本文關(guān)鍵詞： P2P 網(wǎng)絡(luò)借貸 信息不對稱 借款成功率 成交利率 Logistics回歸　出處：《貴州財(cái)經(jīng)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：Peer-to-Peer(P2P)網(wǎng)絡(luò)借貸平臺(tái),顧名思義,是借助發(fā)達(dá)的互聯(lián)網(wǎng)進(jìn)行的資金籌措或投資等活動(dòng)的平臺(tái),它允許借款人通過基于互聯(lián)網(wǎng)的平臺(tái)與其中其他個(gè)人用戶之間直接借貸資金而不需要直接從銀行或者其他傳統(tǒng)途徑的中介機(jī)構(gòu)。由于其操作的便捷性,以及投融資的高效性等優(yōu)點(diǎn),使得這個(gè)新興模式的資金借貸活動(dòng)迅速在全球推廣開來。本文首先對相關(guān)的理論研究的文獻(xiàn)進(jìn)行收集、梳理,闡述P2P網(wǎng)絡(luò)借貸行業(yè)的整體發(fā)展歷程,并結(jié)合理論分析了平臺(tái)存在的信息不對稱問題,導(dǎo)致了P2P網(wǎng)絡(luò)借貸市場中不可避免的存在逆向選擇問題和道德風(fēng)險(xiǎn)問題。新技術(shù)加入并沒有改善這些問題反而加重了交易雙方的信息識別成本,最終會(huì)嚴(yán)重影響P2P網(wǎng)絡(luò)借貸市場的效率,甚至導(dǎo)致P2P網(wǎng)貸平臺(tái)資金調(diào)節(jié)作用的喪失。之后本文借鑒前人的文獻(xiàn)研究結(jié)論,選擇投資人借款意愿與成交利率作為P2P網(wǎng)絡(luò)借貸行為的兩個(gè)衡量指標(biāo),并結(jié)合文獻(xiàn)與人人貸平臺(tái)特點(diǎn),對網(wǎng)絡(luò)借貸行為的影響因素做了假設(shè)與描述性分析。之后文章結(jié)合提出的假設(shè),針對人人貸平臺(tái)設(shè)計(jì)爬蟲程序,利用程序?qū)η拔拇_定的影響因素進(jìn)行數(shù)據(jù)抓取,然后以此數(shù)據(jù)為基礎(chǔ)對投資人出借意愿與成交利率分別作實(shí)證分析檢驗(yàn)。由于出借意愿是衡量借款是否成功的二元變量,因此本文采用logistics回歸分析;對于成交利率的因素影響分析,則采用多元回歸分析方法。最后,結(jié)合實(shí)證分析得到的結(jié)果,得出P2P網(wǎng)絡(luò)借貸平臺(tái)提供的借款人信用信息(信用評級、有無征信報(bào)告等)可以明顯提高網(wǎng)絡(luò)借貸行為的效率;借款人在借貸平臺(tái)提供的基本信息(借款人性別、職業(yè)等)、歷史表現(xiàn)信息等相關(guān)信息對于消除信息不對稱影響比較重要,是區(qū)分優(yōu)質(zhì)與劣質(zhì)借款人的主要影響因素,對這些因素的改進(jìn),可以提高自身的借款成功率并降低最終成交利率。
[Abstract]:Peer-to-PeerP P2P-based lending platform, as its name implies, is a platform for financing or investing with the help of the developed Internet. It allows borrowers to borrow money directly between Internet-based platforms and other individual users of it without requiring intermediaries directly from banks or other traditional channels, because of their ease of operation. As well as the high efficiency of investment and financing, this new model of capital lending activities quickly spread in the world. First of all, the relevant theoretical research literature collection, combing. This paper expounds the whole development course of P2P network lending industry, and analyzes the information asymmetry problem in the platform with the theory. This has led to the inevitable adverse selection and moral hazard problems in the P2P network lending market. The new technology does not improve these problems but increases the information identification costs of both sides of the transaction. Finally, it will seriously affect the efficiency of P2P network lending market, and even lead to the loss of P2P network loan platform funds adjustment. Choosing the willingness of investors to borrow and the transaction interest rate as two indicators of P2P network lending behavior, and combining the literature and the characteristics of peer-to-peer lending platform. This paper makes hypothesis and descriptive analysis on the influencing factors of network lending behavior. Then this paper combines the assumptions put forward and designs a crawler program for peer-to-peer lending platform using the program to capture the data of the influencing factors identified in the previous paper. Then on the basis of this data, the author makes an empirical analysis of the investor's willingness to lend and the transaction interest rate. Because the willingness to lend is a binary variable to measure the success of the loan. Therefore, logistics regression analysis was used in this paper. For the factors affecting the transaction interest rate, the method of multiple regression analysis is used. Finally, combined with the results of empirical analysis, the credit information (credit rating) provided by P2P network lending platform is obtained. Whether there is a credit report, etc.) can significantly improve the efficiency of network lending behavior; The basic information provided by borrowers in the lending platform (borrower gender, occupation, etc., historical performance information and other relevant information in order to eliminate the asymmetric impact of information is more important. The improvement of these factors can improve the success rate of borrowing and reduce the final transaction interest rate.
【學(xué)位授予單位】：貴州財(cái)經(jīng)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：F724.6;F832.4

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