本文關(guān)鍵詞： 財(cái)務(wù)困境 退市制度 財(cái)務(wù)指標(biāo) 非財(cái)務(wù)指標(biāo)　出處：《河北大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：新一輪的國(guó)際金融危機(jī)的陰影并沒完全褪去，2013年中國(guó)優(yōu)秀的企業(yè)的數(shù)量正在減少，更不容樂觀的是面臨巨大風(fēng)險(xiǎn)的企業(yè)數(shù)量卻在加大。鑒于我國(guó)正在實(shí)行更加嚴(yán)苛的上市制度，可以預(yù)見我國(guó)將要退市的上市公司的數(shù)量也會(huì)越來越多，能提前對(duì)退市公司進(jìn)行財(cái)務(wù)預(yù)警于廣大利益相關(guān)者來說無疑具有重大意義。 以往國(guó)內(nèi)的公司財(cái)務(wù)困境預(yù)警研究主要基于財(cái)務(wù)指標(biāo)，以ST作為公司陷入財(cái)務(wù)困境的界限來預(yù)測(cè)企業(yè)的財(cái)務(wù)狀況。然而，僅僅以財(cái)務(wù)指標(biāo)分析企業(yè)財(cái)務(wù)困境的原因并不全面和深入，為了避免此缺陷，，本文將非財(cái)務(wù)指標(biāo)引入財(cái)務(wù)困境預(yù)警體系。以ST作為公司陷入財(cái)務(wù)困境的標(biāo)準(zhǔn)有一定的可取性，但是與國(guó)外的以破產(chǎn)作為公司陷入財(cái)務(wù)困境的標(biāo)準(zhǔn)還具有很大的差異，ST公司并不一定會(huì)破產(chǎn)。本文將公司退市作為深層次的財(cái)務(wù)困境界限，探討上市公司退市財(cái)務(wù)風(fēng)險(xiǎn)預(yù)警。通過借鑒前人的研究成果，本文利用實(shí)證研究的方法對(duì)我國(guó)上市公司退市風(fēng)險(xiǎn)進(jìn)行預(yù)測(cè)研究，期望對(duì)我國(guó)上市公司退市風(fēng)險(xiǎn)預(yù)警研究提供參考價(jià)值。 本文以退市的33家制造業(yè)上市公司和與之配對(duì)的未退市公司為研究樣本，選取了30個(gè)財(cái)務(wù)指標(biāo)和7個(gè)非財(cái)務(wù)指標(biāo)，運(yùn)用非參數(shù)檢驗(yàn)、相關(guān)性檢驗(yàn)等分析方法，最終確定公司退市前5年到2年預(yù)警模型選取的指標(biāo)。本文采用Logistic回歸方法，根據(jù)不同期間和指標(biāo)，建立了8個(gè)模型，包括基于財(cái)務(wù)指標(biāo)的財(cái)務(wù)模型和財(cái)務(wù)指標(biāo)與非財(cái)務(wù)指標(biāo)結(jié)合的綜合模型。最后對(duì)比分析了這幾種模型的有效性。 研究結(jié)果顯示，財(cái)務(wù)指標(biāo)和非財(cái)務(wù)指標(biāo)對(duì)上市公司是否退市都具有重要影響，引入非財(cái)務(wù)指標(biāo)能增加預(yù)警模型的預(yù)測(cè)能力。同時(shí)可以看出，退市公司和未退市公司之間表現(xiàn)出了一定差異性，可以通過退市風(fēng)險(xiǎn)預(yù)警模型來補(bǔ)充我國(guó)的退市制度。
[Abstract]:The shadow of the new round of international financial crisis has not completely faded. The number of outstanding enterprises in China is decreasing in 2013. What is more, the number of enterprises facing enormous risks is increasing. Given that China is implementing a more stringent listing system, It can be predicted that the number of listed companies will be more and more, and it is undoubtedly of great significance for the majority of stakeholders to be able to advance the financial early warning of delisting companies. In the past, the financial distress early warning research in China was mainly based on financial indicators, and St was used as the limit of financial distress to predict the financial situation of enterprises. In order to avoid this defect, this paper introduces non-financial indicators into the financial distress warning system. It is desirable to take St as the standard of financial distress. However, there is still a great difference from the foreign standard of taking bankruptcy as the standard of financial distress. St companies are not bound to go bankrupt. In this paper, the delisting of the company is regarded as a deep financial distress boundary. This paper discusses the early warning of financial risk of delisting of listed companies. By referring to the previous research results, this paper uses the method of empirical research to predict the risks of delisting of listed companies in China. It is expected to provide reference value for the study of delisting risk early warning of listed companies in China. Based on 33 listed manufacturing companies and matched undelisted companies, 30 financial indexes and 7 non-financial indexes were selected, and non-parametric test and correlation test were used in this paper. In this paper, Logistic regression method is used to establish 8 models according to different periods and indicators. It includes the financial model based on financial index and the comprehensive model combining financial index and non-financial index. Finally, the validity of these models is compared and analyzed. The results show that both financial indicators and non-financial indicators have an important impact on the delisting of listed companies, and the introduction of non-financial indicators can increase the forecasting ability of the early warning model. There are some differences between delisting companies and non-delisting companies, and the delisting system can be supplemented by delisting risk warning model.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F832.51;F275

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