本文選題：財務(wù)危機(jī)預(yù)警 + 違約距離��； 參考：《西南財經(jīng)大學(xué)》2014年碩士論文

【摘要】：企業(yè)所有利益相關(guān)者,即企業(yè)的所在者,企業(yè)的經(jīng)營者,股東高度關(guān)注企業(yè)的財務(wù)狀況。對于企業(yè)的所有者來說對企業(yè)的財務(wù)狀況的關(guān)注就好比每個人對于自己身體狀況的關(guān)注一樣,營運良好的企業(yè)可以為企業(yè)的所有者帶來良好的回報并且可以擴(kuò)展融資渠道以及獲得更多的支持和便利。對于企業(yè)的經(jīng)營者來說,企業(yè)的財務(wù)狀況良好說明經(jīng)營者過去的某段時間的成績良好,經(jīng)營者可以獲得更高的薪酬和業(yè)績記錄。相反,若企業(yè)的財務(wù)出現(xiàn)危機(jī),不僅僅會是使股民望而卻步還令對原來的企業(yè)的經(jīng)營者和所有者產(chǎn)生恐慌。 通過上市公司財務(wù)危機(jī)預(yù)警的研究,我們首先可以制定一個科學(xué)的財務(wù)危機(jī)預(yù)警模型,能幫助公司及早根據(jù)預(yù)警信號采取相應(yīng)措施,同時對國家證券監(jiān)管部門監(jiān)控上市公司質(zhì)量和減少市場風(fēng)險也有重要的指導(dǎo)意義。 縱觀以往的財務(wù)危機(jī)預(yù)警研究,主要可以分為兩大類：財務(wù)指標(biāo)預(yù)警模型和信用風(fēng)險量化模型。財務(wù)指標(biāo)預(yù)警模型包括單變量模型,多元線性判別模型,線性概率模型,多元邏輯回歸模型。 信用風(fēng)險度量模型主要有：KMV公司的KMV模型、JP摩根的信用度量術(shù)模型(ceditmetrics model)、麥肯錫公司的宏觀模擬模型(credit portfolio view)、瑞士信貸銀行的信用風(fēng)險附加法模型(cridetrisk+)、死亡率模型(mortality rate)等。本文則要利用經(jīng)典的KMV模型來計算上市公司的違約距離DD. 本文認(rèn)為SVM模型在財務(wù)預(yù)測方面會優(yōu)于其他模型,不僅僅因為其他方法有著諸多的參數(shù)假設(shè),還因為在處理小樣本的問題上,SVM有著穩(wěn)定,能克服“維數(shù)災(zāi)難”等諸多優(yōu)點。 本文首先對所選取的指標(biāo)進(jìn)行了簡單的解釋,本章選取了反映公司盈利能力,償債能力,成長能力和營運能力的四大指標(biāo),它們分別是：凈資產(chǎn)收益率,總資產(chǎn)凈利率,銷售凈利率,銷售毛利率,流動比率,速動比率,產(chǎn)權(quán)比率,存貨周轉(zhuǎn)率,應(yīng)收賬款周轉(zhuǎn)率,總資產(chǎn)周轉(zhuǎn)率,營業(yè)收入增長率,凈利率增長率,總資產(chǎn)增長率,凈資產(chǎn)增長率。選取這些指標(biāo)的原因有兩個,其一是這些指標(biāo)大體上涵蓋了一個企業(yè)在財務(wù)方面的表現(xiàn),其二是參考了有些學(xué)者的研究。 其次對KMV原理進(jìn)行介紹,通過參數(shù)的設(shè)定進(jìn)而求得每家公司的違約距離DD。很多學(xué)者再采用KMV模型求違約距離DD時,參數(shù)的設(shè)定都有所不同,比如在DP=STD+1/2LTD這個公式上,有學(xué)者就認(rèn)為DP-STD+O.93LTD。根據(jù)國內(nèi)的資本市場的情況和很多學(xué)者的研究的理論基礎(chǔ)上,本文認(rèn)為不必要調(diào)整經(jīng)典KMV模型的參數(shù),根據(jù)經(jīng)典KMV模型,最后本文利用MATLAB算出了違約距離DD。 接著本文簡單的介紹了數(shù)據(jù)包絡(luò)分析(DEA)的原理,并求出了相應(yīng)的TE(技術(shù)效率,代表是企業(yè)的投入產(chǎn)出效率)。這部分比較重要的是如何選取DEA的輸入和輸出指標(biāo),本文則參考了潘潔的研究�；诒疚臉颖据^少而指標(biāo)較多的事實,本章決定對樣本所選取的指標(biāo)進(jìn)行主成分分析,提煉出樣本指標(biāo)的大部分信息。這部分簡單的介紹了主成分分析的思想和數(shù)學(xué)上的表示并且算出了樣本的5個主成分值。最后一部分是介紹了支持向量機(jī)的思想并且計算出整個企業(yè)財務(wù)預(yù)警模型的預(yù)測效率,通過將DD,TE和5個主成分進(jìn)行SVM分析,本文得到了預(yù)測正確概率高達(dá)80%以上的財務(wù)預(yù)警模型。 相對于其他學(xué)者做的有關(guān)財務(wù)預(yù)警的SVM模型,本文創(chuàng)新性的加入了KMV模型中的違約距離DD作為上市公司的信用指標(biāo),TE作為上市公司的投入產(chǎn)出指標(biāo),并且預(yù)測也取得了良好的效果。
[Abstract]:All stakeholders, that is, the owner of the enterprise, the operator of the enterprise, and the shareholders, are highly concerned about the financial situation of the enterprise. For the owner of the enterprise, it is like the attention to the state of the body for the financial situation of the enterprise. The good operating enterprise can bring good returns to the owner of the enterprise. And it can expand financing channels and get more support and convenience. For business operators, the financial situation of the enterprise is good to show that the manager has done well in the past some time, and the operator can obtain higher salary and record of performance. The deterrent also caused panic to the operators and owners of the original enterprises.
Through the study of the financial crisis early warning of listed companies, we can first make a scientific financial crisis early warning model, which can help the company to take corresponding measures according to early warning signal, and also have important guiding significance for the state securities regulatory department to monitor the quality of listed companies and reduce the market risk.
