Background and Motivation
As an emerging form of microfinance, online peer-to-peer (P2P) lending helps individual lenders and borrowers perform transactions directly on the Internet. Due to the existence of information asymmetry (Freedman & Jin, 2011; Massa & Simonov, 2006; Yum et al., 2012) and moral hazard (Arnott & Stiglitz, 1991; Pointner & Raunig, 2018) in P2P lending, many scholars are attracted to the study of P2P lending. There are two main streams of research. One explores how borrowers’ information affects lenders’ investment decisions. Borrowers’ financial information has been confirmed to relate significantly to funding success (Herzenstein et al., 2008), and their credit ratings have a strong effect on interest rates (Klafft, 2008). Loan amount and interest rate have a negative impact on the funding success ratio (Puro et al., 2010, Zhang & Liu, 2012). Information such as loan description and borrower’s picture can alleviate information asymmetry (Wang et al., 2019, Liang & He, 2020). The other research stream focuses on the behavior of lenders in P2P lending. Herding behavior has been confirmed in P2P lending, as potential lenders are more likely to fund loans that have more prior lenders (Zhang & Liu, 2012, Herzenstein et al., 2011). The amount funded by prior lenders is also an important attribute valued by potential lenders (Lee & Lee, 2012; Liu et al., 2015). Lenders might bid in the latter stage of a loan period to minimize the opportunity cost of a failed loan (Ceyhan et al., 2011).
P2P lending platforms are facing fierce competition to attract lenders and provide better investment opportunities. Based on massive transaction data and sophisticated knowledge management methods (Roblek et al., 2014), P2P platforms are trying to lure and retain customers with innovative services. Nowadays, more and more P2P lending platforms (e.g., Prosper, LendingClub, and PPDai) are offering automated investment services to lenders. By using such an automated service, a lender can set his/her personal investment criteria including the borrower’s credit rating, the loan’s interest rate, and investment amount, and the automated investment tool will execute accordingly (Ceyhan et al., 2011). The automated investment tool can thus relieve some of the lender’s burden of continuously looking for better investment projects, but the purpose of our study is to ascertain whether there are any other impacts. The authors want to find out how automated investment in P2P lending affects lender behavior, lender return on investment (ROI), and platform operation efficiency.