其他綜合收益在預測破產中的作用

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姓名

萬米山(Milzam Wafi Azhar) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

其他綜合收益在預測破產中的作用
(The Role of Other Comprehensive Income in Predicting Bankruptcy)

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至系統瀏覽論文 (2025-7-21以後開放)

摘要(中)

本研究旨在填補破產預測研究的空白。我們專注於變量預測變量，而之前的大多數研究都使用基於財務比率的模型進行預測。沒有一項研究將財務比率和其他綜合收益 (OCI) 結合起來。以前沒有研究分析過 OCI 如何影響破產預測模型中財務比率的表現。因此，本研究的動機是一個研究問題：總 OCI 能否幫助財務比率開發更好的破產預測模型？為了研究這些問題，我們提出了財務比率和總OCI模型並進行了深入分析。與基線模型相比，我們的模型具有更高的預測精度和更低的 I 類和 II 類錯誤率。實驗表明，總OCI可以幫助財務比率提前一年預測美國上市公司的破產情況。調查結果將幫助私人和公共投資者做出貸款決定。

摘要(英)

This study aims to fill a gap in the research on bankruptcy prediction. We focus on variable predictors, whereas most previous research used a financial ratio-based model to make a prediction. None of the studies present a combination of financial ratios and other comprehensive income (OCI). And none prior study analyzed how OCI affect financial ratios on the performance of bankruptcy prediction models. As a result, this study is motivated by a research question: Could total OCI assist financial ratios in developing a better bankruptcy prediction model? To investigate these issues, we proposed a financial ratio and total OCI model and conducted a thorough analysis. Compared with the benchmark model, our model’s prediction accuracy is higher and the Type I and Type II error rate is lower. Experiment revealed that total OCI could assist financial ratios in predicting bankruptcy one year ahead in US-listed companies. These finding will help private and public investors make lending decisions.

關鍵字(中)

★ 其他綜合收益 (OCI)
★ 財務比率
★ 破產預測

關鍵字(英)

★ other comprehensive income (OCI)
★ 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 Motivation 2
1.3 Problem Statements 2
1.4 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 Other Comprehensive Income 5
2.5 Random Forest 5
2.5.1 Variable Importance in Random Forest 6
2.5.2 Variable Interaction in Random Forest 7
2.6 Research Hypothesis 8
3 Research Method 9
3.1 Experiment Architecture 9
3.2 Dataset 10
3.3 Experiment Design for Hypothesis 11
3.4 Evaluation metrics 17
3.5 Experiment Settings 18
4 Experiment Results 19
4.1 Study for Hypothesis 19
4.1.1 Feature Importance and Variable Interaction 20
4.1.2 Impact of OCI on Prediction Model Improvement 22
5 Conclusion and Suggestion 23
5.1 Conclusion 23
5.2 Suggestion 23
Bibliographies 25
Appendixes A 30

List of Figures

Figure 2.1 Random Forest Flowchart 6
Figure 3.1 Experiment Architecture 9
Figure 3.2 DET Curve of Baseline Comparison 12
Figure 3.3 Flowchart of Bankruptcy Prediction 13
Figure 3.4 Flowchart of Obtain Consistently Recognized Bankrupt Company Data 14
Figure 3.5 Flowchart of Obtaining Bankrupt Companies Data 15
Figure 3.6 Flowchart of Filter Companies 16
Figure 4.1 DET Curve of M0 and M1 19
Figure 4.2 Variable Importance 21
Figure 4.3 Selected Area for Impact Analysis 21
Figure 4.4 Detailed Points of Selected Area 22
Figure 4.5 Data Distribution of Correctly Predicted Bankrupt Companies by M1 and M0 23

List of Tables
Table 3.1 List of Variables Interest 10
Table 3.2 List of Experimental Variables 11
Table 3.3 Profile Analysis of Variables 12
Table 3.4 Wilcoxon Tests Result for Baseline Comparison 13
Table 3.5 Information of Hypothesis Models 14
Table 3.6 Confusion Matrix 16
Table 3.7 Parameter of each algorithm 17
Table 3.8 Range of Cost Ratio and Threshold 18
Table 4.1 Misclassification Cost of M0 model 19
Table 4.2 Misclassification Cost of M1 model 20
Table 4.3 Wilcoxon Tests Result for M0 model and M1 model 21
Table 4.4 Variable Interaction in M1 Model 22

參考文獻

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指導教授

梁德容

審核日期

2022-8-23

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