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研究生：	林淑卿 Lin, Shu-Ching
論文名稱：	盈餘管理與交易量訊息對公司財務危機預測之影響:量變數與質變數 The Effect of Discretionary Accruals and Stock Trading Volume on Firm Bankruptcy Prediction:Quantitative versus Qualitative Variables
指導教授：	黎明淵 Li, Ming-Yuan
學位類別：	碩士 Master
系所名稱：	管理學院 - 財務金融研究所 Graduate Institute of Finance
論文出版年：	2010
畢業學年度：	98
語文別：	英文
論文頁數：	49
中文關鍵詞：	破產預測、裁決性應計項目、股市交易量、羅吉斯迴歸
外文關鍵詞：	Bankruptcy prediction, Discretionary accruals, Stock trading volume, Logistic regression
相關次數：	點閱：177 下載：2
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不同於只使用單一資訊的傳統財務危機預測模型，本篇論文結合了會計、市場、盈餘管理與股市交易量訊息來建構財務危機預測模型。本研究採用500家美國公開上市非金融業公司在1999到2008年期間的資料為樣本。我們的實證研究結果包含兩部分。首先在第一部分，我們清楚地界定了傳統用於破產預測的變數z-score跟DD(違約距離)為我們的量變數。考量到總有其它因素造成破產預測上的偏誤，我們使用裁決性應計項目與股市交易量來衡量z-score與DD所提供的訊息品質，故本篇論文將它們定義為質變數。研究結果指出結合量變數與質變數所成的混和模型預測表現優於本研究中使用單一資訊的模型。接著在第二部分中，我們利用動態模型來觀察z-score與DD在不同的裁決性應計項目與交易量下對破產預測的權重變化，進而去瞭解盈餘管理與交易量訊息對公司財務危機預測之影響。結果顯示動態模型的預測能力比靜態模型佳。

In contrast with traditional bankruptcy prediction approaches that use only one source of risk predictors in the model, this paper combines accounting variables, market variables, earnings management proxy variables, and stock trading volume to construct models for bankruptcy prediction. We adopted 500 US public non-financial firms during the period 1999 to 2008 to be our samples. Our empirical findings consist of two major parts. In the first part, we clearly define the traditional variables used for bankruptcy prediction, i.e., z-score and DD (distance to default) as the quantitative variables. Considering there is always other additional information that causes bias in bankruptcy prediction, we regard discretionary accruals and stock trading volume as the two proxy variables to measure the information quality of the z-score and DD and defined them as the qualitative variables. We then combine the quantitative and the qualitative information to build our hybrid model. The results indicate that the hybrid models we proposed outperform the highbred models used in this research. In the second part, we use the hybrid model to design a system with dynamic loadings on the DD and z-score, and the findings suggest that a model with dynamic loadings has better bankruptcy prediction performance than a model with static loadings.

CONTENTS
ABSTRACT	I
CONTENTS	IV
TABLE OF CONTENTS	V
FIGURE OF CONTENTS	VI
1.	Introduction	1
2.	Literature review	5
3.	Definition of bankruptcy risk factors	11
3.1 Quantitative variables	11
3.1.1 Accounting-based information	11
3.1.2 Market-based information	12
3.2 Qualitative variables	14
3.2.1 Earnings management proxy variable	14
3.2.2 Trading volume variable	16
4.	Methodology	17
4.1 Model specification: the logit model	17
4.2	Validation methodology	20
4.2.1 Cumulative accuracy profile (CAP) curve	20
4.2.2 Receiver operating characteristic (ROC) curve	22
5.	Empirical results	23
5.1 Data	23
5.2 Bankruptcy prediction models: quantitative versus qualitative variables	25
5.2.1 Model estimation results	25
5.2.2 Bankruptcy prediction performance	26
5.2.3 Out of sample predictability of default	27
5.3 Bankruptcy prediction models: static versus dynamic loadings	28
5.3.1 Model estimation results	28
5.3.2 Bankruptcy prediction performance	30
5.3.3 Out of sample predictability of default	31
6.	Discussion and conclusions	32
Reference	34
Appendix	36

TABLE OF CONTENTS
Table 1 Variable Definition	40
Table 2 Summary Statistics and Comparisons of the Explanatory Variables between Bankrupt and Non-bankrupt Firms	41
Table 3 Correlation Coefficient Matrix with all Explanatory Variables	42
Table 4 Estimates of Logistic Model:  Quantitative versus Qualitative Variables	42
Table 5 Accuracy Ratios for Highbred and Hybrid Models:  In-sample Validation	43
Table 6 Accuracy Ratios for Highbred and Hybrid Models:  Out-of--sample Validation	43
Table 7 Estimates of Hybrid Model - Three Levels	44
Table 8 Estimates of Hybrid Model - Four Regions	45
Table 9 Accuracy Ratios for Static and Non-uniform Loadings:  In-sample Validation	46
Table 10 Accuracy Ratios for Static and Non-uniform Loadings:  Out-of-sample Validation	46

FIGURE OF CONTENTS
Figure 1 Illustration of CAP Curves	47
Figure 2 Illustration of ROC Curves	47
Figure 3 The CAP Curves: Quantitative versus Qualitative Variables	48
Figure 4 The ROC Curves: Quantitative versus Qualitative Variables	48
Figure 5 The CAP Curves: Static Loadings versus Non-uniform Loadings	49
Figure 6 The ROC Curves: Static Loadings versus Non-uniform Loadings	49





                                    

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校內：2012-07-09公開
校外：2020-06-01公開

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