本文選題：財(cái)務(wù)困境　切入點(diǎn)：預(yù)測(cè)模型　出處：《首都經(jīng)濟(jì)貿(mào)易大學(xué)》2012年碩士論文

【摘要】：企業(yè)陷入財(cái)務(wù)困境，不僅會(huì)給企業(yè)投資者、債權(quán)人以及其他企業(yè)相關(guān)利益者帶來(lái)經(jīng)濟(jì)損失，而且會(huì)影響社會(huì)穩(wěn)定。找到上市公司陷入財(cái)務(wù)困境的原因，構(gòu)造適合中國(guó)上市公司財(cái)務(wù)困境的預(yù)測(cè)模型，及時(shí)獲得上市公司財(cái)務(wù)狀況出現(xiàn)嚴(yán)重惡化的預(yù)警信號(hào)，不論對(duì)投資者、債權(quán)人、經(jīng)營(yíng)者還是監(jiān)管者，，都具有重大意義。 在以往對(duì)于財(cái)務(wù)困境預(yù)測(cè)的研究中，Logistic回歸是主流的統(tǒng)計(jì)模型之一。然而，Logistic回歸中隱含的模型系數(shù)固定不變假設(shè)可能與事實(shí)相悖，有待于統(tǒng)計(jì)檢驗(yàn)。與此同時(shí)，企業(yè)所在行業(yè)屬性在財(cái)務(wù)困境預(yù)測(cè)模型中經(jīng)常被忽略或回避。因此，有必要將行業(yè)屬性納入預(yù)測(cè)模型，并驗(yàn)證企業(yè)行業(yè)屬性對(duì)回歸系數(shù)變動(dòng)的解釋力。因此，本文選擇分層Logistic模型進(jìn)行預(yù)測(cè)模型的建立，該模型既能適用于離散型響應(yīng)變量，又能處理分層數(shù)據(jù)結(jié)構(gòu)的統(tǒng)計(jì)模型，能夠?qū)⒑暧^變量即行業(yè)信息引入模型，并對(duì)其效應(yīng)檢驗(yàn)。 本文首先對(duì)上市公司財(cái)務(wù)困境預(yù)測(cè)問(wèn)題進(jìn)行理論研究，在此基礎(chǔ)上給出了基于流動(dòng)比率的財(cái)務(wù)困境定義方法；之后進(jìn)行樣本選擇和指標(biāo)選取，選取2010年我國(guó)滬、深股市的A股上市公司作為研究總樣本，得到樣本公司1249家，其中財(cái)務(wù)困境公司272家，選擇財(cái)務(wù)指標(biāo)和非財(cái)務(wù)指標(biāo)作為預(yù)測(cè)變量；而后建立多層Logistic模型，并對(duì)回歸系數(shù)固定效應(yīng)和隨機(jī)效應(yīng)檢驗(yàn)。本研究的數(shù)據(jù)來(lái)自于國(guó)泰安CSMAR數(shù)據(jù)庫(kù)。 基于分層Logistic模型的財(cái)務(wù)困境預(yù)測(cè)模型顯示，微觀層次回歸系數(shù)的變動(dòng)情況在組間（跨行業(yè)）存在，且該變動(dòng)可分解為兩個(gè)方面，一部分可由宏觀層次解釋變量解釋，一部分由微觀層次變量回歸系數(shù)的隨機(jī)斜率解釋。同時(shí)，在財(cái)務(wù)管理意義上，模型在宏觀層次支持了行業(yè)前景水平（由行業(yè)平均營(yíng)業(yè)收入增長(zhǎng)率表示）對(duì)企業(yè)財(cái)務(wù)困境風(fēng)險(xiǎn)的影響，在微觀層次支持了流動(dòng)比率、流動(dòng)負(fù)債比率、資產(chǎn)報(bào)酬率、流動(dòng)資產(chǎn)周轉(zhuǎn)率和審計(jì)意見(jiàn)對(duì)企業(yè)財(cái)務(wù)困境風(fēng)險(xiǎn)的影響。除資產(chǎn)報(bào)酬率為隨機(jī)效應(yīng)外，其余均為固定效應(yīng)�；谀Ｐ偷臄M合效果，多層Logistic模型對(duì)于數(shù)據(jù)擬合情況良好，適用于上市公司財(cái)務(wù)困境預(yù)測(cè)問(wèn)題。
[Abstract]:Financial distress will not only bring economic losses to investors, creditors and other stakeholders of the enterprise, but also affect social stability. Find out why listed companies are in financial distress. It is of great significance for investors, creditors, managers and regulators to construct a forecasting model suitable for the financial distress of listed companies in China and to obtain early warning signals of serious deterioration of the financial situation of listed companies in time. Logistic regression is one of the mainstream statistical models in previous researches on financial distress prediction. However, the assumption of fixed coefficient of the model implied in logistic regression may be contrary to the facts and need to be tested by statistics. The industry attribute of the enterprise is often ignored or evaded in the forecasting model of financial distress. Therefore, it is necessary to incorporate the industry attribute into the forecasting model and verify the explanatory power of the enterprise industry attribute to the change of regression coefficient. In this paper, the hierarchical Logistic model is chosen to build the prediction model. The model can not only be applied to discrete response variables, but also can deal with the statistical model of hierarchical data structure. It can introduce macro variables (i.e. industry information) into the model and test its effects. This paper firstly studies the financial distress prediction of listed companies, and then gives the definition method of financial distress based on current ratio, then selects samples and indicators, and selects 2010 Shanghai, China. The A-share listed companies in Shenzhen stock market are taken as the total sample of 1249 companies, including 272companies with financial distress. The financial index and non-financial index are selected as the predictive variables, and then the multi-layer Logistic model is established. The data of this study are obtained from the Cathay Pacific CSMAR database. The prediction model of financial distress based on hierarchical Logistic model shows that the change of micro-level regression coefficient exists in inter-group (cross-industry), and the change can be decomposed into two aspects, some of which can be explained by macro-level explanatory variables. Part of it is explained by the random slope of regression coefficient of microcosmic variables. At the same time, in the sense of financial management, The model supports the influence of industry prospect level (expressed by the average growth rate of industry income) on the risk of financial distress at the macro level, and supports the current ratio, current debt ratio, asset return rate at the micro level. The influence of current assets turnover rate and audit opinion on the risk of financial distress of enterprises. Except for the stochastic effect of return on assets, the rest are fixed effects. Based on the fitting effect of the model, the multi-layer Logistic model can fit the data well. It is suitable for forecasting financial distress of listed companies.
【學(xué)位授予單位】：首都經(jīng)濟(jì)貿(mào)易大學(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(guó)期刊全文數(shù)據(jù)庫(kù) 前10條

