本文關(guān)鍵詞： 財(cái)務(wù)危機(jī) 描述性統(tǒng)計(jì) 邏輯回歸 預(yù)測(cè)模型 實(shí)證研究　出處：《西南財(cái)經(jīng)大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：一、研究背景 為了健全社會(huì)主義市場(chǎng)經(jīng)濟(jì)體制,完善資本市場(chǎng)體系,拓寬公司融資渠道,我國于1990年末相繼成立了上海證券交易所和深圳證券交易所。來自中國證監(jiān)會(huì)網(wǎng)站的數(shù)據(jù)顯示,截止2011年底,境內(nèi)上市公司數(shù)(A、B股)共計(jì)2342家,股票市價(jià)總值214758.1億元,為我國的經(jīng)濟(jì)發(fā)展做出了重要貢獻(xiàn)。 同時(shí),為了促使社會(huì)資源的優(yōu)化配置,每個(gè)市場(chǎng)主體都必須接受市場(chǎng)競(jìng)爭“優(yōu)勝劣汰”的法則。自從滬深兩市投入運(yùn)營以來,為了規(guī)范上市公司行為,保障投資者權(quán)益,監(jiān)管部門和交易所制定了一系列的法律法規(guī)和規(guī)章制度,對(duì)財(cái)務(wù)狀況已經(jīng)出現(xiàn)異常的上市公司股票實(shí)行通報(bào)警示。這些由于各種原因經(jīng)營不善的上市公司,面臨退市風(fēng)險(xiǎn),給利益相關(guān)人帶來損失。 因此,對(duì)上市公司的財(cái)務(wù)健康狀況進(jìn)行有效的評(píng)估,科學(xué)地預(yù)測(cè)上市公司面臨財(cái)務(wù)危機(jī)的可能性,具有現(xiàn)實(shí)意義。財(cái)務(wù)危機(jī)預(yù)測(cè)模型為不同的利益相關(guān)人提供相應(yīng)的決策依據(jù)：有利于公司的管理者查缺補(bǔ)漏,防止公司陷入財(cái)務(wù)危機(jī);有利于公司的債權(quán)人對(duì)公司的信用進(jìn)行有效評(píng)估,防范信貸風(fēng)險(xiǎn)；有利于投資者做出理性投資決策,規(guī)避投資風(fēng)險(xiǎn)。 二、本文結(jié)構(gòu)及觀點(diǎn) 全文分為五章,內(nèi)容及觀點(diǎn)如下： 第一章是緒論。闡述了研究的背景和意義,回顧了國內(nèi)外對(duì)財(cái)務(wù)危機(jī)定義的探討,確定了本文實(shí)證研究的財(cái)務(wù)危機(jī)上市公司為我國各年首次被ST或*ST的上市公司,并對(duì)國內(nèi)外的財(cái)務(wù)危機(jī)預(yù)測(cè)研究文獻(xiàn)進(jìn)行綜述。 第二章介紹了財(cái)務(wù)危機(jī)預(yù)測(cè)研究的理論與方法。對(duì)公司財(cái)務(wù)狀況的影響因素進(jìn)行歸納,梳理了財(cái)務(wù)危機(jī)預(yù)測(cè)規(guī)范研究的理論之后,著重介紹了財(cái)務(wù)危機(jī)預(yù)測(cè)實(shí)證研究的方法,并作出比較評(píng)價(jià)。 通過對(duì)財(cái)務(wù)危機(jī)預(yù)測(cè)實(shí)證研究方法的歸納總結(jié)來看,自從對(duì)公司財(cái)務(wù)危機(jī)的預(yù)測(cè)引入數(shù)理分析方法以來,各類建模方法層出不窮,主要可分作統(tǒng)計(jì)方法和非統(tǒng)計(jì)方法。就當(dāng)前而言,經(jīng)過眾多研究證明和實(shí)踐檢驗(yàn),且被廣泛接受的統(tǒng)計(jì)方法有多元判別分析和邏輯回歸分析,非統(tǒng)計(jì)方法是人工神經(jīng)網(wǎng)絡(luò)模型。 第三章是我國上市公司財(cái)務(wù)危機(jī)描述性統(tǒng)計(jì)分析。運(yùn)用非財(cái)務(wù)數(shù)據(jù)進(jìn)行描述性統(tǒng)計(jì)分析,定性分析宏觀經(jīng)濟(jì)、行業(yè)差異以及公司治理結(jié)構(gòu)等因素對(duì)上市公司財(cái)務(wù)危機(jī)的影響情況。 本章搜集自1998年實(shí)行上市公司異常狀況警示制度以來,各年首次被實(shí)行ST或*ST的上市公司,定義為“財(cái)務(wù)危機(jī)”公司,搜集這些公司被警示前兩年的數(shù)據(jù)進(jìn)行描述性統(tǒng)計(jì)分析,定性分析宏觀經(jīng)濟(jì)、行業(yè)差異以及公司治理結(jié)構(gòu)等因素對(duì)上市公司財(cái)務(wù)危機(jī)的影響情況。為建立預(yù)測(cè)模型,選擇作為對(duì)照的正常公司樣本時(shí),是否考慮年份、行業(yè)、規(guī)模的對(duì)應(yīng)提供依據(jù),同時(shí)為預(yù)測(cè)模型中非財(cái)務(wù)指標(biāo)的選取提供參考。 第四章是本文的邏輯回歸預(yù)測(cè)模型的實(shí)證研究部分,介紹了實(shí)證研究的數(shù)據(jù)采集來源、樣本選取方法,指標(biāo)的檢驗(yàn)及篩選和模型的構(gòu)建及檢驗(yàn)。由于2004年滬深兩市修訂上市規(guī)則以后,“最近兩年連續(xù)虧損”的將直接被*ST處理。本文選取了2005到2011年七年間233家滬深交易所實(shí)行*ST的上市公司作為財(cái)務(wù)危機(jī)的樣本公司,同時(shí)采用同行業(yè)同期間隨機(jī)選取的原則,取得相同數(shù)量的非ST或*ST的公司為對(duì)照的正常公司。然后把2005到2009五年的公司作為訓(xùn)練組進(jìn)行邏輯回歸建模,而2010年和2011年的作為應(yīng)用組進(jìn)行獨(dú)立樣本測(cè)試。 在預(yù)測(cè)指標(biāo)方面,本文選取了公司償債能力、盈利能力、營運(yùn)能力、現(xiàn)金流量能力、發(fā)展能力、風(fēng)險(xiǎn)水平和公司治理七個(gè)方面的指標(biāo)體系,共36個(gè)指標(biāo)作為初選預(yù)測(cè)變量。先檢驗(yàn)指標(biāo)的正態(tài)性,然后利用非參數(shù)檢驗(yàn)對(duì)指標(biāo)進(jìn)行差異顯著性的檢驗(yàn),對(duì)通過差異性檢驗(yàn)和需進(jìn)一步討論的指標(biāo),再進(jìn)行相關(guān)性度量,看是否需要在建模時(shí)考慮變量間的相互作用。最后在邏輯回歸時(shí)采用逐步回歸法,從而完成指標(biāo)的篩選。 在進(jìn)行邏輯回歸之后得到包含總資產(chǎn)凈利潤率、資產(chǎn)周轉(zhuǎn)率(長期、短期和總資產(chǎn))、現(xiàn)金流量比率和債務(wù)保障率六個(gè)指標(biāo)的預(yù)測(cè)模型。并對(duì)模型進(jìn)行了回代檢驗(yàn)和獨(dú)立樣本驗(yàn)證。從回代檢驗(yàn)來看,模型對(duì)訓(xùn)練組兩類公司總的判別準(zhǔn)確率為76.03%。由于這是對(duì)訓(xùn)練組樣本t-2、t-3、t-4三年的總體數(shù)據(jù)的預(yù)測(cè)結(jié)果,可以說模型具有較好的判別能力。而獨(dú)立樣本的檢驗(yàn)表明,模型在財(cái)務(wù)危機(jī)公司被通報(bào)警示前4年都具有判斷能力,而前3年內(nèi)則有良好的判別能力。 從模型的檢驗(yàn)效果看來,模型在t-2年和t-3年的識(shí)別能力分別為85.7%、83.6%,t-4年為69.4%。從t-4年開始模型對(duì)財(cái)務(wù)危機(jī)公司的識(shí)別能力明顯降低,但仍有微弱預(yù)測(cè)能力,而對(duì)正常公司的識(shí)別能力并不隨預(yù)測(cè)期的前推而降低。 第五章對(duì)本文的研究進(jìn)行了總結(jié),對(duì)后續(xù)研究進(jìn)行展望,并就加強(qiáng)我國公司財(cái)務(wù)危機(jī)預(yù)測(cè)提出了若干建議。 從本文的研究可以得到的結(jié)論是：上市公司公開的財(cái)務(wù)報(bào)表信息能有效的反應(yīng)公司財(cái)務(wù)健康狀況,可以利用一定的財(cái)務(wù)指標(biāo)建立針對(duì)上市公司整體的財(cái)務(wù)危機(jī)預(yù)測(cè)模型；總資產(chǎn)凈利潤率、資產(chǎn)周轉(zhuǎn)率、現(xiàn)金流量比率和債務(wù)保障率幾個(gè)方面對(duì)于公司陷入財(cái)務(wù)危機(jī)的可能性有明顯的指示作用；邏輯回歸預(yù)測(cè)模型從t-2到t-4年都具有預(yù)測(cè)能力。 