破產預測模型之比較

A Comparison of Bankruptcy Prediction Models

張琮勛(Tsung-Hsun Chang)

指導教授 ： 呂育道

國立臺灣大學/電機資訊學院/資訊網路與多媒體研究所/碩士(2011年)

https://doi.org/10.6342/NTU.2011.10910

摘要

公司的倒閉風險，在學術界及銀行界都是相當受到重視的議題，一個判斷上的失誤，可能會產生重大的影響。本論文希望可以利用各種不同的資料分類方法，從傳統的Z-score、Logit迴歸到近期相當熱門的類神經網路及支援向量機等方法，尋找出較佳的破產預測模型，使企業或投資人能夠在公司營運出現問題前及早做出因應，以避免產生巨大的損失。

關鍵字

破產預測 ； 支援向量機 ； 類神經網路 ； Z-score

並列摘要

The risk of corporate bankruptcy has been an important topic for the academia and the banking sector, as any misjudgment may lead to disasters. This thesis compares the performance of various data classification methods, such as Z-score, Logit regression, neutral networks and support vector machines. With a better model, corporations and investors can be better prepared before companies show signs of bankruptcy so that huge losses may be avoided.

並列關鍵字

bankrupt prediction ； SVM ； ANN ； Z-score

參考文獻

[1] Agarwal, V. and Taffer, R. (2008), “Comparing the Performance of Market-Based and Accounting-Based Bankruptcy Prediction Models.” Journal of Banking & Finance, 32(8), 1541–1551.

[2] Altman, E. I. (1968), “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” The Journal of Finance, 23(4), 589–609.

[3] Altman, E.I., Haldeman, R. and Narayanan, P. (1977), “ZETA Analysis: A New Model To Identity Bankruptcy Risk of Corporations.” Journal of Banking & Finance, 1(1), 64–75.

[4] Beaver, W. H. (1966), “Financial Ratios as Predictors of Failure.” Journal of Accounting Research, 4, 71–111.

[5] Bessaou, M. and Siarry, P. (2001), “A Genetic Algorithm with Real-Value Coding To Optimize Multimodal Continuous Functions.” Structural and Multidisciplinary Optimization, 23(1), 63–74.