本文選題：中小企業(yè) + 信用評(píng)級(jí)�。� 參考：《江西財(cái)經(jīng)大學(xué)》2016年碩士論文

【摘要】：中小企業(yè)的發(fā)展關(guān)系著國家經(jīng)濟(jì)社會(huì)的發(fā)展,我國企業(yè)絕大部分是中小企業(yè),其在城鎮(zhèn)人口就業(yè)、出口貿(mào)易、技術(shù)創(chuàng)新等方面都發(fā)揮著重要作用。目前其主要融資來源是商業(yè)銀行,但是商業(yè)銀行對(duì)于中小企業(yè)融資還存在很大的擔(dān)憂。究其原因,一方面是中小企業(yè)的財(cái)務(wù)數(shù)據(jù)等信息不透明;一方面是由于信用評(píng)級(jí)體系和方法的限制,商業(yè)銀行無法對(duì)中小企業(yè)進(jìn)行有效的信用評(píng)估,導(dǎo)致商業(yè)銀行惜貸。因而,完善中小企業(yè)信用評(píng)級(jí)體系也就變得尤為重要。企業(yè)的信用評(píng)級(jí)有助于商業(yè)銀行評(píng)估中小企業(yè)信用風(fēng)險(xiǎn),幫助優(yōu)秀的中小企業(yè)接受銀行更多的政策、資金傾斜;同時(shí),對(duì)于經(jīng)營(yíng)管理不夠好的企業(yè),起著警示督促作用,具有顯著的實(shí)踐意義。在指標(biāo)體系選取中,結(jié)合了定性和定量指標(biāo),在完備中小企業(yè)信用評(píng)級(jí)體系,提供了一定的參考;把BP神經(jīng)網(wǎng)絡(luò)用于中小企業(yè)信用評(píng)級(jí),豐富和更新了商業(yè)銀行信用評(píng)級(jí)方法,具有重要的學(xué)術(shù)價(jià)值。本文從中小企業(yè)融資難作為切入點(diǎn),讓融資問題和信用評(píng)級(jí)進(jìn)行良好的對(duì)接,為中小企業(yè)融資問題解決提供突破口。對(duì)于中小企業(yè)信用評(píng)級(jí)指標(biāo)體系,結(jié)合了定性和定量指標(biāo),并將定性指標(biāo)定量化表示,減少了指標(biāo)選取中的主觀人為因素。評(píng)分模型運(yùn)用了 BP神經(jīng)網(wǎng)絡(luò)的方法,以50個(gè)中小板企業(yè)為樣本,進(jìn)行對(duì)信用評(píng)分,然后也對(duì)構(gòu)建的的神經(jīng)網(wǎng)絡(luò)模型進(jìn)行檢驗(yàn),由此同時(shí),也利用了線性回歸的方法對(duì)上述樣本線性擬合,比較兩種模型的結(jié)果,分析它們結(jié)果的原因,進(jìn)一步闡述了神經(jīng)網(wǎng)絡(luò)在信用評(píng)級(jí)鄰域中具有的重大優(yōu)勢(shì)。首先,本文對(duì)中小企業(yè)面臨的融資困境的原因進(jìn)行闡述,而導(dǎo)致這一現(xiàn)象的原因之一就是銀行對(duì)于企業(yè)信用的擔(dān)憂,提出了解決融資難的一個(gè)突破口,就是完善商業(yè)銀行對(duì)中小企業(yè)的信用評(píng)級(jí)體系。然后,本文參考了中國人民銀行,組織協(xié)會(huì),建設(shè)銀行,標(biāo)準(zhǔn)普爾以及穆迪公司的企業(yè)信用評(píng)級(jí)指標(biāo),但是由于我國中小企業(yè)在信息公開比較少,也存在財(cái)務(wù)指標(biāo)數(shù)據(jù)的不真實(shí)的情況,結(jié)合此特點(diǎn),文本建立了中小企業(yè)的信用評(píng)級(jí)指標(biāo)體系,償債能力、盈利能力、營(yíng)運(yùn)能力、成長(zhǎng)能力、經(jīng)營(yíng)者及員工素質(zhì)、創(chuàng)新能力六個(gè)一級(jí)指標(biāo),二級(jí)指標(biāo)有16個(gè)。在二級(jí)指標(biāo)的篩選中,通過對(duì)前人研究成果的搜集。整理,綜合篩選出16個(gè)指標(biāo),指標(biāo)體系結(jié)合了定性和定量指標(biāo),也有財(cái)務(wù)指標(biāo)和非財(cái)務(wù)指標(biāo)的體現(xiàn),并且也對(duì)領(lǐng)導(dǎo)者管理水平、員工素質(zhì)、創(chuàng)新能力三個(gè)定性指標(biāo),進(jìn)行定量化處理,用可量化的行政管理人員比重、大專以上人員比重、科研技術(shù)人員比重來表示。建立起的指標(biāo)體系比較適用于中小企業(yè),為銀行評(píng)估企業(yè)信用提供一定參考。在指標(biāo)體系構(gòu)建完成后,需要選取一個(gè)評(píng)分模型,傳統(tǒng)的信用評(píng)級(jí)方法有5C、5P、5W,這些方法對(duì)于不能量化的因素帶有主觀不確定性,并且對(duì)于專家人員的數(shù)量和質(zhì)量要求高,需要不斷更新專家?guī)?統(tǒng)計(jì)模型法在信用評(píng)級(jí)方面的運(yùn)用,有其簡(jiǎn)單易操作的特點(diǎn),但是模型不能反映一個(gè)動(dòng)態(tài)的過程;層次分析法用于信用評(píng)級(jí)在判斷相對(duì)重要性時(shí)存在較大的主觀性。然而,在闡述BP神經(jīng)網(wǎng)絡(luò)的理論過程中,發(fā)現(xiàn)它能夠避免在權(quán)值確定等方面時(shí)的主觀性,也能夠很好處理非線性問題,具有很強(qiáng)的學(xué)習(xí)能力,并且適用于中小企業(yè)信用評(píng)級(jí)。經(jīng)過上述指標(biāo)和模型的選取,接下來,進(jìn)行基于此模型的實(shí)證分析,并和線性回歸模型結(jié)果進(jìn)行對(duì)比分析。根據(jù)此問題的需要和神經(jīng)網(wǎng)絡(luò)理論,BP神經(jīng)網(wǎng)絡(luò)采用了 16-8-1的拓?fù)浣Y(jié)構(gòu),即設(shè)置了只有一層隱含層的網(wǎng)絡(luò)結(jié)構(gòu),其中輸入層有16個(gè)神經(jīng)元節(jié)點(diǎn)(16個(gè)二級(jí)指標(biāo)),隱含層有8個(gè)神經(jīng)元,一個(gè)輸出值就是企業(yè)的信用評(píng)分。以50個(gè)中小板企業(yè)樣本數(shù)據(jù)為例,采用樣本剖分法,40個(gè)樣本用于訓(xùn)練BP神經(jīng)網(wǎng)絡(luò),10組數(shù)據(jù)用于檢驗(yàn)網(wǎng)絡(luò)結(jié)構(gòu)。以絕對(duì)誤差小于0.05作為容忍范圍,訓(xùn)練樣本的預(yù)測(cè)評(píng)分準(zhǔn)確率高達(dá)92.5%,檢驗(yàn)樣本的準(zhǔn)確率為80%。同時(shí),也用相同的數(shù)據(jù),對(duì)上述樣本進(jìn)行多元線性擬合,因變量為期望信用評(píng)分,自變量為16個(gè)指標(biāo)節(jié)點(diǎn),擬合結(jié)果的殘差卻要大的多,擬合樣本的準(zhǔn)確率只有42.5%,顯示出線性回歸在中小企業(yè)信用評(píng)級(jí)中的巨大弊端。