浙江省網(wǎng)絡(luò)借貸平臺經(jīng)營風險的影響因素研究
發(fā)布時間:2018-07-23 08:34
【摘要】:經(jīng)營風險因素的預測是金融領(lǐng)域的重要研究課題之一,公司運營的經(jīng)營風險困境既關(guān)系到公司的戰(zhàn)略制定,也會間接波及到投資者的投資情緒,因此正確的預測企業(yè)的經(jīng)營風險因素具有重要的現(xiàn)實意義。網(wǎng)絡(luò)借貸平臺陷入經(jīng)營困境是一個循序漸進的過程,由公司經(jīng)營的問題逐漸惡化到了財務(wù)問題,直至破產(chǎn)和跑路清算。本論文試圖采用網(wǎng)絡(luò)借貸平臺經(jīng)營數(shù)據(jù)和引用實證模型,為了增加網(wǎng)絡(luò)借貸平臺經(jīng)營者對經(jīng)營風險重視和提高網(wǎng)絡(luò)借貸平臺投資者對投資風險的關(guān)注。本研究目的在于通過網(wǎng)絡(luò)借貸平臺的研究為平臺經(jīng)營者降低平臺經(jīng)營風險提供對策,并為平臺投資者識別平臺經(jīng)營風險提供建議。對風險影響因素依次進行分析,并提出相應(yīng)解決建議,為我國網(wǎng)絡(luò)借貸平臺參與者和監(jiān)管者們提供一些力所能及的借鑒,從而能夠讓網(wǎng)絡(luò)借貸平臺發(fā)揮出其信用中介作用。為促進網(wǎng)絡(luò)借貸平臺行業(yè)健康穩(wěn)定發(fā)展,作為補充支持實體經(jīng)濟的發(fā)展。本文的研究框架如下:首先,分析國內(nèi)外網(wǎng)絡(luò)借貸平臺的發(fā)展歷程和所面臨的風險,為網(wǎng)絡(luò)借貸平臺經(jīng)營風險的影響因素研究做出鋪墊。論文著重研究網(wǎng)絡(luò)借貸行業(yè)國內(nèi)外和浙江省發(fā)展具體情況和風險因素,并且重點分析了我國目前網(wǎng)絡(luò)借貸平臺現(xiàn)狀,分析網(wǎng)絡(luò)借貸平臺運營的經(jīng)營風險因素。其次,重點研究浙江省范圍以內(nèi)的網(wǎng)絡(luò)借貸平臺經(jīng)營風險、選取306家網(wǎng)絡(luò)借貸平臺為樣本;在樣本范圍內(nèi)重點收集網(wǎng)絡(luò)借貸平臺經(jīng)營的十個變量,分析網(wǎng)絡(luò)借貸平臺經(jīng)營風險的影響;引入二元Logistic模型,通過二元Logistic模型分析網(wǎng)絡(luò)借貸平臺經(jīng)營風險因素的影響變量。再次,根據(jù)二元Logistic實證模型的結(jié)果做出研究結(jié)論,網(wǎng)絡(luò)借貸平臺正常運營與平臺擁有資金托管、債權(quán)轉(zhuǎn)讓、風險準備金、低于15%的收益率、抵押質(zhì)押產(chǎn)品正相關(guān);網(wǎng)絡(luò)借貸平臺正常運營與平臺擁有保證擔保產(chǎn)品、信用產(chǎn)品負相關(guān);網(wǎng)絡(luò)借貸平臺正常運營與地區(qū)、股東背景、投資期限、公司人數(shù)規(guī)模無關(guān)。實證分析網(wǎng)絡(luò)借貸平臺經(jīng)營風險因素的同時,并對政府的監(jiān)管政策提出了建議。第一、建立網(wǎng)絡(luò)借貸平臺風險準備金;第二、建立網(wǎng)絡(luò)借貸平臺資金托管;第三、建立網(wǎng)絡(luò)借貸平臺財產(chǎn)保險;第四、建立強制信息披露機制。最后并提出了文章的不足與創(chuàng)新點,并對我國網(wǎng)絡(luò)借貸平臺進行了研究展望。本文的創(chuàng)新之處在于通過以往學者研究基礎(chǔ)之上,研究浙江省網(wǎng)絡(luò)借貸平臺行業(yè)的經(jīng)營風險因素,并運用浙江省網(wǎng)絡(luò)借貸平臺的十組變量數(shù)據(jù)進行實證。
[Abstract]:The prediction of operational risk factors is one of the important research topics in the financial field. The plight of operating risk is not only related to the company's strategic formulation, but also indirectly affects the investor's investment sentiment. Therefore, it is of great practical significance to correctly predict the business risk factors of enterprises. It is a gradual and gradual process that the network loan platform falls into trouble. The problem of the company's operation has gradually deteriorated to the financial problem, until it went bankrupt and ran away from liquidation. This paper attempts to use the online lending platform management data and empirical model to increase the network lending platform operators to pay attention to the operating risk and to enhance the network lending platform investors' attention to the investment risk. The purpose of this study is to provide countermeasures for platform operators to reduce platform operating risks and provide suggestions for platform investors to identify platform operating risks through the research of online lending platform. The paper analyzes the risk influencing factors in turn, and puts forward corresponding solutions, which can be used as a reference for the participants and regulators of the online lending platform in our country, so that the online lending platform can play its role as a credit intermediary. In order to promote the healthy and stable development of online lending platform industry, as a supplement to support the development of the real economy. The research framework of this paper is as follows: firstly, this paper analyzes the development history and the risks faced by the domestic and foreign online lending platforms, which pave the way for the research on the influencing factors of the operating risks of the online lending platforms. This paper focuses on the specific situation and risk factors of the development of online lending industry at home and abroad and Zhejiang Province, and focuses on the analysis of the current situation of China's network lending platform and the operational risk factors of the operation of the network lending platform. Secondly, the paper focuses on the risk of online lending platform in Zhejiang Province, and selects 306 online lending platforms as samples. In the sample scope, it focuses on collecting ten variables of network lending platform operation. This paper analyzes the influence of the operating risk of the network lending platform, introduces the binary Logistic model, and analyzes the influence variables of the operating risk factor of the network loan platform through the dual logistic model. Thirdly, according to the results of the binary Logistic empirical model, the conclusion is that the normal operation of the online lending platform is positively related to the platform's ownership of capital custody, creditor's rights transfer, risk reserve, yield below 15%, and mortgage pledge product. The normal operation of the network lending platform is negatively related to the guarantee product and the credit product, and the normal operation of the network loan platform has nothing to do with the region, the background of shareholders, the duration of investment, and the size of the company. This paper empirically analyzes the risk factors of network lending platform, and puts forward some suggestions on the government's supervision policy. First, the establishment of network lending platform risk reserve; second, the establishment of network lending platform fund custody; third, the establishment of network lending platform property insurance; fourth, the establishment of mandatory information disclosure mechanism. Finally, the paper puts forward the deficiency and innovation of the article, and looks forward to the research of China's network lending platform. The innovation of this paper lies in the research on the risk factors of the network lending platform industry in Zhejiang Province based on the previous scholars' research, and the empirical analysis is carried out by using the ten sets of variables of the network lending platform of Zhejiang Province.
