How Algorithm-based Investment Influences Human Investment: Evidence from a Peer-to-Peer Lending Marketplace



As algorithm-based judgement is often expected to outperform human judgement, algorithms are widely used to assist humans with decision-making and also influence human decision-making. However, extant laboratory experiments find mixed evidences that humans may or may not appreciate algorithm-based judgement. While field evidence is scant, we leverage a unique dataset provided by a leading online peer-to-peer lending marketplace in China, where historical algorithm-based investments are visibly distinguishable from human investments for each loan listing upon individual lenders’ manual investment. We investigate how algorithm-based investment influences manual investment. By using multiple empirical identification strategies, we find consistent evidence that algorithm-based investments positively affect subsequent lenders’ manual investment amount. Specifically, it is the average amount of the observed algorithm-based investments that positively affects the manual investment amount but not the number of the algorithm-based investments. We show that the impact of the observed algorithm-based investment decreases with the lenders’ experiences of manual investment, which is in line with the notion of “algorithm appreciation”. We further explore the underlying mechanism by ruling out several alternative explanations.