發(fā)布時(shí)間：2018-05-09 15:15

  本文選題：中小企業(yè) + 商業(yè)銀行　； 參考：《河北工程大學(xué)》2017年碩士論文

【摘要】：近年來,金融危機(jī)發(fā)生的次數(shù)越來越多,中小企業(yè)融資難的問題暴露的越來越明顯,直接影響著我國經(jīng)濟(jì)發(fā)展。因此商業(yè)銀行以及政府都應(yīng)該對此高度關(guān)注。商業(yè)銀行在信用風(fēng)險(xiǎn)的管理上采取了許多新的辦法,雖然有所緩解但是效果不是很明顯。如果想解決中小企業(yè)融資難,就要對其原因進(jìn)行分析,造成中小企業(yè)融資難的根本原因是商業(yè)銀行和中小企業(yè)信息不對稱造成的。如果想解決該問題就應(yīng)該找到能夠準(zhǔn)確評估中小企業(yè)信用風(fēng)險(xiǎn)的方法,商業(yè)銀行對中小企業(yè)的信用進(jìn)行準(zhǔn)確的評估對解決中小企業(yè)融資難起到很大的作用。本文站在商業(yè)銀行的角度對中小融資企業(yè)信用風(fēng)險(xiǎn)的評估,首先通過分析中小企業(yè)特點(diǎn),構(gòu)建中小企業(yè)的信用風(fēng)險(xiǎn)評價(jià)指標(biāo)體系,對比分析現(xiàn)有信用風(fēng)險(xiǎn)評價(jià)方法的優(yōu)缺點(diǎn),確定用支持向量機(jī)方法進(jìn)行評估,由于中小企業(yè)的特殊性,對支持向量機(jī)進(jìn)行了改進(jìn)和優(yōu)化,將最小二乘法與支持向量機(jī)相結(jié)合,并且用粒子群算法對其進(jìn)行了參數(shù)的優(yōu)化,對中小企業(yè)是否違約進(jìn)行了準(zhǔn)確的分類,證明了基于PSO-LSSVM模型在對中小企業(yè)的信用風(fēng)險(xiǎn)評估的優(yōu)越性,為商業(yè)銀行提供了一種很好的對中小企業(yè)進(jìn)行信用風(fēng)險(xiǎn)評估的方法。
[Abstract]:In recent years, the number of financial crises has become more and more, the problem of financing difficult for small and medium-sized enterprises is becoming more and more obvious, which directly affects the economic development of our country. Therefore, commercial banks and the government should pay much attention to this. It is very obvious that if it is difficult to solve the financing of small and medium-sized enterprises, it is necessary to analyze the reasons for the difficulty of financing for small and medium-sized enterprises. If we want to solve this problem, we should find a way to accurately assess the risk of SMEs' credit, and the commercial banks are to small and medium enterprises. The accurate evaluation of credit plays a very important role in solving the financing difficulties of small and medium-sized enterprises. This paper is to evaluate the credit risk of small and medium-sized financing enterprises from the perspective of commercial banks. First, through the analysis of the characteristics of small and medium-sized enterprises, the credit risk evaluation index system of small and medium-sized enterprises is constructed, and the advantages and disadvantages of the existing credit risk evaluation methods are compared. It is determined that the support vector machine is used for evaluation. Because of the particularity of the small and medium enterprises, the support vector machine is improved and optimized. The least square method is combined with the support vector machine, and the particle swarm optimization is used to optimize the parameters of the SVM. It has carried out an accurate classification to the breach of contract for small and medium enterprises, and proved that the PSO-LSSVM model is based on the model. The superiority of the model in the credit risk assessment of small and medium-sized enterprises provides a good way for the commercial banks to conduct credit risk assessment for SMEs.

【學(xué)位授予單位】：河北工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F276.3

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本文編號：1866513