實(shí)證的結(jié)果表明,BP神經(jīng)網(wǎng)絡(luò)在中小企業(yè)信用評(píng)級(jí)中體現(xiàn)出了巨大優(yōu)勢(shì)。最后,本文的結(jié)論是:(1)中小企業(yè)的融資難問題主要原因是信息不對(duì)稱,商業(yè)銀行為了規(guī)避風(fēng)險(xiǎn),對(duì)中小企業(yè)息貸,因此加強(qiáng)企業(yè)信息公開,商業(yè)銀行加強(qiáng)對(duì)中小企業(yè)信用評(píng)級(jí)體系建設(shè),為融資難問題破解助力。(2)中小企業(yè)信用指標(biāo)應(yīng)同時(shí)結(jié)合定性指標(biāo)和定量指標(biāo)、財(cái)務(wù)指標(biāo)和非財(cái)務(wù)指標(biāo)的選取,對(duì)定性指標(biāo)定量化,減少在指標(biāo)中的主觀性。(3)信用評(píng)級(jí)的統(tǒng)計(jì)模型法受制于變量數(shù)據(jù)正態(tài)分布的假設(shè),而財(cái)務(wù)指標(biāo)數(shù)據(jù)一般是不會(huì)服從正態(tài)分布的,在實(shí)證的對(duì)比中,也看出了統(tǒng)計(jì)模型并不適用于中小企業(yè)信用評(píng)級(jí)。(4)同時(shí),在實(shí)證中,也證明了,BP神經(jīng)網(wǎng)絡(luò)在信用評(píng)級(jí)中的優(yōu)勢(shì):一是它具有良好的自適應(yīng)能力,在確定各指標(biāo)體系權(quán)重中,不需要人為來確定,而是根據(jù)數(shù)據(jù)反復(fù)訓(xùn)練學(xué)習(xí),來確定和調(diào)整輸入和輸出之間的關(guān)系,能夠弱化主觀因素的存在;二是處理非線性的能力。用線性回歸模型得出的評(píng)分殘差比較大,而BP神經(jīng)網(wǎng)絡(luò)的殘差比較小,體現(xiàn)出處理非線性問題的能力。三是BP網(wǎng)絡(luò)具有很好的動(dòng)態(tài)評(píng)價(jià)效果。
[Abstract]:The development of small and medium-sized enterprises is related to the development of the national economy and society. Most of our enterprises are small and medium-sized enterprises, which play an important role in urban population employment, export trade and technological innovation. At present, the main source of financing is commercial banks, but the commercial banks still have great worries about the financing of small and medium-sized enterprises. On the one hand, the financial data of small and medium-sized enterprises are not transparent. On the one hand, because of the limitation of the credit rating system and methods, commercial banks can not carry out effective credit evaluation to small and medium-sized enterprises and lead to the credit crunch of commercial banks. Therefore, it becomes particularly important to improve the credit rating system of small and medium-sized enterprises. It helps the commercial banks to assess the credit risk of small and medium-sized enterprises, help the outstanding small and medium-sized enterprises to accept more policies and fund the banks, and at the same time, it has a warning and supervision role for enterprises which are not good enough in management. In the selection of the index system, it combines qualitative and quantitative indicators to complete the credit of small and medium-sized enterprises. The rating system provides a certain reference; it is of important academic value to use the BP neural network in the credit rating of small and medium-sized enterprises and to enrich and update the credit rating methods of commercial banks. This paper makes a good connection between the financing problem and credit rating from the financing difficulty of small and medium-sized enterprises, and proposes to solve the financing problems of small and medium-sized enterprises. For the credit rating system, the credit rating index system of small and medium-sized enterprises, combining qualitative and quantitative indicators, and quantifying qualitative indicators, reduces the subjective factors in the selection of indicators. The scoring model uses the BP neural network