同時(shí)從本研究得到如下啟示：是否對(duì)上市公司實(shí)行通報(bào)警示主要看其盈利能力；公司出現(xiàn)財(cái)務(wù)危機(jī)的可能性受公司盈利能力、營運(yùn)能力、現(xiàn)金流等方面的綜合影響,要保持公司健康的財(cái)務(wù)狀況,’必須注意公司各方面的運(yùn)行情況。 三、本文的主要?jiǎng)?chuàng)新 本文在前人研究的基礎(chǔ)之上,對(duì)財(cái)務(wù)危機(jī)預(yù)測(cè)的實(shí)證研究進(jìn)行了深入的探討,建立了相應(yīng)的財(cái)務(wù)危機(jī)預(yù)測(cè)模型,在研究中本文在以下幾方面進(jìn)行了重要的修正和創(chuàng)新。 (1)對(duì)我國1998年至2011年各年財(cái)務(wù)危機(jī)公司,運(yùn)用非財(cái)務(wù)數(shù)據(jù)進(jìn)行描述性統(tǒng)計(jì)分析,定性分析宏觀經(jīng)濟(jì)、行業(yè)差異以及公司治理結(jié)構(gòu)等因素對(duì)上市公司財(cái)務(wù)危機(jī)的影響情況。為預(yù)測(cè)模型中,選擇作為對(duì)照的正常公司樣本時(shí),是否考慮年份、行業(yè)、規(guī)模的對(duì)應(yīng)提供依據(jù)。同時(shí)為預(yù)測(cè)模型中非財(cái)務(wù)指標(biāo)的選取提供參考。 (2)在建立財(cái)務(wù)危機(jī)預(yù)測(cè)模型時(shí),財(cái)務(wù)危機(jī)公司樣本的選取,剔除了非財(cái)務(wù)原因被交易所實(shí)行通報(bào)警示的公司,主要考慮到審計(jì)意見否定或信息披露方式不合規(guī)定的公司,其財(cái)務(wù)信息的真實(shí)性值得商榷。 (3)在數(shù)據(jù)選取上,考慮到各年宏觀經(jīng)濟(jì)環(huán)境的差異,與不同年份被首次通報(bào)警示的財(cái)務(wù)危機(jī)公司對(duì)照的正常公司,相關(guān)指標(biāo)數(shù)據(jù)也選擇相同時(shí)期的數(shù)據(jù)。 (4)無論財(cái)務(wù)危機(jī)公司,還是正常公司,都是只在滬深A(yù)股主板上市的公司,避免信息披露要求的差異導(dǎo)致可比性存在問題。 (5)在指標(biāo)差異性檢驗(yàn)上,考慮了T檢驗(yàn)的數(shù)據(jù)正態(tài)性前提。先用K-S方法檢驗(yàn)指標(biāo)的正態(tài)性,然后再選擇適當(dāng)?shù)牟町愶@著性檢驗(yàn)方法。 (6)將總體樣本分作訓(xùn)練組和應(yīng)用組,2005至2009年樣本用來建立預(yù)測(cè)模型,2010至2011年樣本用來測(cè)試預(yù)測(cè)模型的有用性,獨(dú)立樣本測(cè)試更具說服力。
[Abstract]:First, research background
In order to improve the socialist market economic system, improve the capital market system, broaden the financing channels for the company in China at the end of 1990 have been established in Shanghai stock exchange and Shenzhen stock exchange. China from the SFC website data show that as of the end of 2011, the number of domestic listed companies (A, b) a total of 2342, the stock market value of 21 trillion and 475 billion 810 million yuan that has made an important contribution to the economic development of our country.
At the same time, in order to promote the optimal allocation of social resources, each main market must accept the market competition of "survival of the fittest" principle. Since the Shanghai and Shenzhen two put into operation, in order to regulate the behavior of listed companies, protect the rights and interests of investors, regulators and exchanges has formulated a series of laws and regulations, the financial situation has been abnormal the shares of listed companies to implement notification alerts. Due to various reasons these companies face delisting risk, cause losses to the stakeholders.
Therefore, to effectively evaluate the financial health of listed companies, the possibility of scientific prediction of listed companies facing financial crisis, is of practical significance. The financial crisis forecasting model to provide the appropriate decision-making basis for various stakeholders: help the company managers check network, to prevent financial crises in favor of the company; the creditors to evaluate the company's credit, to prevent credit risks; help investors to make rational investment decisions, avoid investment risks.
Two, the structure and view of this article
The full text is divided into five chapters, and the contents and views are as follows:
The first chapter introduces the research background and significance, reviews the study on the definition of financial crisis at home and abroad, the empirical research of financial crisis of listed companies of our country every year for the first time by the ST or *ST of the listed companies, and the domestic and foreign financial crisis prediction research literature are reviewed.
The second chapter introduces the theory and method of financial crisis prediction, summarizes the factors that influence the company's financial status, and combs the theory of financial crisis prediction, then focuses on the methods of empirical research on financial crisis prediction, and makes a comparative evaluation.
