本文選題：創(chuàng)業(yè)板 + 財務危機��； 參考：《湘潭大學》2013年碩士論文

【摘要】：1999年1月我國首次提出建立創(chuàng)業(yè)板，2009年10月30日創(chuàng)業(yè)板歷經(jīng)十年在深圳問世，然而十年的漫長等待與準備并未給創(chuàng)業(yè)板帶來長足的平穩(wěn)發(fā)展，，圍繞創(chuàng)業(yè)板出現(xiàn)了種種不合理現(xiàn)象，如創(chuàng)業(yè)板出現(xiàn)頻繁套現(xiàn)現(xiàn)象，高管們出現(xiàn)頻繁辭職現(xiàn)象，創(chuàng)業(yè)板公司市盈率過高等。這一系列的問題無疑給創(chuàng)業(yè)板上市公司本身以及各方投資者帶來極大的隱患。為了使創(chuàng)業(yè)板上市公司能健康發(fā)展，使企業(yè)規(guī)避必要的風險，創(chuàng)業(yè)板上市公司有必要建立有效的財務預警機制，加強對企業(yè)的有效監(jiān)控，防范企業(yè)財務危機的出現(xiàn)，及早診斷企業(yè)狀況，發(fā)現(xiàn)問題及時采取措施，將財務危機消滅于萌芽階段。 首先本文利用2012年5月1日實施的創(chuàng)業(yè)板退市制度作為界定創(chuàng)業(yè)板財務危機公司與健康公司的標準，篩選出23家財務危機公司和23家財務健康公司，解決了創(chuàng)業(yè)板對兩類公司無界定標準的問題。文中納入了70個財務指標和16個公司治理指標，本文期望從這86個指標中選出真正適合創(chuàng)業(yè)板上市公司財務預警的財務指標和公司治理指標。但是大量指標的選入使得模型構建時存在嚴重的多重共線性問題，本文通過使用逐步回歸模型解決了多重共線性問題并構建出兩個logistic模型。 隨后第一個模型由財務指標逐步回歸得出，模型中只包括流動資產(chǎn)凈利潤率這一盈利能力指標，模型自變量對財務危機的解釋能力為19.5%，兩類公司分類正確率為61.9%。第二個模型在第一個模型的基礎上加入了公司治理變量，隨著公司治理變量的加入，第二個模型變得更加合理與完善，它由盈利能力、現(xiàn)金流量、薪酬激勵三方面的指標構成，模型自變量對公司財務危機的解釋能力達到90.5%，兩類公司的平均分類正確率也達到85.7%。 最后對兩個模型在指標構成、判斷正確率、模型擬合度三個方面作了比較，發(fā)現(xiàn)第二個模型在這三個方面均比第一個模型更有優(yōu)勢。同時發(fā)現(xiàn)兩個模型都有盈利能力指標，說明對于創(chuàng)業(yè)板上市公司而言，公司盈利能力非常重要。同時將4個未放入模型的樣本公司代入第二個模型，以檢測模型的實際應用能力，結果表示模型正確區(qū)分了4個樣本公司的種類，模型的實用性較強，因此第二個模型為創(chuàng)業(yè)板公司提供了一個有效的財務危機預警模型，此模型能及時準確的預測本企業(yè)財務危機。
[Abstract]:In January 1999, China first proposed the establishment of the gem. On October 30, 2009, the gem came out in Shenzhen after 10 years. However, the long wait and preparation of the decade did not bring about a steady development of the gem. There are various unreasonable phenomena around the gem, such as frequent cash phenomenon in gem, frequent resignation of executives, high price-earnings ratio of gem companies and so on. This series of problems undoubtedly brings great hidden trouble to gem listed companies and investors. In order to make the gem listed companies develop healthily and avoid the necessary risks, it is necessary for the gem listed companies to establish an effective financial early-warning mechanism, strengthen the effective monitoring of the enterprises, and prevent the emergence of the financial crisis of the enterprises. Early diagnosis of the situation of enterprises, timely measures to identify problems, the financial crisis will be nipped in the bud stage. First of all, using the gem delisting system implemented on May 1, 2012 as the criterion to define the gem financial crisis companies and health companies, 23 financial crisis companies and 23 financial health companies are selected. It solves the problem that the gem has no defined standard for the two kinds of companies. This paper introduces 70 financial indicators and 16 corporate governance indicators. This paper expects to select financial indicators and corporate governance indicators that are suitable for financial early warning of listed companies in the gem from these 86 indicators. However, the selection of a large number of indicators makes the model have a serious problem of multiple collinearity. In this paper, we use stepwise regression model to solve the multiple collinearity problem and construct two logistic models. Then the first model is derived from the gradual regression of financial indicators. The model only includes the net profit margin of current assets, the explanatory ability of the independent variables to financial crisis is 19.5, and the correct classification rate of the two types of companies is 61.9%. The second model adds corporate governance variables on the basis of the first model. With the addition of corporate governance variables, the second model becomes more reasonable and perfect. It consists of three indicators: profitability, cash flow, and salary incentive. The independent variables of the model can explain the financial crisis of the company up to 90.5%, and the average classification accuracy of the two types of companies is 85.775%. Finally, the two models are compared in terms of index composition, judgment accuracy and model fit degree. It is found that the second model has more advantages than the first model in these three aspects. At the same time, we find that both models have profitability index, which shows that the profitability of listed companies is very important. At the same time, four sample companies that are not in the model are substituted into the second model to test the practical application ability of the model. The results show that the model correctly distinguishes the categories of the four sample companies, and the model is more practical. Therefore, the second model provides an effective financial crisis early warning model for gem, which can predict the financial crisis of the enterprise in time and accurately.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F830.42;F832.51

【參考文獻】

相關期刊論文 前10條

1 張鳴,張艷;財務困境預測的實證研究與評述[J];財經(jīng)研究;2001年12期

2 吳冬梅;朱俊;莊新田;楊霖;;基于支持向量機的財務危機預警模型[J];東北大學學報(自然科學版);2010年04期

3 傅榮,吳世農(nóng);我國上市公司經(jīng)營失敗風險的判定分析——BP神經(jīng)網(wǎng)絡模型和Fisher多類線性判定模型[J];東南學術;2002年02期

4 鄭茂;基于EDF模型的上市公司信用風險實證研究[J];管理工程學報;2005年03期

5 李素紅;陳立文;;基于因子分析法的房地產(chǎn)上市公司財務風險評價[J];河北工業(yè)大學學報;2011年06期

6 韓東平;顏寶銅;;Z計分法和模糊評價法在高校財務預警中應用[J];哈爾濱工業(yè)大學學報;2009年12期

7 過新偉;胡曉;;公司治理、宏觀經(jīng)濟環(huán)境與財務失敗預警研究——離散時間風險模型的應用[J];上海經(jīng)濟研究;2012年05期

8 杜菲;;公司治理結構對公司經(jīng)營績效的影響探析[J];經(jīng)濟研究導刊;2011年21期

9 吳世農(nóng),盧賢義;我國上市公司財務困境的預測模型研究[J];經(jīng)濟研究;2001年06期

10 劉洪,何光軍;基于人工神經(jīng)網(wǎng)絡方法的上市公司經(jīng)營失敗預警研究[J];會計研究;2004年02期

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