The previous financial crisis early-warning research can be divided into two major categories: financial indicators early warning model and credit risk quantification model. The financial indicators early warning model includes a single variable model, multiple linear discriminant model, linear probability model, and multiple logistic regression model.
The credit risk measurement models are mainly: KMV company's KMV model, JP Morgan's credit measurement model (ceditmetrics model), the macro simulation model (credit portfolio view) of the McKinsey Co, the credit risk additional model (cridetrisk+), the mortality model (mortality rate) and so on. The model is used to calculate the default distance of a listed company DD.
This paper considers that the SVM model will be superior to other models in financial forecasting, not only because other methods have many parametric assumptions, but also because of the stability of SVM in dealing with small sample problems, and can overcome many advantages such as "dimensionality disaster".
This article first gives a simple explanation of the selected indicators. This chapter selects four major indicators that reflect the company's profitability, solvency, growth capacity and operating capacity. They are net asset returns, net interest rate, net interest rate, gross profit, liquidity ratio, rate of movement, property ratio, and inventory turnover. The accounts receivable turnover rate, total asset turnover rate, business income growth rate, net interest rate growth rate, total asset growth rate and net asset growth rate are two reasons for selecting these indicators. The first is that these indexes generally cover the financial performance of an enterprise, and the other is a reference to some scholars' research.
Secondly, the KMV principle is introduced, and the default distance of each company is obtained through the parameter setting. DD. many scholars use KMV model to find the default distance DD, and the setting of the parameters is different. For example, on the DP=STD+1/2LTD formula, some scholars believe that DP-STD+O.93LTD. is based on the domestic capital market and many scholars. On the basis of the theoretical research, we think that it is unnecessary to adjust the parameters of the classical KMV model. According to the classical KMV model, we use MATLAB to calculate the default distance DD..
Then this paper briefly introduces the principle of data envelopment analysis (DEA), and finds out the corresponding TE (technical efficiency, the representative is the input-output efficiency of the enterprise). This part is more important to select the input and output index of DEA. This article refer to Pan Jie's research. Based on the fact that the sample is less and the index is more, this chapter decides In this part, we simply introduce the idea and mathematical representation of the principal component analysis and calculate the 5 main components of the sample. The last part introduces the thought of support vector machines and calculates the financial early-warning model of the whole enterprise. Through the SVM analysis of DD, TE and 5 principal components, we get the financial early-warning model with the correct probability of more than 80%.
Compared with other scholars' SVM model on financial early-warning, this paper innovatively joins the default distance DD in the KMV model as the credit index of the listed company, TE as the input and output index of the listed company, and the prediction also has achieved good results.

【學(xué)位授予單位】：西南財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F832.51;F275;F224

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