【摘要】：伴隨著互聯(lián)網(wǎng)金融的發(fā)展,供應(yīng)鏈金融已從最早的1.0時(shí)代發(fā)展到3.0時(shí)代,即互聯(lián)網(wǎng)供應(yīng)鏈金融。一方面,在現(xiàn)有開(kāi)展供應(yīng)鏈金融業(yè)務(wù)的主體一銀行和上市公司中,銀行業(yè)務(wù)對(duì)象的抓大放小、上市公司自有資金的運(yùn)營(yíng)弱勢(shì)直接導(dǎo)致供應(yīng)鏈金融業(yè)務(wù)存在空白和短缺。另一方面,2014年,P2P網(wǎng)絡(luò)借貸平臺(tái)出現(xiàn)“資產(chǎn)荒”以及P2P網(wǎng)絡(luò)借貸問(wèn)題平臺(tái)的不斷增加,P2P行業(yè)中逐漸成熟的平臺(tái)已經(jīng)開(kāi)始涉水“供應(yīng)鏈金融”。但是,在P2P供應(yīng)鏈金融模式下,P2P網(wǎng)絡(luò)借貸平臺(tái)面對(duì)的是來(lái)自中小企業(yè)的信用風(fēng)險(xiǎn),一旦供應(yīng)鏈上的中小企業(yè)發(fā)生違約,將會(huì)危及P2P網(wǎng)絡(luò)借貸平臺(tái)的資金運(yùn)轉(zhuǎn),進(jìn)而影響投資者的資金回收。因此,評(píng)價(jià)P2P供應(yīng)鏈金融模式下中小企業(yè)信用風(fēng)險(xiǎn),一方面可以幫助P2P網(wǎng)絡(luò)借貸平臺(tái)發(fā)掘優(yōu)質(zhì)的資產(chǎn),另一方面對(duì)P2P網(wǎng)絡(luò)借貸平臺(tái)有效評(píng)價(jià)供應(yīng)鏈金融模式下中小企業(yè)信用風(fēng)險(xiǎn)評(píng)價(jià)提出可行性建議。本文在文獻(xiàn)研究和理論研究的基礎(chǔ)上,首先從融資企業(yè)資質(zhì)、核心企業(yè)資質(zhì)、融資項(xiàng)目資質(zhì)、供應(yīng)鏈伙伴關(guān)系以及行業(yè)環(huán)境五個(gè)方面構(gòu)建了信用風(fēng)險(xiǎn)預(yù)選評(píng)價(jià)指標(biāo)體系,即5個(gè)一級(jí)指標(biāo),14個(gè)二級(jí)指標(biāo)以及24個(gè)三級(jí)指標(biāo),結(jié)合相關(guān)性分析和鑒別力分析,確定了的最終評(píng)價(jià)指標(biāo)體系,即5個(gè)一級(jí)指標(biāo),13個(gè)二級(jí)指標(biāo)以及20個(gè)三級(jí)指標(biāo)。其次,選取以A股煤炭供應(yīng)鏈上市公司瑞茂通(600180)及其部分子公司為核心企業(yè)的46家中小企業(yè),時(shí)間跨度最長(zhǎng)為2014-2015年的所需數(shù)據(jù),共形成88個(gè)樣本點(diǎn),通過(guò)基于主成分分析的Logistic回歸模型和BP神經(jīng)網(wǎng)絡(luò)模型對(duì)P2P供應(yīng)鏈金融模式下中小企業(yè)信用風(fēng)險(xiǎn)進(jìn)行了評(píng)價(jià),實(shí)證結(jié)果表明,在相同的指標(biāo)體系下,運(yùn)用BP神經(jīng)網(wǎng)絡(luò)模型對(duì)P2P供應(yīng)鏈金融模式下的中小企業(yè)信用風(fēng)險(xiǎn)評(píng)價(jià)的整體準(zhǔn)確率較高,優(yōu)于Logistic回歸模型的評(píng)價(jià)效果。最后結(jié)合理論研究和實(shí)證研究結(jié)果,本文從信用風(fēng)險(xiǎn)評(píng)價(jià)自身工作、信用風(fēng)險(xiǎn)評(píng)價(jià)輔助工作以及加強(qiáng)防范P2P網(wǎng)絡(luò)借貸自身法律風(fēng)險(xiǎn)三個(gè)方面提出了 P2P供應(yīng)鏈金融模式下中小企業(yè)信用風(fēng)險(xiǎn)評(píng)價(jià)建議。
[Abstract]:With the development of Internet finance, supply chain finance has developed from 1.0 times to 3.0 times, that is, Internet supply chain finance. On the one hand, among the banks and listed companies, the main body of the supply chain financial business is banks and listed companies, and the weakness of the operation of their own funds directly leads to the blank and shortage of the supply chain financial business. On the other hand, in 2014, the P2P network loan platform appeared "assets shortage" and the P2P network loan problem platform unceasingly increased, the P2P industry gradually matured platform already began to involve the water "the supply chain finance". However, in the P2P supply chain finance model, P2P network lending platform is faced with the credit risk from small and medium-sized enterprises. Once the small and medium-sized enterprises in the supply chain default, it will endanger the capital operation of P2P network lending platform. In turn, it affects the return of funds to investors. Therefore, evaluating the credit risk of SMEs under the P2P supply chain financial model, on the one hand, can help P2P network lending platform to explore high-quality assets. On the other hand, some feasible suggestions are put forward to evaluate the credit risk of SMEs under the supply chain finance model. On the basis of literature research and theoretical research, this paper first constructs the credit risk pre-selection evaluation index system from five aspects: financing enterprise qualification, core enterprise qualification, financing project qualification, supply chain partnership and industry environment. That is, 5 first class indexes, 14 secondary indexes and 24 third grade indexes, combined with correlation analysis and discriminant analysis, the final evaluation index system is determined, that is, 5 primary index, 13 second class index and 20 third grade index. Secondly, we select 46 small and medium-sized enterprises which take the A share coal supply chain listed company Ruimao Tong (600180) and some of its subsidiaries as the core enterprises. The longest time span is the data needed in 2014-2015, forming a total of 88 sample points. Through the Logistic regression model based on principal component analysis and BP neural network model to evaluate the credit risk of SMEs in P2P supply chain finance model, the empirical results show that under the same index system, The BP neural network model is better than the Logistic regression model in evaluating the credit risk of SMEs under the P2P supply chain finance model. Finally, combined with the theoretical and empirical research results, this paper from the credit risk evaluation of their own work, This paper puts forward some suggestions on credit risk evaluation of SMEs in P2P supply chain finance model from three aspects: supporting work of credit risk evaluation and strengthening prevention of legal risk of P2P network loan.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F832.4;F275

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