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系統識別號 U0026-1208201611153000
論文名稱(中文) 探討晨星基金評等是否有助於解釋共同基金未來存活率
論文名稱(英文) Examining Whether Using the Morningstar Ratings Helps Explain the Survival of Mutual Funds
校院名稱 成功大學
系所名稱(中) 會計學系
系所名稱(英) Department of Accountancy
學年度 104
學期 2
出版年 105
研究生(中文) 李興彥
研究生(英文) Hsing-Yen Li
學號 r16021019
學位類別 碩士
語文別 英文
論文頁數 30頁
口試委員 指導教授-顏盟峯
口試委員-許英麟
口試委員-王澤世
口試委員-劉裕宏
中文關鍵字 共同基金  羅吉斯迴歸  危機預警模型  晨星評等 
英文關鍵字 mutual funds  logistic regression  distress pre-warning model  Morningstar rating 
學科別分類
中文摘要 本篇論文挑選經晨星基金評等並屬於在台灣募集資金且投資於台灣地區的共同基金作為研究樣本,接著應用羅吉斯迴歸模型建立共同基金的危機預警模型,而後藉由交叉驗證法比較包含晨星評等作為基金危機指標的危機預警模型其預測基金發生危機的準確度是否高於無包含晨星評等作為基金危機指標的危機預警模型,如果包含晨星評等的危機預警模型預測準確度較高,則我們就可以推論晨星評等有助於辨別共同基金的存活率,然而在台灣國內共同基金方面,研究結果發現無包含晨星評等作為基金危機指標的危機預警模型預測準確度較包含晨星評等的危機模型高出些微,因此晨星基金評等幫助解釋共同基金未來存活率的效果不大。本研究建議後續的研究者可以採用不同的變數作為基金危機指標,抑或是擴大樣本期間,也許會得到不同結果。
英文摘要 In this paper, Taiwanese domestic funds that raised money invested in Taiwan and were rated by Morningstar are chosen as samples. A logistic regression model is applied to construct a distress pre-warning model. A cross-validation method is then used to examine whether the predictability of a pre-warning model that contains Morningstar ratings as independent variables is higher than the other model which does not contain Morningstar ratings. If the predictability is more accurate in the model with Morningstar ratings, we can infer that these variables help explain the survival of mutual funds. The finding shows the predictability of the distress pre-warning model without Morningstar ratings to be slightly higher than the model with Morningstar ratings for Taiwanese domestic funds that both raised money and invested in Taiwan. The contribution of this study is demonstrating that Morningstar ratings cannot help explain the survival of mutual funds and suggesting other researchers trying different indicators as independent variables or to extend the sample period to improve mutual fund distress predictability.
論文目次 Contents
1. INTRODUCTION 1
1.1 Research background 1
1.2 Research motivation 4
1.3 Research objectives and contributions 5
1.4 Research framework 5
2. LITERATURE REVIEW 5
2.1 Logistic regression model 5
2.2 Distress pre-warning variables 8
3. RESEARCH METHODOLOGY 11
3.1 Data and sample period 11
3.2 Definition of distress pre-warning indicators 12
3.3 Research methodology 16
4. RESULTS 19
4.1 Descriptive statistics 19
4.2 Results of logistic analysis 22
4.3 Cross-validation 24
5. CONCLUSIONS 25
REFERENCES 27

List of tables
Table 1 1 Morningstar rating table 3
Table 1 2 Weights of overall rating 3
Table 3 1 Form of selected variables 12
Table 3 2 Weights of overall rating 16
Table 4 1 Descriptive statistics 20
Table 4 2 Pearson and Spearman correlation coefficient 21
Table 4 3 Variable selection of distress pre-warning model 23
Table 4 4 Predictability of distress pre-warning model 25

List of figures
Figure 1 1 Morningstar rating scale 3


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