Through summarizing the empirical research methods to predict the financial crisis of the induction, since the prediction of financial distress by mathematical analysis method, various kinds of modeling methods mainly emerge in an endless stream, can be divided into statistical and non statistical methods. At present, many research proof and practical test, statistical methods and widely accepted with discriminant analysis logistic regression analysis and multivariate, non statistical method is the artificial neural network model.
The third chapter is descriptive statistics analysis of financial crisis in Chinese listed companies. Descriptive statistics analysis is conducted using non-financial data, and qualitative analysis is made on the impact of macroeconomic factors, industry differences and corporate governance structure on the financial crisis of listed companies.
Since this chapter collected since 1998 the implementation of the listed company abnormal warning system, the first implementation of ST or *ST of the listed companies, defined as "financial crisis", these companies have been collected two years ago warning data descriptive statistical analysis, qualitative analysis of the macroeconomic situation, industry differences and influencing factors of corporate governance structure the financial crisis of the listed companies. In order to establish the prediction model, selected as the normal control samples of the company, whether to consider the year industry, provide the basis for the corresponding scale, and to provide reference for the selection of non-financial index prediction model.
The fourth chapter is the empirical research part logistic regression prediction model, this paper introduces a data collection source of empirical research, sample selection, construction and inspection index inspection and screening and model. After the 2004 amendments to the Shanghai and Shenzhen two city listing rules, the last two years of continuous losses will be directly handled by *ST. This paper selects 2005 to seven years in 2011 233 in Shanghai and Shenzhen Stock Exchange to implement *ST listed companies as the financial crisis of the Sample Firms, the industry in the same period were randomly selected to obtain the principle of the same number of non ST or *ST for normal control. Then the company from 2005 to 2009 in five years as the training group for logistic regression modeling in 2010 and 2011, and as the application group of independent sample test.
In the forecast indexes, this paper selected the company's solvency, profitability, operation ability and cash flow ability, development ability, risk level and corporate governance index system of seven aspects, a total of 36 indicators as the primary predictor variables. To test the normality index, and then test the significant difference of the index the use of non parametric test, through difference test and discuss the index, then the correlation metric, see the need to consider whether the interaction between the variables in the model. Finally, using stepwise regression method in logistic regression, and completed the selection of indicators.
Include net profit rate of total assets after logistic regression, asset turnover (long-term, short-term and total assets), prediction model of cash flow ratio and debt guarantee rate of six indicators. And the model of the back substitution test and independent sample test. From the regression test, discriminant model of total training group of two companies the accuracy of 76.03%. because it is on the training samples T-2, T-3, the prediction results T-4 three years of data, can be said that the model has good distinguishing ability. And the test of independent samples showed that the model was informed in the financial crisis warning 4 years ago has the ability to judge, and the first 3 years there was good discrimination capabilities.
