後博達時代上市公司財務危機預警研究

郭宇閎(Yu-Hung Kuo)

指導教授 ： 吳琮璠

國立臺灣大學/管理學院/會計學研究所/碩士(2005年)

https://doi.org/10.6342/NTU.2005.00466

摘要

民國九十三年六月，博達科技股份有限公司爆發財務危機，震驚國內市場以及有關當局。博達長久以來利用財務操作虛飾營運表現與財務狀況，然而投資人並未察覺財務報表舞弊的發生，最後受到鉅額損害的結果。本文探討幾項相關議題，研究博達案對財務危機預警模式的影響，主要目的為提昇財務危機預警模式的預測能力，除採用過去研究的財務變數外，更加入現金流量變數與非財務變數之會計師意見及事務所規模以提升模型解釋力。本研究使用Logistic迴歸模型與類神經網路與為預測模式進行財務危機預測。
　　本文以民國89年至93年間發生財務危機的70家上市公司，搭配正常公司125家為樣本，建立五個Logistic模型以探討會計師意見與事務所規模的解釋能力。另以Logistic模型的預測結果為基準，以類神經網路軟體NeuralWorks Professional Ⅱ/PLUS建立倒傳遞網路，並比較預測結果的差異。
　　實證結果顯示，會計師意見具有顯著的增額解釋能力，但事務所規模對會計師意見的交叉影響並不顯著。進行類神經網路與Logistic模型預測結果的比較後，發現Logistic模型在預測財務危機的表現較佳，類神經網路在正常公司正確率與總體正確率優於Logistic模型。

關鍵字

現金流量 ； 會計師意見 ； 事務所規模 ； 類神經網路

並列摘要

In June 2004 Procomp Infomatics Ltd. exposed financial failure that shocked domestic market and authorities. Procomp misstating operation performance and financial condition by financial dealings, however, investors were not aware of Procomp management fraud and suffered huge losses. Focusing on some related issues, this thesis studies the influences that Procomp case brings to the early warning model of financial distress. The main purpose of this thesis is to enhance effectiveness of financial distress predicting models. Using financial variables of past studies, this thesis adds cash-flow variables and non-financial variables – going-concern opinion and auditor size – as predictors to improve explanatory power of model. Besides, this research uses logistic regression models and artificial neural networks to predict financial distress.
　　Using the firm-level data from 2000 to 2004, this research uses a sample of 195 listed companies, 70 in crisis state and 125 in normal state, and establishes five logistic models to examine explanatory power of going-concern opinion and auditor size. Establishing back-propagation networks with the artificial neural network software “NeuralWorks Professional Ⅱ/PLUS”, we also use artificial neural networks to compare with logistic regression model to identify the difference.
　　The empirical results indicate that going-concern opinion has significant incremental explanatory power over financial variables, but auditor size doesn’t have significant interactive effect on going-concern opinion. Artificial neural network has better performance on normal company and overall prediction, but logistic regression model has better hit rate of stressed companies.

並列關鍵字

Cash Flow ； Going-Concern Opinion ； Auditor Size ； Artificial Neural Network

參考文獻

審計準則公報第三十三號，財務報表查核報告，民88。

Altman E. I. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” The Journal of Finance (September): 589-609.

Altman E. I. 1982. “Accounting Implications of Failure Prediction Models.” Journal of Accounting, Auditing and Finance (Autumn): 4-19.

Barniv R., Agarwl A. and Leach R. 1997. “Predicting the Outcome Following Bankruptcy Filing: A Three-state Classification Using Neural Networks.” Intelligent Systems in Accounting, Finance and Management 6: 177-194.

Beaver W. H. 1966. “Financial Ratios as Predictors of Failure.” Journal of Accounting Research 4 (Supplement): 71-111.