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| 題名: | 台灣上市公司企業危機探討與預警之研究 |
| 作者: | 張志光 |
| 貢獻者: | 亞洲太平洋研究所
連輕盈 |
| 關鍵詞: | 財務危機;預警系統;上市公司 |
| 日期: | 2001 |
| 上傳時間: | 2015-05-13 11:09:31 (UTC+8) |
| 摘要: | 當企業發生危機時，不僅企業本身蒙受其害，對公司之上下游廠商、債權人及投資大眾更會直接間接受到其引響，尤其在資訊如此開放之現代，消息面所帶來的傷害已不是危機公司單純所引響的廠商或債權人，而是更嚴重的造成市場信心的潰散。因此，當危機發生時除要做好妥善的危機管理，更要在平時建立一套早期警報系統（early warning system）來對初始的危機做預防及修正。 本研究即試圖運用財務性的因素來對企業公司做探討及預警。以民國87至民國90年第一季間曾遭列為全額交割、下市或停止營業處分的公開發行公司作為樣本，其樣本數共30家在加其正常公司之配對樣本共60家公司，以危機發生之前三年的財務變數來建立及計算危機之正確率。而這些財務變數包括了償債能力、獲利能力、成長能力、經營能力及財務結構等五大項20個財務變數。經過因素分析所篩選出來的因子，各年度有所不同，將這些因子輸入類神經網路的倒傳遞網路可得以下之結論： 1. 將分別不同之因子於不同之年度其網路模型預測之正確率依危機之前一年、前二年、前三年分別為90﹪83.34﹪及70.00﹪之正確率。 2. 危機前一年網路輸出值的誤差明顯較危機前二年及危機前三年為小，且距離危機年度越久，模式之正確率越低。 3. 類神經網路以其特有的非線性處理能力、容錯能力及自我學習資料間潛在規則的能力，適合應用在指標之間關係複雜的預測環境中。 4. 失敗公司之償債能力不足，負債比率過高且變現能力差，其反映出的財務比率於流動比率、現金流量比率、現金再投資比率及負債比率上。 5. 近三年發生危機的30家公司12種產業裡，可發現營建業即占了26.67﹪，其次是鋼鐵業的13.33﹪，此兩者營建類與鋼鐵類佔了四成的比率，由二者偏屬公共工程或大型工程，可判定國家投入的公共工程的不足所帶動的有效需求低落。
When enterprise occurs financial crisis, it not only harms to the own business but also affect directly the related business or investors and the stockholders. Especially in such days of wide-open information, the related crisis news could cause worse results such like the broken-confident to the whole market. Therefore, to control the crisis companies well while the crisis is occurred is necessary, and to set up an early warning system to avoid the happening of crisis in usual time is quietly important too. The research tries to use the financial variables to discuss and to predict the business crisis which is according to the companies in public that suffering to be the list for full delivery stock, stock departing from market, or temporary ceasing operation stock as the samples during 1999 to the first quarter of 2001.30crisis companies sample is collected while the total company sample is 60.By the pre 3 years financial variable before the moment year of crisis happening can create the model and count the accurate of the crisis. These variables including pay debt capability, earning capability, growth capability, management capability and financial structure which five items including 20 variables. Factors can be collected by factor-analyze. Each year surely get different factors, and input these factors to BPN that can get some conclusions： 1.Input different factors by different years to the artificial neural network can get different result. The first year can win the accurate rate to 90﹪, the second year can get accurate rate to 83.34﹪and the third year can get just 70.00﹪accurate rate only. 2.The rate of first year is more accurate then the other two year, and the far the year is taken the less the accurate is counted. 3. Artificial neural network has the characteristic of non-linear, with this characteristic it could tolerate mistake and has self —learning capability. And is suit to use on complex circumstance. 4.The financial crisis business has no ability to pay debt, and the debt ratio is also highly, which reflect to the financial variables are cash flow ratio, debt ratio, cash reinvestment ratio and current ratio. 5. We can find that 30 companies among 12 industries of happening financial crisis in recent 3 year, the building industry had take 26.67 percentage and the next one is steel industry which take 13.33 percentage. The two industries took 40 percent of all financial crisis companies. From the catalog of the two industries, we can know the low investment of government expand in building and steel industry and it also cause the decrease of demand. |
| 顯示於類別: | [國際事務與企業學系(亞太研究碩士班,公共政策研究碩士班,歐洲研究碩士班)] 博碩士論文-亞太研究碩士班
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