From the point of view of the model test results, model in T-2 and T-3 recognition ability were 85.7%, 83.6%, T-4 69.4%. T-4 years from the beginning of the model of the recognition of the companies in financial crisis decreased significantly, but there is still a weak predictive power, while the normal company recognition ability is not with the forecast period before push reduced.
The fifth chapter summarizes the research of this paper, looks forward to the follow-up research, and puts forward some suggestions on strengthening the financial crisis prediction in our country.
Can be obtained from the conclusions of this study are: public financial report of the listed company information can be healthy and effective corporate financial situation reaction can use financial indicators, the establishment of the listed company's financial crisis prediction model; net profit rate of total assets, asset turnover ratio, cash flow ratio and debt guarantee rate aspects significant role for the possibility of financial crisis; logistic regression prediction model has predictive power from T-2 to T-4.
At the same time get the inspiration from the study: whether to implement the notification warning mainly depends on the profitability of listed companies; profitability, the possibility of company financial crisis by the company operating capacity, cash flow and other aspects of the comprehensive effect, to maintain a healthy financial condition of the company, "must pay attention to the operation of each aspect of the company.
Three, the main innovation of this article
Based on previous studies, this paper has made an in-depth discussion on the empirical research of financial crisis prediction, and established a corresponding financial crisis prediction model. In this study, the following important amendments and innovations were carried out in the following aspects.
(1) in China from 1998 to 2011 each year, the financial crisis, using financial data descriptive statistical analysis, qualitative analysis of the macroeconomic situation, industry differences and corporate governance structure and other factors on the financial crisis of the listed company. As the prediction model, selected as a normal company control sample, consider whether the year the industry, provide the basis for the corresponding scale. At the same time provide a reference for the selection of non-financial index prediction model.
(2) in the model of financial crisis, the financial crisis companies selected sample, excluding the non financial reasons by the exchange to implement reporting warning of the company, taking into account the main negative audit opinion and information disclosure irregularities, the authenticity of the questionable financial information.
(3) in data selection, considering the difference of macro economic environment in different years, the relevant index data of normal financial companies with different warning dates were also selected in the same period.
(4) whether financial crisis companies or normal companies are listed on the main board of Shanghai and Shenzhen A shares, the difference between them will lead to comparable problems.
(5) in terms of index difference test, we consider the normality of data in T test. First we use K-S to test normality of index, then choose the appropriate difference significance test.
(6) the total samples were divided into training group and application group. From 2005 to 2009, samples were used to establish prediction models. From 2011 to 2011, samples were used to test the usefulness of prediction models, and the independent sample test was more convincing.

