@article{Zeng2017,
abstract = {{\textcopyright} 2017 Zeng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Peer-to-peer (P2P) lending, as a novel economic lending model, has triggered new challenges on making effective investment decisions. In a P2P lending platform, one lender can invest N loans and a loan may be accepted by M investors, thus forming a bipartite graph. Basing on the bipartite graph model, we built an iteration computation model to evaluate the unknown loans. To validate the proposed model, we perform extensive experiments on real-world data from the largest American P2P lending marketplace - Prosper. By comparing our experimental results with those obtained by Bayes and Logistic Regression, we show that our computation model can help borrowers select good loans and help lenders make good investment decisions. Experimental results also show that the Logistic classification model is a good complement to our iterative computation model, which motivates us to integrate the two classification models. The experimental results of the hybrid classification model demonstrate that the logistic classification model and our iteration computation model are complementary to each other. We conclude that the hybrid model (i.e., the integration of iterative computation model and Logistic classification model) is more efficient and stable than the individual model alone.},
author = {Zeng, Xiangxiang and Liu, Li and Leung, Stephen and Du, Jiangze and Wang, Xun and Li, Tao},
doi = {10.1371/journal.pone.0184242},
file = {:D$\backslash$:/Nuts/Reference/A decision support model for investment on P2P lending platform - Zeng et al. - 2017.pdf:pdf;:D$\backslash$:/Nuts/Reference/A decision support model for investment on P2P lending platform - Zeng et al. - 2017(2).pdf:pdf},
isbn = {1111111111},
issn = {19326203},
journal = {PLoS ONE},
number = {9},
pages = {1--18},
title = {{A decision support model for investment on P2P lending platform}},
volume = {12},
year = {2017}
}
@article{EINAV2010,
author = {Einav, Liran And Cullen, Amy Finkelstein Mark R.},
file = {:D$\backslash$:/Nuts/Reference/Estimating Welfare in Insurance Markets Using Variation in Prices - EINAV, CULLEN - 2010.pdf:pdf},
isbn = {1932-6203 (Electronic) 1932-6203 (Linking)},
issn = {19326203},
journal = {Quarterly Journal of Economics},
number = {August},
pages = {877--921},
pmid = {24710357},
title = {{Estimating Welfare in Insurance Markets Using Variation in Prices}},
volume = {CXXV},
year = {2010}
}
@article{Veiga2018,
abstract = {Would consumer surplus increase if annuity rates were age-neutral? This paper characterizes the socially optimal contractibility of a given signal in markets with adverse selection. A signal (e.g., age) partitions consumers into subsets (e.g., young and old). A regulator restricts price-discrimination on the basis of the signal if the consumer subsets where the level of cost is higher are also the subsets where there is greater deadweight loss due to adverse selection. Such signals are empirically common. To illustrate the welfare benefit of price discrimination policy, I use a structural model to estimate its impact on the UK annuities market. The model is estimated using proprietary data that include the annuity seller's estimate of each individual's longevity. I find that restricting price discrimination can increase consumer surplus by the equivalent of about {\pounds}6.5 million per year.},
author = {Veiga, Andr{\'{e}}},
file = {:D$\backslash$:/Nuts/Reference/The Impact of Price Discrimination in Markets with Adverse Selection - Veiga - 2018.pdf:pdf},
keywords = {Adverse Selection,D41,L52,Price Discrimination,Structural Estimation JEL Classification Codes: D8},
title = {{The Impact of Price Discrimination in Markets with Adverse Selection}},
year = {2018}
}

@article{Chen2018,
abstract = {We use data from a major peer-to-peer lending marketplace in China to study whether female and male investors evaluate loan performance differently. Controlling for variables of investor demographics, investor experience, and loan characteristics, we find that loans invested by female investors are more likely to default and have lower loan return in the future than loans invested by male investors. We define abnormal default or abnormal loan return as the part of the loan default or the part of loan return that is not explained by loan characteristics and find that the loans invested by female investors have higher abnormal default and lower abnormal loan return than the loans invested by male investors. Furthermore, female investors perform similarly to male investors in abnormal default or abnormal loan return when investors have high levels of education or income or when investors work in finance or information technology industries.},
author = {Chen, Jia and Jiang, Jiajun and jane Liu, Yu},
doi = {10.1016/j.jempfin.2018.06.004},
file = {:D$\backslash$:/Nuts/Reference/Financial literacy and gender difference in loan performance - Chen, Jiang, Liu - 2018.pdf:pdf},
issn = {09275398},
journal = {Journal of Empirical Finance},
keywords = {Financial Literacy, Gender Difference, Loan Default, Loan Return,Peer-to-peer Lending},
number = {71673007},
pages = {307--320},
title = {{Financial Literacy and Gender Difference in Loan Performance}},
volume = {48},
year = {2018}
}

@article{Havrylchyk2018,
abstract = {The objective of our paper is to explore the role of P2P lending platforms through the prism of the theory of financial intermediation. P2P lending platforms perform the brokerage function of financial intermediaries by matching lenders' supply and borrowers' demand of funding, according to the risk and the maturity of their needs. Unlike banks, P2P lending platforms do not create money and do not perform risk and maturity transformation. However, they can organize secondary markets to trade loan contracts before maturity and some P2P lending platforms aim at providing a fixed income to lenders. To ensure efficient and sustainable financial intermediation, P2P lending platforms need to ensure that they are not subject to principal-agent problems and that their incentives coincide with those of lenders. The possibility of orderly resolution of P2P lending platforms failures might decrease moral hazard problems that are inherent in the modern financial intermediation.},
author = {Havrylchyk, Olena and Verdier, Marianne},
doi = {10.1057/s41294-017-0045-1},
file = {:D$\backslash$:/Nuts/Reference/The Financial Intermediation Role of the P2P Lending Platforms - Havrylchyk, Verdier - 2018.pdf:pdf},
issn = {14783320},
journal = {Comparative Economic Studies},
keywords = {Access to finance,Financial crisis,Information and communication technologies,Internet,Market structure,Online lenders,Peer-to-peer lending},
number = {1},
pages = {115--130},
publisher = {Palgrave Macmillan UK},
title = {{The Financial Intermediation Role of the P2P Lending Platforms}},
url = {https://doi.org/10.1057/s41294-017-0045-1},
volume = {60},
year = {2018}
}