1 吳德勝,梁j,殷尹;不同模型在財(cái)務(wù)預(yù)警實(shí)證中的比較研究[J];管理工程學(xué)報(bào);2004年02期

2 李義超;蔡晶晶;;基于財(cái)務(wù)與非財(cái)務(wù)指標(biāo)的財(cái)務(wù)困境預(yù)警研究——以制造業(yè)上市公司為例[J];中國(guó)管理信息化;2010年21期

3 鮮文鐸;金延華;;基于Probit模型的上市公司財(cái)務(wù)失敗實(shí)證研究[J];貴州財(cái)經(jīng)學(xué)院學(xué)報(bào);2007年06期

4 劉迎春;;基于Logistic回歸的中國(guó)上市公司信用風(fēng)險(xiǎn)度量研究[J];黑龍江對(duì)外經(jīng)貿(mào);2010年11期

5 吳世農(nóng),盧賢義;我國(guó)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

6 呂長(zhǎng)江,徐麗莉,周琳;上市公司財(cái)務(wù)困境與財(cái)務(wù)破產(chǎn)的比較分析[J];經(jīng)濟(jì)研究;2004年08期

7 姜國(guó)華,王漢生;上市公司連續(xù)兩年虧損就應(yīng)該被“ST”嗎?[J];經(jīng)濟(jì)研究;2005年03期

8 郭斌;戴小敏;曾勇;方洪全;;我國(guó)企業(yè)危機(jī)預(yù)警模型研究—以財(cái)務(wù)與非財(cái)務(wù)因素構(gòu)建[J];金融研究;2006年02期

9 劉京軍;秦宛順;;上市公司陷入財(cái)務(wù)困境可能性研究[J];金融研究;2006年11期

10 劉洪,何光軍;基于人工神經(jīng)網(wǎng)絡(luò)方法的上市公司經(jīng)營(yíng)失敗預(yù)警研究[J];會(huì)計(jì)研究;2004年02期

中國(guó)博士學(xué)位論文全文數(shù)據(jù)庫(kù) 前1條

1 徐鹿;上市公司財(cái)務(wù)困境預(yù)測(cè)實(shí)證研究[D];東北林業(yè)大學(xué);2002年

本文編號(hào)：1681891