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系統識別號 U0026-0812200914135633
論文名稱(中文) Does Earnings Quality Influence the Power of Financial Warning Model?
論文名稱(英文) Does Earnings Quality Influence the Power of Financial Warning Model?
校院名稱 成功大學
系所名稱(中) 會計學系碩博士班
系所名稱(英) Department of Accountancy
學年度 96
學期 2
出版年 97
研究生(中文) 鄭淳元
研究生(英文) Chun-yuan Cheng
電子信箱 r1695115@mail.ncku.edu.tw
學號 r1695115
學位類別 碩士
語文別 英文
論文頁數 54頁
口試委員 指導教授-吳清在
口試委員-陳光谷
口試委員-王澤世
中文關鍵字 none 
英文關鍵字 Z-Score  Option-Pricing Model  O-Score  Financial warning model  Earnings quality 
學科別分類
中文摘要 none
英文摘要 This study examines whether earnings quality (or earnings management) could influence the power of financial warning model, and whether accounting-based model or market-based model is easier to be affected.
Many famous financial warning models such as Altman Z-Score use variables from financial statement. If the accounting numbers are unreliable, the output of financial warning models should be unreliable too. So we predict (1) that earnings quality is positively associated with the predict power of financial warning model; (2) compared to accounting based models, market-based models should be less sensitive to earnings quality because they doesn’t rely accounting numbers that much.
We select Z-Score, O-Score, and Option-Pricing Model to be examined in our study. We use discretionary accruals (DA) to be proxy of earning quality, and the way we used to estimate DA is different from literatures because of accommodating our research designs.
Our results are consistent with our prediction and go through robustness check. In addition we find our hypotheses are stronger in high tech industries. Prior literature provides a theoretical basis to empirical studies of correlation between financial crisis and earnings management, made earnings quality a variable to construct financial warning model, and whether financial warning models work in single fraud case. But our main issue is few to be touched. We believe we provide useful contribution in this issue.
論文目次 1. INTRODUCTION 1
1.1 Overview the issues 1
1.2 Hypothesis development 3
1.3 Research framework 4
2. PRIOR LITERATURES 6
2.1 The definition of financial crisis 6
2.2 Financial warning models 7
2.2.1 Dichotomous classification test 7
2.2.2 Multiple discriminate analysis (MDA) 7
2.2.3 Logit and probit 9
2.2.4 Option-Pricing Model 12
2.2.5 Other financial warning models 15
2.3 Earnings quality and financial crisis 17
2.4 Measures of earnings quality 18
3. RESEARCH DESIGN 21
3.1 Sample selection 21
3.2 Calculate BSM-DD 23
3.3 Calculate DA 26
3.4 Methodology 28
3.4.1 Two-sample t-test 28
3.4.2 Regression analysis 28
3.5 Robustness tests 30
4. EMPIRICAL RESULT 31
4.1 Descriptive statistics 31
4.2 Two-sample t-test 33
4.3 Regression analysis 35
4.4 Robustness tests 37
4.4.1 Delete survived companies 37
4.4.2 Divide high tech firms from others 40
4.4.3 Shift the period of volatility estimation when
calculate BSM-DD 43
5. CONCLUSIONS 46
REFERENCE 48
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