其他綜合收益在預測破產中的作用｜國立中央大學博碩士論文系統

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研究生：	萬米山 Milzam Wafi Azhar
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
指導教授：	梁德容
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 資訊工程學系 Department of Computer Science & Information Engineering
論文出版年：	2022
畢業學年度：	110
語文別：	英文
論文頁數：	43
中文關鍵詞：	其他綜合收益 (OCI) 、財務比率、破產預測
外文關鍵詞：	other comprehensive income (OCI), financial ratio, bankruptcy prediction
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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。

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.

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

                                

[1] E. I. Altman, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,” The Journal of Finance, vol. 23, no. 4, pp. 589–609, Sep. 1968.
[2] J. A. Ohlson, “Financial Ratios and the Probabilistic Prediction of Bankruptcy,” Journal of Accounting Research, vol. 18, no. 1, p. 109, 1980.
[3] F. Barboza, H. Kimura, and E. Altman, “Machine learning models and bankruptcy prediction,” Expert Systems with Applications, vol. 83, pp. 405–417, 2017.
[4] W. H. Beaver, “Financial Ratios as Predictors of Failure,” Journal of Accounting Research, vol. 4, pp. 71–111, 1966.
[5] D. Liang, C.-C. Lu, C.-F. Tsai, and G.-A. Shih, “Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study,” European Journal of Operational Research, vol. 252, no. 2, pp. 561–572, 2016.
[6] Anderson, Joshua D., et al. “Other Comprehensive Income, Its Components, and Analysts’ Forecasts.” SSRN Electronic Journal, 2021.
[7] Dinda et al, “Role of Comprehensive Income in Predicting Bankruptcy”. National Central University, Taiwan, 2013.
[8] J. LEE, S. J. LEE, S. CHOI, and S. KIM, “The Usefulness of Other Comprehensive Income for Predicting Future Earnings,” The Journal of Asian Finance, Economics and Business, vol. 7, no. 5, pp. 31–40, May 2020.
[9] Kanagaretnam, K., Mathieu, R., & Shehata, M. (2009). Usefulness of comprehensive income reporting in Canada. Journal of Accounting and Public Policy, 28(4), 349–365.
[10] Chambers, D., Linsmeier, T. J., Shakespeare, C., & Sougiannis, T. (2007). An evaluation of SFAS No. 130 comprehensive income disclosures. Review of Accounting Studies, 12(4), 557–593. https://doi.org/10.1007/s11142-007-9043-2
[11] J. L. Bellovary, D. E. Giacomino, and M. D. Akers, “A Review of Bankruptcy Prediction Studies: 1930 to Present,” Journal of Financial Education, vol. 33, no. Winter, pp. 1–42, 2007.
[12] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

[13] L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification And Regression Trees, vol. 1, no. 1. 1984.

[14] C. Kelly and K. Okada, “Variable interaction measures with random forest classifiers,”
Proceedings - International Symposium on Biomedical Imaging, pp. 154–157, 2012.

[15] J. Efrim Boritz and D. B. Kennedy, “Effectiveness of neural network types for prediction of business failure,” Expert Systems With Applications, vol. 9, no. 4, pp. 503–512, 1995.
[16] J. Demšar, “Statistical Comparisons of Classifiers over Multiple Data Sets,” Journal of Machine Learning Research, vol. 7, pp. 1–30, 2006.

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