Lending Club: Empirical Analysis of Online Peer-to-Peer Lending, Asymmetric Information and Risk PreferenceTools Stylanides, Michalis (2015) Lending Club: Empirical Analysis of Online Peer-to-Peer Lending, Asymmetric Information and Risk Preference. [Dissertation (University of Nottingham only)]
AbstractThe recent banking crisis and increased regulations in US, generated substantial challenges in the traditional finance markets which are now characterised for the lack of consumers’ confidence and weaknesses to provide loans for necessary growth. The evolution of the internet and open availability of information have facilitated the rapid growth of alternative sources of finance, such as peer-to-peer lending markets, which are able to gather borrowers and investors under a single online lending platform. While peer-to-peer organisations have been regarded as cost-effective, profitable and innovative, they have also been criticised for the lack of individuals’ lenders expertise and the organisations’ lack of lending experience. This aim of this paper is to empirically examine the industry of online unsecured lending and whether inexperienced lenders that operate within the Lending Club platform are subject to information asymmetries. To meet our aim, the objectives are to (i) Provide an empirical analysis of peer-to-peer market, (ii) Understand whether inexperienced lenders can operate effectively in the online environment of Lending Club, (iii) Evaluate whether Lending Club’s lenders can infer borrower creditworthiness prior to any investment and (iv) Obtain evidence whether information asymmetries do exist within Lending Club’s operations. We examine our aforesaid objectives, using a quantitative approach and following a methodology that monitors the interdependence between borrower-specific characteristics, auction decision variables and their effects on loan performance. Using a large sample of successfully funded loans, our evidence suggest that borrower characteristics can be used as signals by lenders to observe possible risks associated with their investment, as well as, to improve the return on investment. Furthermore, borrowers are substantially evaluated on their financial strength and significant predictors of borrower creditworthiness are highly associated with traditional credit financing variables. Our methodology also provides evidence of adverse selection in the Lending Club platform which is effectively minimised over time through more thorough credit screening processes and more borrower information towards investors. The research concludes that Lending Club evolves towards a viable online lending platform which can facilitate credit to small borrowers. Moreover, experienced and inexperienced investors can gain from profitable investments in the long term – when compared to alternative traditional investments – given that they evaluate, control and monitor borrower attributes effectively.
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