公司治理、績效與危機預警機制-台灣製造業之實證研究

Corporate Governance, Performance and Financial Crisis Warning System–The Empirical Study of Taiwanese Manufacturing Industries

鄧伊惠(Yi-Huei Deng)

指導教授 ： 劉定焜

朝陽科技大學/管理學院/財務金融系/碩士(2012年)

摘要

全球金融風暴造成全球股市及房市下跌，信用緊縮，全球消費驟減，導致經濟成長減緩，至2008年底引發一連串的企業及工廠倒閉與企業裁員，造成經濟衰退、失業率升高以及通貨緊縮等現象。因此，在面對全球金融風暴，與企業爆發財務危機的環境下，企業危機預警機制的建立，顯得相當重要與迫切。台灣上市公司陸續出現財務危機，而造成財務缺口的原因，可能是經營決策或錯誤投資，與物流管理失當等，亦可能由於公司內部舞弊，利用會計作假帳，惡意掏空公司資產，或涉及內線交易，各公司相互轉投資與交叉持股，不當背書保證與擅自挪用資金，透過關係企業股票相互質押貸款，以及家族掌握董監事席次，刻意隱匿重大訊息與財報不實等，人為因素導致公司營運和財務危機。因此，本研究除了探討公司治理對經營績效的影響外，也進一步將公司治理納入危機預警模型，分析公司治理的好壞是否對公司財務危機預測機率有所影響。
近年多數研究財務危機預警的學者，多數以上市、上櫃個別產業或者以全產業為研究對象，在製造業方面的實證研究較為少見。有鑑於製造業對於我國的重要性，本研究建構2001至2011年上市 (櫃) 製造業公司之balanced panel data。首先，本研究探討公司治理對製造業廠商經營績效之影響程度，有鑑於過去文獻多數僅研究公司治理與營運績效間之關聯性，針對公司治理與危機預警機制之研究並不多見，因此，本研究運用Altman (1968) 所發展的Z-score model，建構台灣製造業危機預警模型。樣本選取方面，除參考過去較多文獻採用之1:1與1:2模式進行危機與正常樣本之實證配對，並考量1:3之配對比例 (Lee and Teng，2009；蔡明春等人，2009；陳雪芳等人，2010)。為了比較不同預警模型的預測準確性，本研究進一步建構Logistic model，除納入Z-score model之財務變數外，考量巴賽爾新協議 (BaselⅢ) 的議題，加入總體經濟與過去文獻鮮少探討之風險變數，亦納入淨值與現金流量之考量，為了檢視公司治理變數是否影響危機預警之預測率，考量納入與未納入公司治理變數之實證模型。
此外，本研究進一步將樣本予以分類，將樣本分為上市、上櫃與全樣本，由於台灣廠商以中小企業為主體，進一步將樣本區分為中小型與大型製造業公司。最後，進一步運用Z-score model與Logistic model建構之危機預警機制，藉此推估不同分類及不同預警模型，何者具有較佳之預測能力，選出適合預測製造業財務危機的機制。
實證結果顯示，Z-score model之分類正確率低於Logistic model，不論在各分類樣本與配對比率上，Z-score model之最高分類準確性 (60.13%) 皆低於Logistic model之最低分類準確性 (76.73%)，表示Z-score model並不適用臺灣之財務資料。若以Z-score model之年資料與季資料比較，年資料之準確性較高，與余義賢 (2008) 以季資料使用Z-score model之估計結果相符，顯示使用季資料建構Z-score model於預測上，呈現不穩定的狀態。
整合各模型之不同，本研究發現以Logistic model預測財務危機之準確性較高，建立之危機預警模型最高準確率可達到80.87%。由此可知，不論在分類樣本與配對比率上，皆以Logistic model之正確分類率為佳。
相較於過去的研究，本研究具備更完整的研究期間與更詳盡的變數及樣本資料，並將製造業樣本予以分類，進一步選擇最適panel data模型進行實證分析，期提供相關資訊予政府機關政策評估與研擬，金融機構營運與危機預警，以及普遍大眾投資決策擬定，以及後續研究者學術研究之參考。

關鍵字

製造業 ； Logistic 模型 ； Z-score 模型 ； 財務危機預警模型

並列摘要

Global financial crisis caused the price of global stock and housing market down, and the credit crunch, global consumption plummeted, lead to slowdown in economic growth. To the end of 2008 a series of enterprises and factories closed down and corporate layoffs, resulting in the recession the unemployment rate increased and deflation. Therefore, in the face of global financial turmoil and the financial crisis broke out in the environment, the establishment of the enterprise crisis early warning mechanism is seemed to be important and urgent. Listed companies in Taiwan continued emergence of the financial crisis may cause by the reasons of business decision-making or error investment, and material flow management misconduct, etc., and may also be due to the company internal fraud interest to use accounting for accounting fraud, malicious hollowed out company assets, or involved inside cross-trade, companies mutual transfer investments and cross holdings, not when the endorsement to ensure that unauthorized use of funds, through affiliated companies stock mutual pledge loans, and family to master directors and supervisors of seats deliberately hidden anonymous material information and earnings are not real, etc., man-made factors that led to the company operational and financial crisis. Therefore, this study besides to detect the impact of corporate governance on business performance, but also further take corporate governance into crisis forecasting model to analyze the corporate governance whether have an impact on the predicted probability of the company''s financial warning system.
Recently, most of the researchers in finance crisis forecasting that select companies of the individual industry or the entire industry in TSEC and OTC. The empirical studies of the manufacturing industry are rarely. In view of the importance of the manufacturing industry in our country, this study constructs the panel data of manufacturing industry companies in Taiwan from 2001 to 2011, and we use the Z-score model developed by Altman (1968). We follow former literatures to use 1:1 and 1:2 patterns on the financial distress and non-financial distress, and consider simultaneous pair of proportion 1:3 construction (see Lee and Teng, 2009, Tsai et al., 2009, and Chen et al., 2010). This research constructs Logistic model for different financial crisis precaution model. The model Besides integrates finance of variable into the Z-score model, corporate governance, macroeconomic factors and a rare discussed risk of variable in the past literatures with regard to the issues of BaselⅢ. In addition, we try to find out the corporate governance mechanism whether has the effects on forecasting, then the empirical models with and without corporate governance are taking into consideration.
The results show that the classification of accuracy of Z-score model is lower than the Logistic model. No matter in each classified sample and pair accurate rate, Z-score model’s highest classified accuracy (60.13%) is lowest than the Logistic model (76.73%). The result shows that Z-score model is not suitable for financial distress prediction model in Taiwan. Compare the sample of year and the season form Z-score model, accurate rate of the yearly data is higher than seasonal data which consisted with Yu (2008). To integrate all the individual financial warning systems, this study finds that the accuracy rate of Logistic model with the highest rate of 80.87%. We can conclude that no matter in the classification or pair, the Logistic model is the best financial warning system among all. This study with a more complete, detailed of the banks, and financial information and we select the optimal model for further empirical analysis. We hope the empirical results would provide some valuable information for government, firms, investors and the further study.

並列關鍵字

financial warning system ； Logistic model ； Z-score model ； manufacturing industry

參考文獻

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