internet co-searches, Journal of Financial Economics, 116(2), 410–431, doi:10.1016/j.jfineco.2015.02.003, 2015. Li, F., Annual report readability, current earnings, and earnings persistence, Journal of Accounting and Economics, 45(2-3), 221–247, doi:10.2139/ssrn.887382, 2008. Liu, Y.-W., L.-C. Liu, C.-J. Wang, and M.-F. Tsai, Fin10k: A web-based information system for financial report analysis and visualization, in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, ACM, doi:10.1145/2983323.2983328, 2016. Liu, Y.-W., L.-C. Liu, C.-J. Wang, and M.-F. Tsai, Riskfinder: A sentence-level risk detector for financial reports, in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, Association for Computational Linguistics, doi:10.18653/v1/n18-5017, 2018. Lopatta, K., M. Gloger, and R. Jaeschke, Can language predict bankruptcy? the explanatory power of tone in 10‐k filings, Accounting Perspectives, 16(4), 315–343, doi:10.1111/1911-3838.12150, 2017. Loughran, T., and B. Mcdonald, When is a liability not a liability? textual analysis, dictionaries, and 10-ks, The Journal of Finance, 66(1), 35–65, doi:10.1111/J.1540-6261.2010.01625.X, 2010. McMullin, J. L., B. Miller, and B. J. Twedt, Increased mandated disclosure frequency and price formation: evidence from the 8-k expansion regulation, Review of Accounting Studies, 24(1), 1–33, doi:10.1007/S11142-018-9462-2, 2018. Mitra, S., T. Al-Hayale, and M. Hossain, Does late 10k filing impact companies＇financial reporting strategy? evidence from discretionary accruals and real transaction management, Journal of Business Finance & Accounting, 46(5-6), 569–607, doi:10.1111/jbfa.12369, 2019. Plachouras, V., C. Smiley, H. Bretz, O. Taylor, J. L. Leidner, D. Song, and F. Schilder, Interacting with financial data using natural language, in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, ACM, doi:10.1145/2911451.2911457, 2016. Ratcliff, J., and D. Metzener, Ratcliff-obershelp pattern recognition, Dictionary of Algorithms and Data Structures, 1998. Ravula, S., Text analysis in financial disclosures, 2021. Yen, J.-C., and T. Wang, The association between xbrl adoption and market reactions to earnings surprises, Journal of Information Systems, 29(3), 51–71, doi:10.2308/isys-51039, 2015. You, H., and X.-J. Zhang, Investor under-reaction to earnings announcement and 10-k report, SSRN Electronic Journal, doi:10.2139/ssrn.1084332, 2007. You, H., and X.-J. Zhang, Limited attention and stock price drift following earnings announcements and 10-k filings, China Finance Review International, 1(4), 358–387, doi:10.2139/ssrn.1475479, 2011. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86267 | - |
| dc.description.abstract | 在經歷 1930 年代經濟大蕭條後，美國國會為了確保市場的公平、透明及穩定性而制定與通過了 1934 年的證券交易法，該法案的出現賦予了美國證券交易委員會（SEC）管理監督與標準制定的權限。依據法條中的申報規定，資產在 1000 萬美元以上且股東人數超過 500 人的公司 需提供年度報告及其他定期財務報告（主要為 Form 10-K、10-Q 及 8-K）給 SEC，而這些報告皆可於 SEC 提供的資料庫 EDGAR 中查詢。然而，受限於 SEC EDGAR 本身提供的搜尋功能只有簡單的財報搜尋，對於想要取得更深入資訊的使用者來說沒辦法輕易的達成，過去也有研究提到許多重要的資訊往往是分散在多個財報之中，例如說是希望了解一份財務報告相較去年時期的改變。在這個研究之中，我們首先基於 SEC EDGAR 的系統重新設計與實作了一套新的資訊系統 MAD EDGAR 輔助使用者閱讀 10-K 年報，我們的系統將年報中兩年間的變化以紅色及綠色的醒目提示呈現在報告之中以 輔助使用者閱讀。其次，為了系統成效的評估我們也設計了兩套實驗與任務並招募了 20 名受測者實際操作系統。我們的研究結果顯示，當任務涉及到跨年報的資訊時，MAD EDGAR 在回答問題的效率上確實表現的比 SEC EDGAR 更加良好。 | zh_TW |
| dc.description.abstract | This paper addresses the problem of SEC EDGAR's limitations in identifying year-over-year differences among financial reports. We presented Making A Difference for SEC EDGAR (MAD EDGAR), a new web-based information system that facilitates the analysis of year-over-year modifications in 10-K reports. MAD EDGAR highlights the differences between 10-K reports to help investors efficiently comprehend the modifications in documents. The year-over-year differences in a 10-K report are presented in a colorized format, where the color green stands for new statements added in the current year of the report, while the color red stands for deletion from the previous year on the contrary. Twenty graduate students from four universities were recruited and observed while completing two types of tasks specifically designed to evaluate MAD EDGAR's usefulness during the experiment. The results indicated that our system out-performed SEC EDGAR in terms of identifying year-over-year changes. