發(fā)布時間：2018-03-14 13:47

  本文選題：熵權(quán)法　切入點：面板數(shù)據(jù)　出處：《太原科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：上市公司陷入財務(wù)困境不僅威脅到公司自身的生存發(fā)展，而且為債權(quán)人和投資者帶來巨大的損失。為了減少債權(quán)人和投資者的經(jīng)濟損失并且使上市公司能夠健康發(fā)展，需要建立能反映上市公司各方面風(fēng)險的財務(wù)困境預(yù)警模型，有效地預(yù)測企業(yè)經(jīng)營狀況，以防范企業(yè)陷入財務(wù)困境，這對于提高企業(yè)經(jīng)營管理質(zhì)量，保護相關(guān)利益者利益以及促進我國資本市場的良性發(fā)展具有重要意義。 關(guān)于上市公司財務(wù)預(yù)警的研究，國內(nèi)外學(xué)者都做了許多工作，但以往的研究大都基于不同方法以單期截面數(shù)據(jù)預(yù)測公司是否陷入財務(wù)困境，屬于靜態(tài)預(yù)測研究。企業(yè)的財務(wù)狀況具有持續(xù)性和累積效應(yīng)，公司陷入財務(wù)困境是有一個逐漸演變的過程，并且企業(yè)財務(wù)狀況暫時偏離正常值不應(yīng)被歸為困境公司。為了盡可能全面客觀的反應(yīng)企業(yè)財務(wù)狀況，，本文從財務(wù)和非財務(wù)兩個維度進行預(yù)警指標(biāo)選取，采用能有效處理面板數(shù)據(jù)的PanelLogit模型對我國上市公司的財務(wù)困境進行預(yù)警研究，彌補了傳統(tǒng)截面數(shù)據(jù)研究不能動態(tài)反應(yīng)企業(yè)財務(wù)狀況演變的不足，較客觀地反映公司財務(wù)狀況發(fā)展的動態(tài)事實，使預(yù)警模型更具有實際意義。 本文以滬深A(yù)股制造業(yè)上市公司作為研究對象，首先選取2011-2013年首次被特殊處理（ST）的30家上市公司作為財務(wù)困境的公司樣本，按照1:2的比例選取相同時間段從未被ST過的60家制造業(yè)上市公司作為財務(wù)正常公司樣本，并初步選取了28個財務(wù)指標(biāo)和10個非財務(wù)指標(biāo)作為預(yù)警指標(biāo)；其次，利用熵權(quán)法對38個初選指標(biāo)進行分析，根據(jù)每個初選指標(biāo)所提供的影響財務(wù)困境預(yù)警模型的信息量的不同，最終選取21個財務(wù)指標(biāo)和8個非財務(wù)指標(biāo)；第三，對篩選的29個變量指標(biāo)進行因子分析，消除變量指標(biāo)多重共線性對預(yù)警模型估計的影響，提取出對企業(yè)財務(wù)狀況具有重要影響的7個財務(wù)公共因子和3個非財務(wù)公共因子，并將7個財務(wù)公共因子作為僅基于財務(wù)指標(biāo)的PanelLogit財務(wù)困境預(yù)警模型的解釋變量，將7個財務(wù)公共因子和3個非財務(wù)公共因子作為財務(wù)指標(biāo)與非財務(wù)指標(biāo)相結(jié)合的Panel Logit財務(wù)困境預(yù)警模型的解釋變量；第四，運用Hausman檢驗，確定采用隨機效應(yīng)的Panel Logit回歸模型。僅基于財務(wù)指標(biāo)建立的Panel Logit財務(wù)困境預(yù)警模型實證結(jié)果表明，償債因子、盈利因子、資本運用因子、資本結(jié)構(gòu)因子對企業(yè)陷入財務(wù)困境有重要影響，財務(wù)指標(biāo)與非財務(wù)指標(biāo)相結(jié)合建立的Panel Logit財務(wù)困境預(yù)警模型實證結(jié)果表明償債因子、盈利因子、資本運用因子、資本結(jié)構(gòu)因子、股權(quán)集中度因子、評價因子均是影響企業(yè)陷入財務(wù)困境的重要因素；最后，本文對已建立的財務(wù)困境預(yù)警模型做了樣本外預(yù)測檢驗，預(yù)測結(jié)果表明正確率分別達(dá)到86.67%和91.11%，并將在財務(wù)困境預(yù)警模型中加入非財務(wù)指標(biāo)和僅包含財務(wù)指標(biāo)的財務(wù)困境預(yù)警模型的預(yù)測效果做了對比，結(jié)果表明，引入非財務(wù)指標(biāo)有助于提高財務(wù)困境預(yù)警模型的預(yù)測能力。
[Abstract]:Listed companies in financial distress is not only a threat to the company's own survival and development, but also bring huge losses to creditors and investors. In order to reduce the economic losses of creditors and investors and listed companies to make a healthy development, need to establish the financial early-warning model can reflect all aspects of the risk of listed companies effectively predict the condition of business, in order to prevent the financial crisis of enterprises, to improve the quality of enterprise management, to protect the interests of stakeholders and promote the healthy development of the capital market of our country has important significance.
Research on financial early warning of listed companies, domestic and foreign scholars have done a lot of work, but most of the previous studies are based on different methods to single section data of Forecast Ltd into financial distress, belongs to the static prediction. The financial situation of enterprises is persistent and cumulative effect, the financial distress is a gradual evolution the financial situation of enterprises, and temporarily deviate from the normal should not be classified as distressed companies. In order to reflect the financial situation of enterprises comprehensively and objectively as possible, the early warning index is selected from the two dimensions of financial and non-financial, using the PanelLogit model can effectively deal with the panel data to study the early warning of financial distress in China's listed companies. To compensate for the lack of development of the financial situation of the traditional research on dynamic response of enterprises cannot cross section data, objectively reflect the dynamic development of the company's financial situation In fact, the early warning model is more practical.
In this paper, the Shanghai and Shenzhen A share listed companies in manufacturing industry as the research object, firstly selected for the first time in 2011-2013 years by the special treatment (ST) of the 30 listed companies as the financial distress of listed companies, 60 manufacturing according to the ratio of 1:2 to select the same time period has never been ST listed companies as the normal financial company and the preliminary sample. We selected 28 Financial Indicators and 10 non-financial indicators as early warning indicators; second, the 38 primary indexes were analyzed by using entropy method, according to the different amount of information in financial distress prediction effect of each primary index provided by the selected 21 Financial indicators and 8 non-financial indicators; third, to the factor analysis of 29 variables selection, eliminate variables multicollinearity of early-warning model estimation, extract has an important influence on the financial situation of the 7 factors and 3 Public Finance Non financial factors, and the 7 factors as the only public financial PanelLogit financial early-warning model of financial indicators of the explanatory variables based on the 7 financial factors and 3 non-financial public factors as the Panel Logit financial early-warning model of financial indicators and non-financial indicators combined with the explanatory variables; fourth, the use of Hausman test, determined using random effects Panel regression model Logit. Only Panel Logit financial early-warning model of financial indicators to establish the empirical results show that based on the solvency factor, profit factor, capital factor, capital structure factor has an important influence on the enterprise into financial distress, financial indicators and non-financial indicators combined with the Panel Logit financial distress the empirical results show that the model established solvency factor, profit factor, capital factor, capital structure, ownership concentration factor, evaluation Factors are important factors for financial distress; finally, the sample for prediction of financial distress prediction model has been established, the prediction results show that the correct rate of 86.67% and 91.11% respectively, and in the early warning model of financial distress prediction effect add non-financial refers to the standard and contains only the financial early-warning model of financial the indexes were compared. The results show that the introduction of non-financial indicators can help improve the ability to predict the financial distress prediction model.

