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研究生: 吳敏聖
Wu, Min-Sheng
論文名稱: 美國商業銀行備抵呆帳是否隱含對公司債市場違約風險之資訊?
Does the Loan Loss Reserve of US Commercial Banks Contain Information for Corporate Bonds Market Default Risk?
指導教授: 王澤世
Wang, Tse-Shih
學位類別: 碩士
Master
系所名稱: 管理學院 - 會計學系
Department of Accountancy
論文出版年: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 53
中文關鍵詞: 備抵呆帳公司債市場利差動態縱橫資料模型
外文關鍵詞: loan loss reserve, corporate bond market spread, dynamic panel data model
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  • 本篇研究以2009第一季至2012年第二季,215家美國商業銀行之追蹤資料 (panel data)為樣本,並利用固定效果之動態追蹤資料模型及時間序列模型分析檢視美國兩個重要的金融市場:債券市場及信貸市場,兩者之間是否有違約風險資訊傳遞關係。研究結果顯示,銀行盈餘顯著負相關影響呆帳費用金額,代表銀行經理人並無使用呆帳費用平穩化銀行盈餘,而此結果與先前文獻研究不一致,不一致原因是近期會計法規已更加嚴格審視公允價值衡量之準則,用以減少公司盈餘管理程度,使得財務報表更趨允當表達。
    此外,公司債債券市場利差顯著正相關影響經理人提列呆帳費用、淨打銷呆帳費用以及銀行不良貸款金額。更深入的解釋,且債券市場利差領先不良貸款四期(四個季度)。不過此結果並未顯示在小規模銀行中,這代表小規模銀行與債券市場連結性較差,所以,一般而言,信貸市場確實隱含債券市場的違約風險資訊。

    This study examines 215 commercial banks in U.S. during the period of 2009Q1-2012Q2. This study uses dynamic panel data regression models and time series models to analyze the influence of bond market default risk information on loan market. This study finds that the loan loss provision is negatively impacted by earnings before tax and provision, but positively impacted by spread in the high yield corporate bond market. Moreover, this study finds non-performing loans and net charge-offs of large banks are positively related to changes in spreads in the high yield corporate bond market.
    Our major findings are as follows: (1) there is no evidence of earnings management via the loan loss provision/loan loss reserve. (2) As it turns out, large banks executives recognize loan loss provision/loan loss reserve based on default risk of bond market, but not for small banks.

    摘要 III 誌謝 IIII Content V List of Tables VII List of Figures VIII Chapter I Introduction 1 Chapter II Literature Reviews 4 2.1 The Influence of Factors on Loan Loss Reserves 4 2.2 The Impact of Recognizing Loan Loss Reserves 6 2.3 The Relationship between Financial Market and Loan Loss Reserves 9 Chapter III Data and Methodology 11 3.1 Data Collection and Descriptive 11 3.2 Cross Correlogram 24 3.3 Dynamic Panel Data Analysis 28 3.3.1 Dynamic Panel Data Models 28 3.3.2 Fixed Effects Model 31 3.4 Time-Series Analysis 32 3.4.1 Unit Root Test 32 3.4.2 Vector Autoregression Model 34 3.4.3 Granger Causality Test 35 Chapter IV Empirical Results 37 4.1 Dynamic Panel Analysis 37 4.2 Time series analysis 46 4.2.1 Unit Roots Test 46 4.2.2 VAR-Granger Causality Test 47 Chapter V Conclusion 48 References 51

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