【摘要】：中小企業(yè)在我國國民經(jīng)濟和社會發(fā)展中處于重要的戰(zhàn)略地位,但中小企業(yè)長期以來一直面臨著融資困境,制約了其健康發(fā)展。形成融資難的原因是多方面的,如果能建立一套適用中小企業(yè)特點的信用評級方法,將在很大程度上解決銀企之間的信息不對稱問題,從而緩解其融資困境。但是,我國目前中小企業(yè)信用評級基本沿用大型企業(yè)的評級方法,致使其評級結(jié)果并不能準確反映真實的信用水平,難以真實反映中小企業(yè)信用風險。近年來,中小企業(yè)信用評級研究方興未艾,取得了一系列的研究成果,也積累了很多成功經(jīng)驗,但仍然缺乏一套能充分反映中小企業(yè)自身特點的專用信用評級系統(tǒng),因此需要對已有的中小企業(yè)信用評價指標體系和技術(shù)路線予以優(yōu)化,為中小企業(yè)健康發(fā)展提供金融支持服務。 本研究致力于中小企業(yè)信用評級指標體系的選取和技術(shù)路線的優(yōu)化。文章首先對信用評級的相關(guān)理論和問題進行了詳細的分析和研究,然后通過對目前商業(yè)銀行指標體系缺陷性的研究情況下,結(jié)合中小企業(yè)自身特點以及信用狀況水平進行系統(tǒng)分析后,建立了一套針對中小企業(yè)自身的信用評級指標體系。為了克服現(xiàn)有基于傳統(tǒng)統(tǒng)計模型評級方法的局限性,本研究力圖將信用評級轉(zhuǎn)換為模式識別和聚類分析,通過選用一種小樣本學習理論支持向量機(SVM)方法對中小企業(yè)信用狀況進行評估,形成較為先進的中小企業(yè)信用評級方法。文章對該方法進行了詳細的介紹,最后通過實證分析并與BP神經(jīng)網(wǎng)絡進行比較最終證明了該方法的有效性,并對未來的深入研究進行了展望。
[Abstract]:Small and medium-sized enterprises (SMEs) are in an important strategic position in the national economy and social development of our country, but SMEs have been facing financing difficulties for a long time, which has restricted their healthy development. There are many reasons for the difficulty in financing. If we can establish a set of credit rating methods suitable for the characteristics of small and medium-sized enterprises, we will solve the problem of information asymmetry between banks and enterprises to a great extent, and then alleviate their financing difficulties. However, at present, the credit rating of small and medium-sized enterprises in our country basically follows the method of large enterprises, which makes the result of credit rating can not accurately reflect the true credit level, and it is difficult to truly reflect the credit risk of small and medium-sized enterprises. In recent years, the research on the credit rating of small and medium-sized enterprises is in the ascendant, and has made a series of research results and accumulated a lot of successful experiences. However, there is still a lack of a special credit rating system that can fully reflect the characteristics of small and medium-sized enterprises. Therefore, it is necessary to optimize the existing credit evaluation index system and technical route of SMEs to provide financial support services for the healthy development of SMEs. This study is devoted to the selection of credit rating index system and the optimization of technical route. Firstly, this paper makes a detailed analysis and research on the relevant theories and problems of credit rating, and then through the current research on the defect of the index system of commercial banks, After analyzing the characteristics and credit status of SMEs, a set of credit rating index system is established. In order to overcome the limitations of traditional statistical model based rating methods, this study attempts to transform credit rating into pattern recognition and clustering analysis. By selecting a small sample learning theory support vector machine (SVM) method to evaluate the credit status of small and medium-sized enterprises, a more advanced credit rating method for small and medium-sized enterprises is formed. This paper introduces the method in detail, and finally proves the effectiveness of this method by empirical analysis and comparison with BP neural network, and looks forward to further research in the future.
【學位授予單位】：安徽財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：F275;F832.4;TP181

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