Bids for speed: An empirical study of investment strategy automation in a peer-to-business lending platform

Abstract

We investigate how introducing a bidding agent impacts the process and outcome of an online reverse auction in the context of a crowdlending platform. We consider this issue in the context of a peer-to-business platform that connects individual lenders to small and medium-sized enterprises. Using a before/after study design, we perform an econometric analysis and find that introducing a bidding agent had a positive and dramatic impact on the number of bids and bidders and reduced the time necessary to collect the funds. For projects with lower ratings, it also positively impacted the number of lenders and indirectly enhanced portfolio diversification. We find that after the bidding agent was introduced, well-rated projects benefited from lower interest rates, the magnitude of the change depending positively on their rating. These results provide evidence that the bidding agent generates savings in the screening and bidding costs incurred by lenders and benefits both sides of the platform. Our contribution documents the role of bidding agent as a strategic tool to enhance financial intermediation. It also sheds light on how two types of decision support systems (rating-based and bidding agent) interact and shows that this interaction is of crucial importance with respect to the financial regulation of platforms if the crowd has low financial literacy.