【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F276.6;F275

【參考文獻】

相關(guān)期刊論文 前10條

1 周劍濤;龐文鳳;;Z-SCORE財務(wù)預(yù)警模型在上市公司應(yīng)用的實證研究[J];財會通訊;2009年24期

2 周娟;王麗娟;;基于EVA的財務(wù)危機預(yù)警模型應(yīng)用[J];財會月刊;2007年08期

3 康曉玲;張懿;;企業(yè)短期財務(wù)預(yù)警模型研究[J];財會月刊;2008年29期

4 劉澄;胡巧紅;孫瑩;;基于粗糙集—決策樹的上市公司財務(wù)預(yù)警[J];財會月刊;2012年18期

5 戴紅軍;吳國強;;公司治理、外部審計信息與財務(wù)危機預(yù)警[J];大連民族學(xué)院學(xué)報;2010年02期

6 錢光明;陳德艷;;非財務(wù)指標(biāo)對我國上市公司財務(wù)預(yù)警的影響[J];遼寧石油化工大學(xué)學(xué)報;2010年02期

7 陳啟明;陳華友;;基于層次分析和灰色模糊評價法的企業(yè)財務(wù)危機預(yù)警度研究[J];工業(yè)技術(shù)經(jīng)濟;2011年03期

8 孫潔;李輝;韓建光;;基于滾動時間窗口支持向量機的財務(wù)困境預(yù)測動態(tài)建模[J];管理工程學(xué)報;2010年04期

9 萬希寧;王艷;;基于非財務(wù)指標(biāo)的企業(yè)財務(wù)危機模糊預(yù)警模型研究[J];管理學(xué)報;2007年02期

10 郭德仁;王培輝;;基于模糊聚類和模糊模式識別的企業(yè)財務(wù)預(yù)警[J];管理學(xué)報;2009年09期

相關(guān)博士學(xué)位論文 前1條

1 劉彥文;上市公司財務(wù)危機預(yù)警模型研究[D];大連理工大學(xué);2009年

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