by Hongchang Wang and Eric Overby (preliminary manuscript completed; target journal Information System Research)
Abstract: Algorithmic trading has reshaped equity markets and had significant effects on market performance. We examine the effect of algorithmic trading in online peer-to-peer lending markets. As the “peer-to-peer” label suggests, these markets were originally designed to be accessible to individual investors. However, because algorithmic trading is typically used by institutional investors with substantial resources, advances in algorithmic trading threaten to shut individual investors out of the market. Ironically, this could exacerbate inequalities in the financial system that peer-to-peer lending markets were designed to help eliminate. To study the effects of algorithmic trading, we examine the effect of an API upgrade on Prosper.com that facilitated algorithmic trading. Using a difference-in-differences strategy, we find that individual “manual” investors were crowded out of the most quickly-funded and typically best-performing loans after the API upgrade. However, the API upgrade may have increased the size of the market, thereby allowing individual investors to continue investing in the market, albeit for somewhat lower quality loans. Our study contributes to several emerging research areas, including online lending, algorithmic trading, data-driven decision making, and the effect of technology on social and financial inequality.
by Hongchang Wang and Eric Overby (Accepted for ICIS presentation; to be submitted to Management Information Systems Quarterly)
Abstract: Over the last several years, the United States has become increasingly polarized politically. We study whether political differences inhibit market efficiency by examining whether investors in online lending markets are less likely to lend to borrowers whose political ideology (i.e., liberal or conservative) is likely to be different from their own. We leverage state-level legalization of same-sex marriage as a natural experiment to investigate how investors in online lending markets respond to this signal of a state’s “liberalness”. Results of a difference-in-differences analysis show that: (1) investors make more bids (i.e., loan offers) to borrowers in states that legalize same-sex marriage in the days immediately after passage of the law; and (2) investors from relatively liberal states contribute more to this increase than do investors from relatively conservative states. This suggests that political ideology influences lending decisions in online lending markets, potentially preventing beneficial investor/borrower matches from being formed. To test the generalizability of these findings, we use all U.S. states and measure the number of bids from investors in each state to borrowers in each state. We use a gravity model to examine how political differences across states influence bidding behaviors. Results are consistent with the difference-in-differences analysis. Borrowers are more likely to attract investors from politically similar states than investors from politically dissimilar states. Given the fast growth of online lending (and the sharing economy more broadly) as well as the rapid increase in political polarization, understanding the impact of political differences on market outcomes yields important theoretical and practical implications.