【學(xué)位授予單位】：西南財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：F275;F832.51;F224

【參考文獻(xiàn)】

中國期刊全文數(shù)據(jù)庫 前10條

1 張鳴,張艷;財(cái)務(wù)困境預(yù)測(cè)的實(shí)證研究與評(píng)述[J];財(cái)經(jīng)研究;2001年12期

2 楊華;;財(cái)務(wù)危機(jī)預(yù)警模型中的非財(cái)務(wù)指標(biāo)應(yīng)用研究[J];財(cái)會(huì)通訊(綜合版);2007年05期

3 楊軼;張友棠;;企業(yè)財(cái)務(wù)預(yù)警國內(nèi)外研究述評(píng)[J];財(cái)會(huì)月刊;2008年20期

4 鄧曉嵐,王宗軍,李紅俠,熊銀平;企業(yè)經(jīng)營失敗評(píng)價(jià)方法研究進(jìn)展述評(píng)[J];系統(tǒng)工程;2005年08期

5 宋鵬;張信東;;基于Logistic模型的上市公司財(cái)務(wù)危機(jī)預(yù)警研究[J];經(jīng)濟(jì)問題;2009年08期

6 吳世農(nóng),盧賢義;我國上市公司財(cái)務(wù)困境的預(yù)測(cè)模型研究[J];經(jīng)濟(jì)研究;2001年06期

7 郭兆;;上市公司財(cái)務(wù)危機(jī)預(yù)警Logistic模型實(shí)證分析[J];金融經(jīng)濟(jì);2009年12期

8 陳靜;上市公司財(cái)務(wù)惡化預(yù)測(cè)的實(shí)證分析[J];會(huì)計(jì)研究;1999年04期

9 李玲;譚滔;;企業(yè)財(cái)務(wù)危機(jī)預(yù)警模型構(gòu)建的理論解析[J];科技與管理;2008年01期

10 宋秋萍;開展財(cái)務(wù)預(yù)警分析,增強(qiáng)經(jīng)營者憂患意識(shí)[J];生產(chǎn)力研究;2000年Z1期

中國博士學(xué)位論文全文數(shù)據(jù)庫 前2條

1 孫潔;企業(yè)財(cái)務(wù)危機(jī)預(yù)警的智能決策方法研究[D];哈爾濱工業(yè)大學(xué);2007年

2 劉彥文;上市公司財(cái)務(wù)危機(jī)預(yù)警模型研究[D];大連理工大學(xué);2009年

中國碩士學(xué)位論文全文數(shù)據(jù)庫 前1條

1 盧賢義;我國上市公司財(cái)務(wù)困境預(yù)測(cè)的實(shí)證研究[D];廈門大學(xué);2001年

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