AJ Chen

By leveraging machine-learning algorithms and using nontraditional digital data derived primarily from borrowers’ mobile devices, digital lenders have vastly expanded access to credit in developing economies for millions of individuals without a prior credit history. At the same time, short-term, high-interest-rate digital loans are often issued to borrowers with low financial literacy and are subject to weak consumer protection, raising concerns over predatory lending practices. To examine how access to digital credit influences borrowers’ financial well-being, we take advantage of a proprietary data provided by a large digital lender in Kenya that randomly approves loan applications that would have otherwise been rejected based on the borrower’s credit profile. We find that access to digital credit enhances the financial well-being of borrowers across a variety of mobile-phone-based well-being measures, including their monetary transaction volume, mobility, and social networks, as well as borrowers’ self-reported income and employment. Additionally, we provide some supporting evidence that access to digital credit leads to a greater improvement in financial well-being when borrowers have limited access to credit, take loans for business purposes, and obtain more credit. Overall, we demonstrate that access to short-term, high-interest-rate digital loans can benefit borrowers in developing economies.

Under Review

We examine the link between product life cycles and disclosure using a novel 4-stage text-based life cycle model and report two main results. Firms with peers having mature-stage products disclose more, consistent with collaborative disclosure across peers to deter entry and improve market stability. In addition to disclosing more, these firms also have higher profits, lower risk, and mention fewer disruption concerns. In contrast, the standard link between competition and less disclosure is significantly amplified when industry peers are in the product development stage of the life cycle, as product placements are not yet public and are important to conceal. Our product life cycle results are new to the literature, have large economic magnitudes, and are reinforced by a quasi natural experiment based on patent expirations.

Under Revise and Resubmit

Using over one million Wall Street Journal articles from 1980 to 2020, we create a novel measure of institutional investors’ crowd-sourcing based on their predictions in news media (InstPred). We show that for industries with higher excess InstPred, (i) value and momentum anomaly returns are 34% to 63% larger, and (ii) institutional investors collectively trade the anomalies more aggressively. Our results are reinforced by quasi-exogenous variation in industries’ investor-WSJ connections and cannot be explained by existing measures such as document tone. Our findings are consistent with institutional investors communicating with reputable news media to crowd-source information and expedite price correction.

In Preparation for Submission

Under Revise and Resubmit