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T23:45:51Z (GMT). No. of bitstreams: 1 U0001-0208202220343200.pdf: 44776263 bytes, checksum: 57bc296b2dac088257de71b0c3faf865 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | Acknowledgements i 摘要 iii Abstract iv Contents v List of Figures viii List of Tables x Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Literature Review 8 SEC EDGAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.1 XBRL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.2 EDGAR System Usage Data . . . . . . . . . . . . . . . . . . . . . 11 Chapter 1 Chapter 2 2.1 2.2 Text Analysis in Financial Reports . . . . . . . . . . . . . . . . . . . v 12 2.2.1 Tone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.2 Readability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.3 MD&A Modifications . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.3 Information System for Financial Information . . . . . . . . . . . . . 15 Research Gaps and Research Questions 17 3.1 Research Gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 System Design and Implementation 18 4.1 System Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2 User Interface Design of Functionality . . . . . . . . . . . . . . . . . 21 4.3 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.3.1 Data Tier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 3 Chapter 4 4.3.1.1 Master Server . . . . . . . . . . . . . . . . . . . . . . 28 4.3.1.2 Task Queue Server . . . . . . . . . . . . . . . . . . . . 28 4.3.1.3 Worker Servers . . . . . . . . . . . . . . . . . . . . . 29 4.3.1.4 The Differencing Algorithm . . . . . . . . . . . . . . . 29 4.3.2 Application Tier . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.3.3 Presentation Tier . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 Chapter 5 Experimental Design 35 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.1.2 Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.1.3 Technical Environment . . . . . . . . . . . . . . . . . . . . . . . . 36 5.1.4 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.1.5 Hypothesis Settings and Expected Experimental Results . . . . . . . 39 5.2 Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . . . 40 5.2.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.2.2 Coding and Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.2.3 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Chapter 6 Result and Discussion 43 6.1 Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.2 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 6.3 Analysis of Variance (ANOVA) . . . . . . . . . . . . . . . . . . . . 48 6.3.1 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.3.1.1 First Task . . . . . . . . . . . . . . . . . . . . . . . . 48 6.3.1.2 Second Task . . . . . . . . . . . . . . . . . . . . . . . 49 6.3.2 Tests of Normality . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.3.3 Homogeneity of Variances . . . . . . . . . . . . . . . . . . . . . . 52 6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 References 55 Appendix A — Data Collection Form 61 | - |
| dc.language.iso | en | - |
| dc.subject | 10-K 年度報告 | zh_TW |
| dc.subject | 美國證券交易委員會 | zh_TW |
| dc.subject | EDGAR | zh_TW |
| dc.subject | 財務報表 | zh_TW |
| dc.subject | 變化偵測 | zh_TW |
| dc.subject | Financial Report | en |
| dc.subject | Change Detection | en |
| dc.subject | 10-K Report | en |
| dc.subject | EDGAR | en |
| dc.subject | SEC | en |
| dc.title | MAD EDGAR: 10-K 報表變動比對與檢視系統 | zh_TW |
| dc.title | Making A Difference for SEC EDGAR (MAD EDGAR): Highlighting 10-K Year-over-Year Differences for Better Awareness | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 110-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 洪茂蔚;畢南怡 | zh_TW |
| dc.contributor.oralexamcommittee | Mao-Wei Hung;Nan-Yi Bi | en |
| dc.subject.keyword | 美國證券交易委員會,EDGAR,財務報表,10-K 年度報告,變化偵測, | zh_TW |
| dc.subject.keyword | SEC,EDGAR,Financial Report,10-K Report,Change Detection, | en |
| dc.relation.page | 75 | - |
| dc.identifier.doi | 10.6342/NTU202201989 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2022-08-30 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 資訊管理學系 | - |
| dc.date.embargo-lift | 2023-08-29 | - |
| 顯示於系所單位： | 資訊管理學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-110-2.pdf | 43.73 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。