本文選題：科技型企業(yè)　切入點(diǎn)：信貸評(píng)價(jià)指標(biāo)體系　出處：《天津財(cái)經(jīng)大學(xué)》2014年碩士論文

【摘要】：科技型企業(yè)具有高科技性、高成長(zhǎng)性、高風(fēng)險(xiǎn)性的“三高”特點(diǎn),在全球科技創(chuàng)新飛速發(fā)展和我國(guó)現(xiàn)有金融體系“避險(xiǎn)”特性的背景下,“融資難”問(wèn)題是其不斷發(fā)展壯大過(guò)程中所面臨的長(zhǎng)期問(wèn)題。在我國(guó),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(chǎng)風(fēng)險(xiǎn)相對(duì)大,經(jīng)營(yíng)利潤(rùn)不穩(wěn)定,企業(yè)所擁有的可以明確進(jìn)行評(píng)估和抵押的資產(chǎn)少等特點(diǎn),導(dǎo)致企業(yè)的信用級(jí)別普遍不高,因此很難通過(guò)商業(yè)銀行的貸款資格審查。本文以我國(guó)科技型企業(yè)為研究對(duì)象,首先介紹了信貸評(píng)價(jià)指標(biāo)體系基本理論和信貸評(píng)價(jià)模型,并將人工神經(jīng)網(wǎng)絡(luò)模型與常見(jiàn)評(píng)價(jià)模型進(jìn)行比較分析。隨后闡述了科技型企業(yè)信貸評(píng)價(jià)指標(biāo)的設(shè)置原則并提出本文選取的評(píng)價(jià)指標(biāo),接著運(yùn)用層次分析法和專(zhuān)家打分法對(duì)指標(biāo)進(jìn)行深入分析并賦予權(quán)重,以此建立一套科學(xué)、合理、有效的科技型企業(yè)信貸評(píng)價(jià)指標(biāo)體系,并通過(guò)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(jìn)行了實(shí)證研究：基于遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)建立了一個(gè)針對(duì)我國(guó)科技型企業(yè)的信貸評(píng)價(jià)模型�；贕A-BP算法的神經(jīng)網(wǎng)絡(luò)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(jié)構(gòu)不易確定等缺陷,同時(shí),在繼承傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)的自學(xué)習(xí)、自適應(yīng)和非線性映射能力的基礎(chǔ)上,有效降低了輸出結(jié)果的誤差,改善了模型的性能,提高了BP神經(jīng)網(wǎng)絡(luò)的泛化能力。最后通過(guò)數(shù)據(jù)的驗(yàn)證說(shuō)明了基于GA-BP算法的神經(jīng)網(wǎng)絡(luò)模型對(duì)于科技型企業(yè)信貸評(píng)價(jià)指標(biāo)體系的建立是完全有效并可行的。本文的研究成果對(duì)進(jìn)一步在實(shí)踐中探索科技型企業(yè)信貸評(píng)價(jià)指標(biāo)體系和解決科技型企業(yè)融資難的問(wèn)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(xué)位授予單位】：天津財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F275;F832.4

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