本文選題：BP神經(jīng)網(wǎng)絡(luò) + 農(nóng)業(yè)類上市企業(yè)　； 參考：《湖南大學(xué)》2012年碩士論文

【摘要】：農(nóng)業(yè)是國(guó)民經(jīng)濟(jì)的基礎(chǔ)產(chǎn)業(yè)，在我國(guó)經(jīng)濟(jì)發(fā)展過(guò)程中發(fā)揮著重要作用。隨著我國(guó)工業(yè)化和城市化進(jìn)程的加快，農(nóng)業(yè)在三大產(chǎn)業(yè)中的比重逐年下降，但是農(nóng)業(yè)與其他產(chǎn)業(yè)的發(fā)展卻更加緊密。作為一個(gè)弱勢(shì)產(chǎn)業(yè)，農(nóng)業(yè)需要通過(guò)工業(yè)化的發(fā)展為其提供支持，同時(shí)國(guó)家也在積極采取措施解決“三農(nóng)”問(wèn)題。農(nóng)業(yè)經(jīng)濟(jì)的發(fā)展需要國(guó)家及金融機(jī)構(gòu)部門加大對(duì)農(nóng)業(yè)的資金投入力度，，從而使得農(nóng)業(yè)信貸項(xiàng)目得到有效的支持和良好的發(fā)展。信貸業(yè)務(wù)是商業(yè)銀行的一項(xiàng)傳統(tǒng)業(yè)務(wù)，是目前乃至今后很長(zhǎng)一段時(shí)間內(nèi)商業(yè)銀行的主要利潤(rùn)來(lái)源。然而近幾年我國(guó)農(nóng)業(yè)行業(yè)的信貸違約程度一直居全行業(yè)之首，作為重點(diǎn)扶持農(nóng)業(yè)發(fā)展的商業(yè)銀行而言，對(duì)農(nóng)業(yè)信貸風(fēng)險(xiǎn)的預(yù)警、評(píng)估和防范是其面臨的主要問(wèn)題。如何構(gòu)建適合我國(guó)現(xiàn)實(shí)情況的農(nóng)業(yè)企業(yè)信貸風(fēng)險(xiǎn)評(píng)估模型，提高商業(yè)銀行農(nóng)業(yè)信貸風(fēng)險(xiǎn)管理水平，加強(qiáng)商業(yè)銀行信貸決策，更好地防范和應(yīng)對(duì)信貸風(fēng)險(xiǎn)是本文要解決的主要問(wèn)題。 早期的信貸評(píng)估方法比較容易受主觀因素的影響，而神經(jīng)網(wǎng)絡(luò)模型在進(jìn)行信貸風(fēng)險(xiǎn)評(píng)估過(guò)程中，不需要建立模型，可以將定性因素和定量因素綜合考慮，將相關(guān)數(shù)據(jù)輸入神經(jīng)網(wǎng)絡(luò)就能對(duì)數(shù)據(jù)之間的關(guān)系進(jìn)行總結(jié)，并且神經(jīng)網(wǎng)絡(luò)對(duì)數(shù)據(jù)的處理有著良好的自適應(yīng)性以及很強(qiáng)的學(xué)習(xí)、模仿、抗干擾能力，因此神經(jīng)網(wǎng)絡(luò)模型能夠靈活地處理多變量的復(fù)雜環(huán)境，有效地表示出信貸指標(biāo)和信用等級(jí)之間的非線性映射關(guān)系。本文在總結(jié)國(guó)內(nèi)企業(yè)信貸風(fēng)險(xiǎn)評(píng)估文獻(xiàn)的基礎(chǔ)上，運(yùn)用顯著性分析和主成分分析構(gòu)造出農(nóng)業(yè)類上市企業(yè)的信貸風(fēng)險(xiǎn)評(píng)估指標(biāo)，并將神經(jīng)網(wǎng)絡(luò)模型和Matlab軟件工具相結(jié)合，提出了基于BP神經(jīng)網(wǎng)絡(luò)的農(nóng)業(yè)企業(yè)信貸風(fēng)險(xiǎn)評(píng)估模型，并對(duì)模型進(jìn)行調(diào)試，最終結(jié)果顯示神經(jīng)網(wǎng)絡(luò)模型對(duì)農(nóng)業(yè)上市企業(yè)信貸風(fēng)險(xiǎn)評(píng)估的準(zhǔn)確率達(dá)到了85%，具有較高的精度，通過(guò)建立神經(jīng)網(wǎng)絡(luò)信貸風(fēng)險(xiǎn)評(píng)估模型從而為商業(yè)銀行發(fā)放農(nóng)業(yè)類貸款提供依據(jù)，達(dá)到規(guī)避農(nóng)業(yè)企業(yè)信貸風(fēng)險(xiǎn)、降低銀行不良貸款比率、減少銀行經(jīng)營(yíng)成本的目的。
[Abstract]:Agriculture is the basic industry of national economy and plays an important role in the process of economic development of our country. With the acceleration of industrialization and urbanization in China, the proportion of agriculture in the three industries is decreasing year by year, but the development of agriculture and other industries is more closely. As a weak industry, agriculture needs to provide support through the development of industrialization. Meanwhile, the country is also actively taking measures to solve the "three rural" problems. The development of agricultural economy requires the government and financial institutions to increase the investment in agriculture, so that the agricultural credit project can be effectively supported and well developed. Credit is a traditional business of commercial banks, which is the main profit source of commercial banks for a long time. However, in recent years, the credit default degree of agricultural industry in China has been the first in the whole industry. As a commercial bank which focuses on supporting the development of agriculture, the assessment and prevention of agricultural credit risk is the main problem it faces. How to construct the agricultural enterprise credit risk assessment model suitable for the reality of our country, how to improve the agricultural credit risk management level of the commercial bank, strengthen the commercial bank credit decision-making, Better prevention and response to credit risk is the main problem to be solved in this paper. The early credit assessment methods are easily influenced by subjective factors, but the neural network model does not need to establish a model in the process of credit risk assessment, so it can take both qualitative and quantitative factors into account. The relationship between the data can be summarized by inputting the relevant data into the neural network, and the neural network has good adaptability and strong learning, imitation and anti-interference ability to deal with the data. So the neural network model can deal with the complex environment of multivariable flexibly and express the nonlinear mapping relationship between credit index and credit grade effectively. On the basis of summarizing the literature of credit risk assessment of domestic enterprises, this paper constructs the credit risk assessment index of agricultural listed enterprises by using significant analysis and principal component analysis, and combines the neural network model with Matlab software tools. The credit risk assessment model of agricultural enterprises based on BP neural network is put forward, and the model is debugged. The final results show that the accuracy of the neural network model for agricultural listed enterprises' credit risk assessment reaches 855.The model has a high accuracy. Through the establishment of neural network credit risk assessment model to provide the basis for commercial banks to issue agricultural loans to avoid the credit risk of agricultural enterprises reduce the ratio of non-performing loans of banks and reduce the operating costs of banks.
【學(xué)位授予單位】：湖南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP183;F832.43

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