本文關(guān)鍵詞：基于KMV模型的中小企業(yè)板上市公司財(cái)務(wù)預(yù)警研究　出處：《青島理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 財(cái)務(wù)預(yù)警 KMV模型 違約距離 主成分分析 Logistic回歸

【摘要】：我國已步入后金融危機(jī)時(shí)代，，受到2010年歐債危機(jī)的影響后，以出口為主的很多中小企業(yè)紛紛倒閉，出口受到阻礙，銀行的信貸違約案例增加，對金融業(yè)造成沖擊。中小板上市公司成立的時(shí)間短，規(guī)模較小，導(dǎo)致業(yè)績評價(jià)困難，更易出現(xiàn)如操縱市場、內(nèi)幕交易等行為，爆發(fā)財(cái)務(wù)危機(jī)的可能性也更大。因此需要建立積極有效的財(cái)務(wù)預(yù)警模型系統(tǒng)，用以合理避免企業(yè)的財(cái)務(wù)危機(jī)，保護(hù)投資者的權(quán)益，維持資本市場的穩(wěn)定，尤其是能夠解決目前中小板上市公司普遍存在的銀行不良資產(chǎn)率高的問題。 目前我國對財(cái)務(wù)預(yù)警模型領(lǐng)域的研究依賴傳統(tǒng)的財(cái)務(wù)指標(biāo)，因?yàn)槲覈倪`約數(shù)據(jù)庫系統(tǒng)尚未建立，部分的國內(nèi)學(xué)者試圖研究將KMV模型引入至預(yù)警模型，但是這方面的研究大多限于在我國KMV模型能否有效適用方面，實(shí)際上信用風(fēng)險(xiǎn)度量層面的最新研究成果尚未真正與我國企業(yè)的財(cái)務(wù)預(yù)警模型相結(jié)合。本文在前人研究的基礎(chǔ)上，將傳統(tǒng)的財(cái)務(wù)指標(biāo)預(yù)警模型與信用風(fēng)險(xiǎn)度量模型相結(jié)合，引入KMV的違約距離與預(yù)期違約率指標(biāo)，以期提高財(cái)務(wù)預(yù)警模型的預(yù)測精度。 本文在闡述Logistic邏輯回歸模型及KMV模型的基礎(chǔ)上，把KMV模型中的違約距離（DD）引入到Logistic模型，建立一套新的財(cái)務(wù)預(yù)警模型，以時(shí)間因素為縱向，比較加入違約距離前后兩種模型在預(yù)測的準(zhǔn)確性的區(qū)別，考察違約距離對財(cái)務(wù)預(yù)警模型預(yù)測和解釋能力的影響，再分析違約距離變量對模型2的影響。 首先根據(jù)我國資本市場的實(shí)際情況對KMV模型的參數(shù)修正：一是股東權(quán)益市場價(jià)值,我國存在股權(quán)分置現(xiàn)象，除了流通股外還存在非流通股，非流通股大多處于股權(quán)分置改革的限售期。非流通股的定價(jià)依據(jù)凈資產(chǎn)定價(jià)法；二是違約點(diǎn)DP，當(dāng)公司的資產(chǎn)價(jià)值低于某一臨界值時(shí)，對債權(quán)人和公司股東會出現(xiàn)違約，與這一臨界值相應(yīng)的資產(chǎn)價(jià)值稱為違約點(diǎn)DP。 其次對KMV模型的輸出結(jié)果違約距離及依據(jù)違約距離計(jì)算得出的預(yù)期違約率做相關(guān)性分析和顯著性檢驗(yàn)。經(jīng)過相關(guān)性分析，DD和EDF呈負(fù)相關(guān)關(guān)系，而顯著性檢驗(yàn)說明DD和EDF在0.05的顯著性水平下均存在顯著性差異。 再次對22個(gè)基礎(chǔ)指標(biāo)進(jìn)行篩選。本文將通過正態(tài)性檢驗(yàn)及顯著性檢驗(yàn)篩選出能夠?qū)δＰ陀休^好代表的自變量，再通過因子分析剔除具有多重共線性的自變量，選出更具有代表性的變量。 最后構(gòu)建回歸模型�；谏鲜龅难芯浚肔ogistic回歸模型針對2010年和2011年分別構(gòu)建基于財(cái)務(wù)指標(biāo)的預(yù)警模型和引入違約距離的預(yù)警模型，通過對兩種判別模型在橫向和縱向的對比分析，得出結(jié)論：引入KMV模型可以提高財(cái)務(wù)預(yù)警模型的解釋和判別能力，提高預(yù)測的準(zhǔn)確性；經(jīng)檢驗(yàn)新的預(yù)警模型對中小企業(yè)板上市公司危機(jī)預(yù)警效果良好。
[Abstract]:Our country has entered the post-financial crisis era, by the European debt crisis in 2010, many small and medium-sized enterprises mainly export have closed down, export has been hindered, the bank credit default cases increased. The small and medium board listed companies have short time and small scale, which lead to the difficulty of performance evaluation, such as market manipulation, insider trading and so on. Therefore, it is necessary to establish an active and effective financial early-warning model system in order to reasonably avoid the financial crisis of enterprises, protect the rights and interests of investors, and maintain the stability of the capital market. In particular, it can solve the problem of high non-performing assets in banks. At present, the research on financial early-warning model in China depends on the traditional financial indicators, because the default database system has not been established in China. Some domestic scholars try to introduce KMV model into the early-warning model. However, most of the research in this area is limited to whether the KMV model can be effectively applied in our country. In fact, the latest research results in the level of credit risk measurement have not been really combined with the financial early-warning model of Chinese enterprises. Combining the traditional financial index early warning model with the credit risk measurement model, this paper introduces the default distance and the expected default rate index of KMV in order to improve the prediction accuracy of the financial early warning model. Based on the description of Logistic logical regression model and KMV model, this paper introduces the default distance (DDD) of KMV model into Logistic model. A new financial early warning model is established to compare the prediction accuracy of the two models before and after adding the default distance, taking the time factor as the vertical. The influence of default distance on forecasting and explaining ability of financial early warning model is investigated, and the influence of default distance variable on model 2 is analyzed. First, according to the actual situation of the capital market in China, the parameters of the KMV model are revised: first, the market value of shareholders' rights and interests, there is the phenomenon of split share structure in China, in addition to circulating shares, there are also non-tradable shares. Most of the non-tradable shares are in the restricted period of the split share structure reform. The pricing of non-tradable shares is based on the net assets pricing method. The other is the default point DP.When the company's asset value is below a certain critical value, it will default on creditors and shareholders. The corresponding asset value corresponding to this critical value is called default point DP. Secondly, the correlation analysis and significance test of the default distance between the output of KMV model and the expected default rate calculated according to the default distance are made. Through the correlation analysis, DD and EDF are negatively correlated. The significance test showed that there were significant differences between DD and EDF at the significant level of 0. 05. Again, 22 basic indexes were screened. In this paper, the independent variables which can represent the model were screened by normal test and significance test. Then factor analysis is used to eliminate the independent variables with multiple collinearity, and the more representative variables are selected. Finally, the regression model is constructed. Based on the above research. The Logistic regression model is used to build the early warning model based on financial indicators and the early warning model based on default distance for 2010 and 2011 respectively. Through the comparative analysis of the two discriminant models in the horizontal and vertical, it is concluded that the introduction of KMV model can improve the interpretation and discriminant ability of the financial early-warning model and improve the accuracy of prediction; The new warning model is proved to be effective for the crisis warning of SMEs listed companies.
【學(xué)位授予單位】：青島理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F275;F832.51

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