Shill bidding in lenders’ eyes? A cross-country study on the influence of large bids in online P2P lending

Dongyu Chen (), Xiaolin Li () and Fujun Lai ()
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Dongyu Chen: Soochow University
Xiaolin Li: Towson University
Fujun Lai: Soochow University

Electronic Commerce Research, 2023, vol. 23, issue 2, No 16, 1089-1114

Abstract: Abstract This research examines the perception of shill bidding in the online peer-to-peer (P2P) lending market by looking into the influence of existing large bids of a loan request (also known as a borrower listing) on the investment decisions of potential lenders who are interested in this listing. We use two panel data sets of funding dynamics of borrower listings from two separate P2P lending platforms, XLending.com in China and YLending.com in the US. We identify the anti-herding effect of large bids using within-loan variations in the amount received in each period, the lagged cumulative number of large bids, and other observable time-varying listing attributes. The analysis reveals that large bids of a listing have a negative impact on the listing’s funding from potential lenders (i.e., the anti-herding effect) both in China and in the U.S. The finding suggests that, when making bidding decisions, lenders in an online P2P lending market are influenced by the perception of Internet shillings. Our analyses also suggest that this anti-herding effect of large bids is moderated by the number of bids. Interestingly, the examination of the association between large bids and ex-post loan default reveals a negative association, indicating the credit signaling functionality of large bids. The evident disparity between the reality (credit signaling of large bids) and bidders’ perception (the presence of large bids perceived as the result of Internet Shilling) illustrates an amplifying erosive effect of dishonest actions on bidders’ trust in information cues from the lending platform.

Keywords: Online peer-to-peer lending; Shilling; Large bids; Cross-country; Shill bidding; Borrower and lender (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10660-021-09503-x

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