Abstract

This paper demonstrates that it is possible to construct accurate pictures of firm revenue, growth, geographic dispersion, and customer base characteristics using an increasingly accessible class of consumer financial transaction data. We develop two new measures which characterize firms' customer bases: the rate of churn in a firm's customer base and a metric of the pairwise similarity between firms' customer bases. We show that these measures provide important insights into the behavior of both real firm decisions and firm asset prices. Rates of customer churn affect the level and volatility of firm-level investment, markups, and profits. Churn also affects how quickly firms respond to shocks in the value of their growth options (i.e. Tobin's~Q). Moreover, high churn firms tended to face steeper declines in consumer spending during the recent COVID-19 outbreak. Similarity between firms' customer bases highlights one under-explored type of predictability among stock returns -- we demonstrate that significant alpha can be generated using a trading strategy that exploits our index of customer base similarity across firms.

Full Text

Published Version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.