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  本文關(guān)鍵詞：商業(yè)銀行知識產(chǎn)權(quán)質(zhì)押貸款風(fēng)險預(yù)警研究　出處：《天津財經(jīng)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 知識產(chǎn)權(quán)質(zhì)押貸款 風(fēng)險預(yù)警指標(biāo) BP神經(jīng)網(wǎng)絡(luò) 風(fēng)險預(yù)警模型

【摘要】：隨著知識經(jīng)濟(jì)的迅猛發(fā)展,知識產(chǎn)權(quán)質(zhì)押貸款為企業(yè)加速科技成果資本化、拓展融資渠道提供了新的途徑。2008年以來,政府為促進(jìn)該項業(yè)務(wù)的開展先后出臺了各項辦法與指導(dǎo)意見,扶持高新技術(shù)企業(yè)利用知識產(chǎn)權(quán)獲得貸款,但作為該項業(yè)務(wù)的主角——商業(yè)銀行卻因無法控制知識產(chǎn)權(quán)自身帶來的高風(fēng)險而謹(jǐn)慎行事。這不僅不利于商業(yè)銀行在知識經(jīng)濟(jì)時代中積極調(diào)整原有擔(dān)保結(jié)構(gòu),搶占新市場,更不利于整個經(jīng)濟(jì)市場上知識產(chǎn)權(quán)資本的增值,對科技創(chuàng)新產(chǎn)生一定的負(fù)面效應(yīng)。 如何才能合理預(yù)測知識產(chǎn)權(quán)質(zhì)押貸款的風(fēng)險呢?文章針對商業(yè)銀行面臨的這一棘手問題展開關(guān)于知識產(chǎn)權(quán)質(zhì)押貸款風(fēng)險預(yù)警的研究。一般來說風(fēng)險預(yù)警研究主要包括風(fēng)險識別和風(fēng)險評估兩大部分,文章主要從四個方面進(jìn)行詳細(xì)闡述。第一,知識產(chǎn)權(quán)質(zhì)押貸款風(fēng)險理論主要由有限性理論、新經(jīng)濟(jì)增長理論、不完全契約理論、預(yù)期收入理論、信息不對稱理論五大部分組成,這五大理論從不同角度為風(fēng)險成因提供了理論支持。第二,從宏觀和微觀兩個層面對知識產(chǎn)權(quán)質(zhì)押貸款風(fēng)險影響因素進(jìn)行識別并建立了囊括準(zhǔn)入性指標(biāo)與判定性指標(biāo)的風(fēng)險預(yù)警指標(biāo)體系。經(jīng)過研究發(fā)現(xiàn),知識產(chǎn)權(quán)自身的無形性與獨特性是產(chǎn)生高風(fēng)險的根源,同時知識產(chǎn)權(quán)的法律屬性導(dǎo)致商業(yè)銀行可能遭受不可逆轉(zhuǎn)的損失。第三,通過對已有風(fēng)險預(yù)警模型的對比發(fā)現(xiàn),BP神經(jīng)網(wǎng)絡(luò)是一個具有較強的自適應(yīng)性、自我學(xué)習(xí)能力和容錯性的非線性動態(tài)模型,適合對知識產(chǎn)權(quán)質(zhì)押貸款業(yè)務(wù)的風(fēng)險進(jìn)行評估。第四,文章采用主成分分析法對知識產(chǎn)權(quán)質(zhì)押風(fēng)險狀況進(jìn)行判定,借鑒著名的“3σ”法則確定警度標(biāo)準(zhǔn),構(gòu)建了基于LM算法的三層BP神經(jīng)網(wǎng)絡(luò)模型,并對上市商業(yè)銀行的數(shù)據(jù)進(jìn)行訓(xùn)練與測試,實驗結(jié)果表明該模型的準(zhǔn)確率達(dá)到95%,能夠有效的為商業(yè)銀行知識產(chǎn)權(quán)質(zhì)押貸款業(yè)務(wù)的風(fēng)險進(jìn)行預(yù)警。
[Abstract]:With the rapid development of knowledge economy, intellectual property mortgage loan has provided a new way for enterprises to accelerate the capitalization of scientific and technological achievements and expand financing channels. Since 2008. In order to promote the development of the business, the government has issued various measures and guidance to support high-tech enterprises to use intellectual property rights to obtain loans. However, as the protagonist of this business, commercial banks act prudently because they cannot control the high risks brought about by intellectual property rights, which is not only unfavorable for commercial banks to actively adjust the original guarantee structure in the era of knowledge economy. Seizing the new market is not conducive to the appreciation of intellectual property capital in the whole economic market, and has a certain negative effect on scientific and technological innovation. How can we reasonably predict the risk of intellectual property mortgage loans? In this paper, commercial banks face the thorny problem of intellectual property mortgage loan risk warning research. Generally speaking, risk early warning research mainly includes two parts: risk identification and risk assessment. Firstly, the risk theory of intellectual property mortgage loan mainly consists of finiteness theory, new economic growth theory, incomplete contract theory and expected income theory. The information asymmetry theory consists of five parts. These five theories provide theoretical support for the causes of risk from different angles. Second. This paper identifies the risk factors of intellectual property mortgage loan from macro and micro levels and establishes a risk early warning index system which includes access index and judgment index. The intangibility and uniqueness of intellectual property itself is the source of high risk. Meanwhile, the legal attribute of intellectual property rights may lead to irreversible losses to commercial banks. By comparing the existing risk warning models, it is found that BP neural network is a nonlinear dynamic model with strong self-adaptability, self-learning ability and fault tolerance. It is suitable to evaluate the risk of intellectual property mortgage loan business. 4th, this paper uses principal component analysis method to judge the risk of intellectual property mortgage, and uses the famous "3 蟽" rule to determine the alarm standard. A three-layer BP neural network model based on LM algorithm is constructed, and the data of listed commercial banks are trained and tested. The experimental results show that the accuracy of the model reaches 95%. It can effectively warn the risk of intellectual property mortgage loan business of commercial banks.
【學(xué)位授予單位】：天津財經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：F832.4

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