method, takes 50 small and medium-sized board enterprises as samples, carries on the credit score, and then also constructs the nerve. The network model is tested, and at the same time, linear regression is also used to fit the above samples linearly, compare the results of the two models, analyze the causes of their results, and further elaborate the important advantages of the neural network in the credit rating neighborhood. First, the reasons for the financing difficulties faced by the SMEs are explained. One of the reasons for this phenomenon is that the bank is worried about the credit of the enterprise, and puts forward a breakthrough to solve the difficulty of financing. It is to perfect the credit rating system of commercial banks to small and medium-sized enterprises. Then, this article refers to the credit evaluation of the people's Bank of China, the organization association, the Construction Bank, the standard & Poor's and the Moodie company. But because the small and medium enterprises in our country have little information disclosure, there is also an untrue situation of financial index data. In combination with this characteristic, the text establishes the credit rating index system of small and medium-sized enterprises, the solvency, profitability, operation ability, growth ability, the quality of operators and employees, and the six first grade index of innovation ability and two level. There are 16 indicators. In the screening of the two level indicators, through the collection of previous research results, the comprehensive screening of 16 indicators, the index system combines the qualitative and quantitative indicators, also the embodiment of financial and non-financial indicators, and also the leadership management level, staff quality, innovation ability three qualitative indicators, the quantitative department. According to the proportion of quantifiable administrative staff, the proportion of college and above personnel, the proportion of scientific and technical personnel, the established index system is more suitable for small and medium enterprises, and provides a reference for the bank to evaluate the enterprise credit. After the completion of the index system, a scoring model should be selected, and the traditional credit rating method is 5C, 5P, 5W, these methods have subjective uncertainty for the factors that can not be quantified, and the requirements for the quantity and quality of the experts are high, and the expert library needs to be updated continuously. The application of the statistical model in credit rating has its simple and easy to operate characteristics, but the model can not reflect a dynamic process; the analytic hierarchy process is used for credit. Rating has great subjectivity in judging the relative importance. However, in the theory of BP neural network, it is found that it can avoid subjectivism in the determination of weights and so on. It also can handle nonlinear problems well, has strong learning ability and is suitable for credit rating of small and medium-sized enterprises. According to the need of the problem and the neural network theory, the BP neural network adopts