【學位授予單位】:浙江財經(jīng)大學
【學位級別】:碩士
【學位授予年份】:2017
【分類號】:F724.6;F832.4
[Abstract]:The prediction of operational risk factors is one of the important research topics in the financial field. The plight of operating risk is not only related to the company's strategic formulation, but also indirectly affects the investor's investment sentiment. Therefore, it is of great practical significance to correctly predict the business risk factors of enterprises. It is a gradual and gradual process that the network loan platform falls into trouble. The problem of the company's operation has gradually deteriorated to the financial problem, until it went bankrupt and ran away from liquidation. This paper attempts to use the online lending platform management data and empirical model to increase the network lending platform operators to pay attention to the operating risk and to enhance the network lending platform investors' attention to the investment risk. The purpose of this study is to provide countermeasures for platform operators to reduce platform operating risks and provide suggestions for platform investors to identify platform operating risks through the research of online lending platform. The paper analyzes the risk influencing factors in turn, and puts forward corresponding solutions, which can be used as a reference for the participants and regulators of the online lending platform in our country, so that the online lending platform can play its role as a credit intermediary. In order to promote the healthy and stable development of online lending platform industry, as a supplement to support the development of the real economy. The research framework of this paper is as follows: firstly, this paper analyzes the development history and the risks faced by the domestic and foreign online lending platforms, which pave the way for the research on the influencing factors of the operating risks of the online lending platforms. This paper focuses on the specific situation and risk factors of the development of online lending industry at home and abroad and Zhejiang Province, and focuses on the analysis of the current situation of China's network lending platform and the operational risk factors of the operation of the network lending platform. Secondly, the paper focuses on the risk of online lending platform in Zhejiang Province, and selects 306 online lending platforms as samples. In the sample scope, it focuses on collecting ten variables of network lending platform operation. This paper analyzes the influence of the operating risk of the network lending platform, introduces the binary Logistic model, and analyzes the influence variables of the operating risk factor of the network loan platform through the dual logistic model. Thirdly, according to the results of the binary Logistic empirical model, the conclusion is that the normal operation of the online lending platform is positively related to the platform's ownership of capital custody, creditor's rights transfer, risk reserve, yield below 15%, and mortgage pledge product. The normal operation of the network lending platform is negatively related to the guarantee product and the credit product, and the normal operation of the network loan platform has nothing to do with the region, the background of shareholders, the duration of investment, and the size of the company. This paper empirically analyzes the risk factors of network lending platform, and puts forward some suggestions on the government's supervision policy. First, the establishment of network lending platform risk reserve; second, the establishment of network lending platform fund custody; third, the establishment of network lending platform property insurance; fourth, the establishment of mandatory information disclosure mechanism. Finally, the paper puts forward the deficiency and innovation of the article, and looks forward to the research of China's network lending platform. The innovation of this paper lies in the research on the risk factors of the network lending platform industry in Zhejiang Province based on the previous scholars' research, and the empirical analysis is carried out by using the ten sets of variables of the network lending platform of Zhejiang Province.
【學位授予單位】:浙江財經(jīng)大學
【學位級別】:碩士
【學位授予年份】:2017
【分類號】:F724.6;F832.4
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