綜合收益與應計項目在財務危機預測中的表現

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姓名

丁諾芙(Dinda Novitasari) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

綜合收益與應計項目在財務危機預測中的表現
(The Role of Comprehensive Income and Accrual in Predicting Bankruptcy)

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摘要(中)

這項研究旨在擴展財務危機預測研究方向。我們專注於找出能提升財務危機預測的因子，其中大多數以前的研究構建了基於財務比率的模型來進行預測。以往的研究將可自由分配的應計項目當作盈餘管理的代表和權益管理與財務比率相結合，證明盈餘管理對財務比率和破產預測有影響。然而，以前的研究沒有一個分析應計對財務比率的影響，以及它如何影響破產預測的表現。因此，為了研究此問題：應計項目資料是否有助於財務比率制定更好的破產預測模型？為了檢驗這些問題，我們提出了財務比率和權責發生製模型的組合，並進行了綜合分析。這項研究表明，綜合收入可以提供比主要收入更多的信息。這些發現將有助於私人和公共投資作出貸款決定。

摘要(英)

This study is aimed to fill the gap in the bankruptcy prediction research. We focused on the variable predictors while most of the previous research build a financial ratio-based model to make a prediction. Previous work combined discretionary accrual as a proxy of earnings management with the financial ratio to prove that earning management have the impact on financial ratio and bankruptcy prediction. However, none of the previous research presents an analysis of the impact of accrual on financial ratios and how it may affect bankruptcy prediction performance. Therefore, this study is motivated by research question: could accruals information help financial ratios to make better bankruptcy prediction model? To examine this question, we proposed a combination of financial ratio and accrual model and conducted a comprehensive analysis. This study demonstrated that comprehensive income could give more information than the main income. These findings will help private and public investment to give a lending decision.

關鍵字(中)

★ 應計項目
★ 財務比率
★ 破產預測

關鍵字(英)

★ accruals
★ financial ratio
★ bankruptcy prediction

論文目次

摘要 i
Abstract ii
Acknowledgements iii
Table of Contents iv
List of Figures vi
List of Tables vii
1 Introduction 1
1.1 Background 1
1.2 Problem Statements 2
1.3 Outline of Chapters 2
2 Literature Review 3
2.1 Related Works 3
2.2 Bankruptcy Prediction Overview 4
2.3 Financial Ratio 4
2.4 Accruals 5
2.5 Random Forest 6
2.5.1 Variable Importance in Random Forest 6
2.5.2 Variable Interaction in Random Forest 7
2.6 Support Vector Machines 8
2.7 K-Nearest Neighbours 10
2.8 Naive Bayes 10
2.9 Research Hypotheses 11
3 Research Method 12
3.1 Experiment Architecture 12
3.2 Dataset 12
3.3 Experiment Design for Hypothesis I 14
3.4 Evaluation metrics 19
3.5 Experiment Settings 20
4 Experiment Results 21
4.1 Study for Hypothesis I 21
4.1.1 Baseline Model 21
4.1.2 Proposed Model 23
4.1.3 Baseline Model v. Proposed Model 26
4.1.4 Feature Importance and Variable Interaction 28
4.1.5 Impact of Accrual on Prediction Model Improvement 29
5 Conclusion and Suggestion 32
5.1 Conclusion 32
5.2 Suggestion 32
Bibliographies 33
Appendixes A 35
Appendixes B 42

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指導教授

梁德容(Deron Liang)

審核日期

2018-7-26

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