the topology of 16-8-1, that is, a network structure with only one layer of hidden layer is set up, in which there are 16 neuron nodes in the input layer (16 two). There are 8 neurons in the hidden layer. One output value is the credit score of the enterprise. Taking the sample data of 50 small and medium sized enterprises as an example, the sample dissection method is used, 40 samples are used to train the BP neural network, and the 10 sets of data are used to test the network structure. The absolute error is less than 0.05 as tolerance range, and the accuracy of the training sample is predicted. Up to 92.5%, the accuracy of the test sample is 80%. at the same time, and the same data is also used for multivariate linear fitting of the above samples. Because the variable is the expected credit score, the independent variable is 16 index nodes, the residual error of the fitting result is much larger, the accuracy rate of the fitting sample is only 42.5%, showing the linear regression in the credit rating of the small and medium-sized enterprises. The empirical results show that the BP neural network has shown great advantages in the credit rating of small and medium enterprises. Finally, the conclusion of this paper is: (1) the main reason for the financing difficulty of SMEs is information asymmetry, the commercial banks have to avoid the risks and interest loans to small and medium-sized enterprises, so the information disclosure of enterprises is strengthened and the commercial banks are strengthened. The construction of credit rating system for small and medium enterprises can help solve the problem of financing difficulties. (2) the credit index of SMEs should be combined with qualitative and quantitative indicators, the selection of financial and non-financial indicators, the quantitative index of the qualitative indicators and the reduction of subjectivity in the indicators. (3) the statistical model method of credit rating is subject to the normal state of variable data. The distribution hypothesis, and the financial index data is generally not subordinate to the normal distribution, in the empirical comparison, it is also found that the statistical model does not apply to the credit rating of small and medium enterprises. (4) at the same time, in the empirical, it is also proved that the BP neural network has the advantage in the credit rating: first, it has good adaptive ability and determines the index body. The weight of the system does not need to be determined artificially, but is trained and learned repeatedly according to the data to determine and adjust the relationship between the input and output, and can weaken the existence of the subjective factors; two is the ability to deal with the nonlinearity. The residual error of the score is relatively large with the linear regression model, and the residual difference of the BP neural network is small, reflecting the non line processing. The ability of sexual problems. Three, the BP network has a good dynamic evaluation effect.

【學(xué)位授予單位】：江西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP183;F276.3;F270

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