本文選題：科技型企業(yè)　切入點：信貸評價指標體系　出處：《天津財經(jīng)大學》2014年碩士論文

【摘要】：科技型企業(yè)具有高科技性、高成長性、高風險性的“三高”特點,在全球科技創(chuàng)新飛速發(fā)展和我國現(xiàn)有金融體系“避險”特性的背景下,“融資難”問題是其不斷發(fā)展壯大過程中所面臨的長期問題。在我國,商業(yè)銀行貸款是解決科技型企業(yè)融資困難的主要手段,也是首要的間接融資方式。但由于科技型企業(yè)數(shù)量多,發(fā)展歷史短,市場風險相對大,經(jīng)營利潤不穩(wěn)定,企業(yè)所擁有的可以明確進行評估和抵押的資產(chǎn)少等特點,導致企業(yè)的信用級別普遍不高,因此很難通過商業(yè)銀行的貸款資格審查。本文以我國科技型企業(yè)為研究對象,首先介紹了信貸評價指標體系基本理論和信貸評價模型,并將人工神經(jīng)網(wǎng)絡模型與常見評價模型進行比較分析。隨后闡述了科技型企業(yè)信貸評價指標的設置原則并提出本文選取的評價指標,接著運用層次分析法和專家打分法對指標進行深入分析并賦予權重,以此建立一套科學、合理、有效的科技型企業(yè)信貸評價指標體系,并通過分析科技型企業(yè)的產(chǎn)業(yè)特征和信貸需求,最終以天津市25家科技型企業(yè)為例進行了實證研究：基于遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡建立了一個針對我國科技型企業(yè)的信貸評價模型�；贕A-BP算法的神經(jīng)網(wǎng)絡模型克服了傳統(tǒng)BP算法容易陷入局部最小收斂速度較慢、模型結構不易確定等缺陷,同時,在繼承傳統(tǒng)BP神經(jīng)網(wǎng)絡的自學習、自適應和非線性映射能力的基礎上,有效降低了輸出結果的誤差,改善了模型的性能,提高了BP神經(jīng)網(wǎng)絡的泛化能力。最后通過數(shù)據(jù)的驗證說明了基于GA-BP算法的神經(jīng)網(wǎng)絡模型對于科技型企業(yè)信貸評價指標體系的建立是完全有效并可行的。本文的研究成果對進一步在實踐中探索科技型企業(yè)信貸評價指標體系和解決科技型企業(yè)融資難的問題具有重要意義。
[Abstract]:Science and technology enterprises have the characteristics of high technology, high growth and high risk. In the context of the rapid development of global scientific and technological innovation and the "safe haven" characteristics of our existing financial system, the problem of "financing difficulty" is a long-term problem in the process of its continuous development and expansion. Commercial bank loans are the main means to solve the financing difficulties of sci-tech enterprises and the primary indirect financing methods. However, due to the large number of sci-tech enterprises, short history of development, relatively large market risks and unstable operating profits, The characteristics that enterprises have, such as less assets that can be clearly assessed and mortgaged, lead to a general low credit level of enterprises, so it is difficult to pass the loan qualification examination of commercial banks. This paper takes our country's science and technology enterprises as the research object. First of all, it introduces the basic theory of credit evaluation index system and credit evaluation model. The paper compares the artificial neural network model with the common evaluation model, then expounds the setting principle of the credit evaluation index of the science and technology enterprise and puts forward the evaluation index selected in this paper. Then it uses the analytic hierarchy process and the expert scoring method to carry on the thorough analysis to the index and endows the weight, thus establishes a set of scientific, reasonable, effective science and technology enterprise credit appraisal index system. And through the analysis of the industrial characteristics and credit demand of science and technology enterprises, Finally, 25 enterprises in Tianjin are taken as an example. BP neural network based on genetic algorithm optimization is used to establish a credit evaluation model for Chinese science and technology enterprises. Neural network model based on GA-BP algorithm. It overcomes the slow convergence speed of traditional BP algorithm, which is easy to fall into local minimum convergence. The model structure is difficult to determine and so on. At the same time, on the basis of inheriting the self-learning, adaptive and nonlinear mapping ability of the traditional BP neural network, the output error is effectively reduced, and the performance of the model is improved. The generalization ability of BP neural network is improved. Finally, the neural network model based on GA-BP algorithm is proved to be effective and feasible for the establishment of credit evaluation index system of science and technology enterprises. The research results are of great significance to further explore the credit evaluation index system of science and technology enterprises in practice and to solve the problem of financing difficulties of science and technology enterprises.
【學位授予單位】：天津財經(jīng)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F275;F832.4

【參考文獻】

相關期刊論文 前10條

1 宋昱雯;劉亞娜;;模糊AHP法在中小企業(yè)信用評價指標體系構建中的應用[J];企業(yè)導報;2013年16期

2 呂曉丹;范宏;;基于決策樹的信用評價模型及實證研究[J];市場周刊(理論研究);2013年08期

3 趙玲;賀小海;陳曉慧;張義榮;;我國科技型企業(yè)成長性評價體系研究——以科技型中型企業(yè)為例[J];科技和產(chǎn)業(yè);2013年03期

4 賈煒瑩;王迪;;中小企業(yè)信用貸款風險指標體系構建及評價[J];河北工程大學學報(社會科學版);2013年01期

5 侯昊鵬;;國內(nèi)外企業(yè)信用評級指標體系研究的新關注[J];經(jīng)濟學家;2012年05期

6 藺全錄;劉洋;;基于BP-ANN的科技型中小企業(yè)成長性測度研究──以蘭州市為例[J];企業(yè)活力;2012年03期

7 黃飛鳴;;金融體系的順周期性問題解讀[J];經(jīng)濟評論;2010年02期

8 朱勇萍;陳繼祥;;科技型中小企業(yè)創(chuàng)新能力評估指標體系研究[J];計算機應用與軟件;2009年03期

9 王凱;黃世祥;;行業(yè)內(nèi)中小企業(yè)信用評估模型及應用[J];數(shù)學的實踐與認識;2008年04期

10 蔣志華;張銳;;上市公司信用評價指標體系的構建[J];商場現(xiàn)代化;2006年24期

相關碩士學位論文 前6條

1 鄧舒放;基于決策樹—神經(jīng)網(wǎng)絡的個人信用評估組合模型的構建[D];湖南大學;2012年

2 汪莉;基于Logistic回歸模型的中小企業(yè)信用評分研究[D];合肥工業(yè)大學;2008年

3 黃英婷;我國商業(yè)銀行的中小企業(yè)信用評級研究[D];暨南大學;2006年

4 馬杰;我國中小企業(yè)信用評價模型及評級制度探討[D];對外經(jīng)濟貿(mào)易大學;2006年

5 陶軍;基于業(yè)績的企業(yè)信用風險預警模型研究[D];北京化工大學;2005年

6 田俊平;我國企業(yè)信用評價指標有效性研究[D];北京化工大學;2005年

，

本文編號：1663678