本文選題：網(wǎng)絡(luò)借貸(P2P)　切入點：量化監(jiān)管　出處：《華南理工大學(xué)》2015年碩士論文

【摘要】：網(wǎng)絡(luò)借貸(P2P)作為一種網(wǎng)絡(luò)金融時代下的金融創(chuàng)新模式,對于完善我國金融體系、彌補中小企業(yè)融資缺口以及緩解民間資本投資需求具有重要意義。隨著網(wǎng)絡(luò)借貸(P2P)平臺數(shù)量的劇增,網(wǎng)絡(luò)借貸(P2P)行業(yè)的競爭也日漸激烈,相關(guān)風(fēng)險也在不斷積聚,2013年以來網(wǎng)絡(luò)借貸(P2P)平臺不斷出現(xiàn)詐騙、跑路和體現(xiàn)困難等風(fēng)險事件,大大阻礙行業(yè)發(fā)展。因此,網(wǎng)絡(luò)借貸(P2P)監(jiān)管政策的出臺迫在眉睫,通過監(jiān)管改變現(xiàn)狀,促使行業(yè)健康發(fā)展。但是,網(wǎng)絡(luò)借貸(P2P)作為一個新興行業(yè),監(jiān)管部門應(yīng)給予其更大的發(fā)展空間,避免出現(xiàn)“一管就死”。為此,本文嘗試基于一系列量化方法建立一套適合我國網(wǎng)絡(luò)借貸(P2P)運營特征的量化監(jiān)管體系,為銀監(jiān)會監(jiān)管網(wǎng)絡(luò)借貸(P2P)平臺提供參考。本文在定性分析方面,首先通過一些指標(biāo)數(shù)據(jù)對我國網(wǎng)絡(luò)借貸(P2P)平臺的發(fā)展現(xiàn)狀進行歸納總結(jié),包括平臺的運營情況和運營模式,并指出我國網(wǎng)絡(luò)借貸(P2P)行業(yè)正面臨著法律風(fēng)險、商業(yè)經(jīng)營風(fēng)險、信用違約風(fēng)險等問題。接著,分析國內(nèi)網(wǎng)絡(luò)借貸(P2P)監(jiān)管的發(fā)展,確定我國監(jiān)管層對待網(wǎng)絡(luò)借貸(P2P)這一新興行業(yè)持有鼓勵和支持的態(tài)度,為之后的量化分析奠定基礎(chǔ)。在量化監(jiān)管研究方面,針對我國網(wǎng)絡(luò)借貸(P2P)平臺的風(fēng)險特征,借鑒傳統(tǒng)線下信用評級方法,本文首先運用相關(guān)性分析和主成分分析方法構(gòu)建監(jiān)管評價指標(biāo)體系,并基于我國網(wǎng)絡(luò)借貸(P2P)平臺發(fā)展的特點選取基于熵值修正G1組合賦權(quán)的評價模型進行監(jiān)管評價,以此體現(xiàn)監(jiān)管層的適度監(jiān)管和分類、分級監(jiān)管的原則。熵值修正G1組合賦權(quán)能同時反映客觀數(shù)據(jù)信息和專家意見,由此反映平臺運營特征及監(jiān)管政策,這種組合賦權(quán)方法更適合并能更有效地對我國網(wǎng)絡(luò)借貸(P2P)平臺進行監(jiān)管評價。其次,在對平臺評價結(jié)果的基礎(chǔ)上,建立基于Logistic回歸模型的風(fēng)險預(yù)警模型,通過實證研究證明該模型能有效地對我國網(wǎng)絡(luò)借貸(P2P)平臺的風(fēng)險進行預(yù)測,為監(jiān)管程序提供決策支持。最后,詳細分析了監(jiān)管評價指標(biāo)體系、監(jiān)管評價模型和風(fēng)險預(yù)警模型在實際監(jiān)管過程中的應(yīng)用,以量化手段落實網(wǎng)絡(luò)借貸(P2P)的分類監(jiān)管、適度監(jiān)管、科學(xué)監(jiān)管。
[Abstract]:As a kind of financial innovation mode in the era of network finance, network lending and lending (P2P) can improve the financial system of our country. It is of great significance to make up for the financing gap of small and medium-sized enterprises and to ease the demand for private capital investment. With the rapid increase in the number of online lending platforms, the competition in the online lending and lending industry is becoming increasingly fierce. The risks are also accumulating. Since 2013, there have been a lot of risks such as fraud, running the road and difficulties in implementing the P2P platform, which has greatly hindered the development of the industry. Therefore, the introduction of a regulatory policy on online lending and lending is imminent. Changing the status quo through regulation to promote the healthy development of the industry. However, as a new industry, regulators should give them more room for development and avoid the "death of one tube". Based on a series of quantitative methods, this paper attempts to establish a set of quantitative regulatory system suitable for the characteristics of China's network lending and lending, which provides a reference for the CBRC to supervise the network lending and lending platform. First of all, through some index data, this paper summarizes the current situation of the development of China's network lending platform, including the platform's operation and operation mode, and points out that our country's network lending and lending industry is facing legal risks and business risks. Credit default risk and other issues. Then, by analyzing the development of domestic online lending and lending (P2P) regulation, it is determined that China's regulators have an encouraging and supportive attitude towards the emerging industry of online lending and lending (P2P). In the research of quantitative supervision, aiming at the risk characteristics of our country's network lending platform, we draw lessons from the traditional offline credit rating method. In this paper, we first use the methods of correlation analysis and principal component analysis to construct the regulatory evaluation index system, and based on the characteristics of the development of China's network lending and lending platform, we select the evaluation model based on entropy modified G1 combination weight to conduct regulatory evaluation. According to the principle of appropriate supervision and classification and hierarchical supervision, entropy correction G1 combination weight can reflect both objective data information and expert opinion, and thus reflect platform operation characteristics and regulatory policies. This combination weighting method is more suitable and effective for monitoring and evaluation of China's network lending and lending platform. Secondly, based on the evaluation results of the platform, a risk early warning model based on Logistic regression model is established. It is proved by empirical research that the model can effectively predict the risk of China's network lending and lending platform and provide decision support for the regulatory process. Finally, the monitoring evaluation index system is analyzed in detail. The application of supervision evaluation model and risk warning model in the process of actual supervision, the classification supervision, moderate supervision and scientific supervision of network lending and lending are carried out by quantitative means.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：F724.6;F832.4

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