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  1. NTU Theses and Dissertations Repository
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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72948
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor王泰昌(Tay-chang Wang)
dc.contributor.authorYu-Chen Yangen
dc.contributor.author楊于禛zh_TW
dc.date.accessioned2021-06-17T07:11:23Z-
dc.date.available2019-07-24
dc.date.copyright2019-07-24
dc.date.issued2019
dc.date.submitted2019-07-18
dc.identifier.citation財團法人中華民國會計研究發展基金會臺灣財務報導準則委員會,2015,國際財務報導準則第9號「金融工具」( IFRS 9),財團法人中華民國會計研究發展基金會。
財團法人中華民國會計研究發展基金會臺灣財務報導準則委員會,2013,國際會計準則第39號「金融工具:認列與衡量」(IAS 39),財團法人中華民國會計研究發展基金會。
財團法人中華民國會計研究發展基金會,2008,放款及應收款暨其他金融商品會計處理問答集,中華民國銀行公會委託執行,頁63至69。
鄭惠如,2010,34號公報第三次修訂條文對銀行業之影響介紹,貨幣觀測與信用評等,第八十六期,頁47至53。
Cantrell, B. W., J. M. McInnis, and C. G. Yust. 2014. Predicting Credit Losses: Loan Fair Values versus Historical Costs. The Accounting Review 89 (1): 147-176.
Chen, P. F. and L. Daley. 1996. Regulatory Capital, Tax, and Earnings Management Effects on Loan Loss Accruals in the Canadian Banking Industry. Contemporary Accounting Research 13 (1): 91-128.
Eng, L. L. and S. Nabar. 2007. Loan Loss Provisions by Banks in Hong Kong, Malaysia and Singapore. Journal of International Financial Management & Accounting 18 (1): 18-38.
Harris, T. S., U. Khan, and D. Nissim. 2018. The Expected Rate of Credit Losses on Banks' Loan Portfolios. The Accounting Review 93 (5): 245-271.
International Accounting Standards Board (IASB). 2014. International Financial Reporting Standard 9 Financial Instruments, IFRS Foundation.
Kanagaretnam, K., G. J. Lobo, and D. H. Yang. 2010. Joint Tests of Signaling and Income Smoothing through Bank Loan Loss Provisions. Contemporary Accounting Research 21 (4): 843-884.
Lee, M. J., I. T. Hwang, and S. M. Kang. 2018. The Effect of Forward‐looking Criteria and IFRS on the Informativeness of Banks’ Loan Loss Allowances: Evidence from Korea. Australian Accounting Review forthcoming
Ma, M. L. Z. , and V. Song. 2016. Discretionary Loan Loss Provisions and Systemic Risk in the Banking Industry. Accounting Perspectives 15 (2): 89-130.
Wahlen, J. M.. 1994. The Nature of Information in Commercial Bank Loan Loss Disclosures. The Accounting Review 69 (3): 455-478.
Wall, L. D. and I. Hasan. 2004. Determinants of the Loan Loss Allowance: Some Cross-Country Comparisons. Bank of Finland Discussion Papers.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72948-
dc.description.abstractHarris et al. (2018)之研究發展了一個衡量未來一年預期信用損失的方法,其中結合了若干信用風險指標,並排除銀行經理人自由裁量空間之影響,本研究即欲探討該模型於我國之適用狀況。本研究將該預測模型適用於2000年至2018年之台灣銀行業時,預測模型所計算出之預期信用損失之預測能力略低於前期淨壞帳沖銷數,但相較於前期備抵壞帳與前期壞帳費用,其預測能力較好。另外,2011年財務會計準則公報第34號第三次修訂之適用並未對預期信用損失之預測能力造成太大影響。最後,將該預測模型應用於已開始適用國際財務報導準則第9號「金融工具」之2018年時,其解釋力大幅提升,且預測模型之預測能力均較前期淨壞帳沖銷數、前期備抵壞帳、前期壞帳費用為佳。zh_TW
dc.description.abstractThis study is intended to explore the expected credit losses of the banking industry's loans and receivables, and Harris et al. (2018) have developed a methodology for measuring expected credit losses in the coming year. It combines several credit risk indicators and is committed to eliminating the impact of bank managers' discretionary spaces. In this study, when the forecast model is applied to loans and receivables of the banking industry in Taiwan, the predictive ability of ExpectedRCL calculated by the forecast model is slightly worse than net charge-offs’s, and is better than loan loss allowance’s and loan loss provision’s. In addition, the application of the third amendment to the Financial Accounting Standards Bulletin No. 34 in Taiwan did not have much impact on the predictive ability of the forecast model. Besides, when the forecast model was applied to year 2018, when the banking industry in Taiwan began to apply the International Financial Reporting Standard No. 9 “Financial Instruments”, its explanatory power was greatly improved, and the predictive ability of the forecast model was better than that of net charge-offs, loan loss allowance and loan loss provision.en
dc.description.provenanceMade available in DSpace on 2021-06-17T07:11:23Z (GMT). No. of bitstreams: 1
ntu-108-R06722011-1.pdf: 1592313 bytes, checksum: 57b490146580d1712e123537ed492bae (MD5)
Previous issue date: 2019
en
dc.description.tableofcontents論文口試委員審定書 i
謝辭 ii
中文摘要 iii
英文摘要 iv
第一章 緒論 1
第一節 研究背景與目的 1
第二節 研究架構 2
第二章 文獻探討與研究假說 3
第一節 銀行業之備抵壞帳、壞帳費用與預期信用損失 3
第二節 財務會計準則公報第34號與其第三次修訂 7
第三節 國際財務報導準則第9號「金融工具」 8
第三章 研究方法 10
第一節 資料來源與樣本選取 10
第二節 實證模型與變數衡量 14
第三節 資料分析方法 24
第四章 實證結果與分析 25
第一節 敘述性統計 25
第二節 樣本相關係數檢定 27
第三節 預測模型與預測能力之實證結果 31
一、所有期間 32
二、財務會計準則公報第34號第三次修訂前之期間 45
三、財務會計準則公報第34號第三次修訂後之期間 62
第五章 結論 79
第六章 參考文獻 81
dc.language.isozh-TW
dc.subject預期信用損失zh_TW
dc.subject備抵壞帳zh_TW
dc.subject壞帳費用zh_TW
dc.subject淨壞帳沖銷zh_TW
dc.subject預測模型zh_TW
dc.subjectExpected credit lossen
dc.subjectLoan loss allowanceen
dc.subjectLoan loss provisionen
dc.subjectNet charge-offen
dc.subjectForecast modelen
dc.title放款及應收款預期信用損失率之探討-以台灣銀行業為例zh_TW
dc.titleThe expected credit losses of Taiwan banking industry's loans and receivablesen
dc.typeThesis
dc.date.schoolyear107-2
dc.description.degree碩士
dc.contributor.coadvisor劉嘉雯(Chia-wen Liu)
dc.contributor.oralexamcommittee邱士宗,林瑞青
dc.subject.keyword預期信用損失,備抵壞帳,壞帳費用,淨壞帳沖銷,預測模型,zh_TW
dc.subject.keywordExpected credit loss,Loan loss allowance,Loan loss provision,Net charge-off,Forecast model,en
dc.relation.page82
dc.identifier.doi10.6342/NTU201901631
dc.rights.note有償授權
dc.date.accepted2019-07-19
dc.contributor.author-college管理學院zh_TW
dc.contributor.author-dept會計學研究所zh_TW
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