Abstract
Empirical research has shed little light on the nature of bank formation as a banking behavior in an unregulated setting, due to the lack of observational data. On the other hand, recent years have witnessed the increasing popularity of peer-to-peer lending platforms which connect borrowers to lenders. An interesting observation is that some users are conducting micro banking activities, freely performing dual roles as both borrowers and lenders. They are referred to as microbanks. The microbanks face few regulatory restrictions or supervisory powers. Seizing this opportunity, we empirically examine the dynamics of free entry behaviors, using a sample of unregulated microbanks from one of the largest online peer-to-peer lending platforms in China. In particular, we explore the formation of microbanks at monthly intervals. Further, we create a quasi-experiment by leveraging the fact that the exact date to receive a repayment is exogenous to the microbanks. We find that a positive liquidity shock is positively associated with microbank formation.
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Notes
- 1.
Refer to FDIC website: https://www.fdic.gov/bank/statistical/stats/2020mar/fdic.pdf
- 2.
Remarks by FDIC Chairman Martin J. Gruenberg at the FDIC Community Banking Conference, “Strategies for Long-Term Success,” Arlington, VA. https://www.fdic.gov/news/speeches/spapr0616.html
- 3.
There was a lack of well-established credit bureau scores in China during the sample period of this study.
- 4.
It is equivalent to US$ 7.2, based on the currency exchange rate on December 31, 2016.
- 5.
The sample size can be increased by relaxing the time period requirement. For example, 20,755 platform users can be identified as microbanks on a yearly basis, while our sample consists of 6172 microbanks defined at the monthly level. Although the latter is smaller in size, it allows us to explore the time-varying behaviors throughout 2016.
- 6.
Due to data constraints, we do not explicitly predict for the individual’s entry or exit behavior, which is in the form of conditional probability. Both entry and exit rates are conditional on the behavior of microbanks in the previous month, leading to a reduced sample size for conditional prediction.
- 7.
The earlier and later data (in January and December in 2016) are discarded due to the lack of loan repayment behaviors and the truncation on the lead of dependent variable respectively.
- 8.
To identify the structural parameters, a total of 6170 observations of 617 microbanks with non-varying response are dropped.
- 9.
- 10.
The recovery rate (i.e. the proportion recovered by the lender when the borrower defaults) is also important. However, in this study, we examine a simplified scenario by assuming a zero recovery rate.
- 11.
For simplicity, we focus on the first occurrence of the shock in this study.
- 12.
The original data is in long form. Given a month τ, we only consider the predictors prior to t = τ. We transform the data into wide form and run a simple logit regression to estimate the score. To impose a requirement that isBank i,τ is the same for a matched pair, we run the matching process separately on the two subsets of data where isBank i,τ = 1 and isBank i,τ = 0 separately.
- 13.
We vary the cutoffs such as 33%, 50% and 66%. The size of treatment group is decreasing, but the results are almost consistent.
- 14.
We try larger cutoffs such as 10%. The size of the treatment group is smaller, but the results are consistent. However, when the cutoff is even larger, the size will be reduced dramatically. For example, when the cutoff is 25%, only 17% of original sample receives a negative shock.
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Acknowledgments
We thank Dr. Xuesong Lu for providing valuable suggestions for data cleaning. We gratefully acknowledge the funding support from the Singapore Ministry of Education (Grant R-252-000-A08-112).
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Keppo, J., Phan, T.Q., Tan, T. (2022). Microbanks in Online Peer-to-Peer Lending: A Tale of Dual Roles. In: Babich, V., Birge, J.R., Hilary, G. (eds) Innovative Technology at the Interface of Finance and Operations. Springer Series in Supply Chain Management, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-81945-3_9
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