摘要(英) |
Researches on the prediction of financial distress companies focus on traditional financial indicators. Common financial indicators have provided good predicting ability over firms whose core businesses are operated inefficiently, or having extremely high leverage. For those whose core businesses remain profitable, but craving for financial manipulation, the predictability is insufficient. Besides, many under-performed companies varnish their financial statements by selling inventories to subsidiaries; this phenomenon will not be discovered through the conventional model. To improve the predictability of the financial distress model, we include the pledge ratio of major shareholders, the percentage of subsidiaries purchasing parent companies’ shares, and the ratio of short-term investment as explanatory variables, and examine the differences between consolidated statement and ordinary statement. Based on our empirical results, we find that incorporating financial manipulation indicators helps to increase predictability (90.625%-93.75%). In addition, consulting consolidated statements instead of unconsolidated statements boosts the predictability from 68% to 76%. |
參考文獻 |
參考文獻
中文部分:
1. 台灣經濟研究院,我國企業爆發財務危機之成因、影響及防範之研究,行政院經濟建設委員會,民國88年三月。
2. 李智雯,運用現金流量資訊預測企業財務危機之實證研究,政治大學會計系碩士論文,民國89年。
3. 俞秀美, 財務危機模型之研究--考慮背書保證及董監事質押因素,中原大學會計學系碩士論文,民國89年。
4. 徐淑芬,台灣上市公司財務危機預警-應用多變量CUSUM時間序列分析,東華大學企業管理研究所碩士論文,民國88年。
5. 梁清源,財務危機判斷模式之探討-公司財務比率與相對財務比率判斷能力,淡江大學管理科學研究所碩士論文,民國81年。
6. 陳肇榮,運用財務比率預測企業財務危機之實證研究,政治大學企業管理研究所碩士論文,民國72年。
7. 郭人誌 ,上市公司交叉持股行為特性之研究,國立政治大學國際貿易研究所碩士論文,民國88年。
8. 張美玲,由交叉持股觀點探討財務危機問題-台灣上市公司之實證研究,淡江大學管理科學研究所碩士論文,民國89年。
9. 葉怡成,應用類神經網路,儒林圖書有限公司,民國88年四月2版。
10. 葉怡成,類神經網路模式應用與實作,儒林圖書有限公司,2000年四月七版。
11. 戴鳳玲,類神經網路與Logit模式對財務危機預測能力之比較研究-以台灣股票上市公司為例,東吳大學企管研究所論文,民國85年。
12. 顏月珠,商用統計學,三民書局,民國84年9版。
英文部分:
1. Altman, E.I. (1968), “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,” Journal of Finance 22, 589-609.
2. Altman, E.I. and G. V. Marco and F. Varetto (1994), “Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks,” Journal of Banking and Finance 18,505-529.
3. Beaver, W.H. (1966), “Financial ratios as predictors of failure,” Journal of Accounting Research 4, 71-111.
4. Blum, M. (1974), ”Failing company discriminant analysis,“ Journal of Accounting Research 12, 72-102.
5. Coats, P. K. and L. F. Fant (1993), “Recognizing financial distress patterns using a neural network tool”, Financial Management12,142-155.
6. Hopwood, W., J. Mckeown and Mutchler, J. (1989), “A test of the incremental explanatory power of opinions qualified for consistency and uncertainty”, Accounting Review, 28-48.
7. Koh, H. (1991), “Model predictions and auditor assessments of going concern status,” Accounting and Business Research 12, 331-338.
8. Koh, H. and S. Tan (1999),“A neural network approach to the prediction of going concern status,” Accounting and Business Research 21, 211-216.
9. Martin, D. (1977),”Early Warning of banking failure, ” Journal of Banking and Finance, 249-276.
10. Mossman, C.E., G. G. Bell, L. M. Swartz and H. Turtle (1998),”An empirical comparison of bankruptcy models,” Financial Review33, 35-54.
11. Odom, M.D. and Sharda, R. (1990), “A neural network model for bankruptcy prediction,” IEEE INNS IJCNN International Joint Conference on Neural Networks 2, 163-168.
12. Ohlson, J .A. (1980), “Financial ratios and the probabilistic prediction of bankruptcy,” Journal of Accounting Research, 109-131.
13. Platt, H. D. and Marjorie B. Platt (1991), “A note on the use of industry-relative ratios in bankruptcy prediction, ”Journal of Banking and Finance, 1183-1194. |