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2022 | OriginalPaper | Chapter

Microbanks in Online Peer-to-Peer Lending: A Tale of Dual Roles

Authors : Jussi Keppo, Tuan Q. Phan, Tianhui Tan

Published in: Innovative Technology at the Interface of Finance and Operations

Publisher: Springer International Publishing

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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previous chapter Large-Scale Price Optimization for an Online Fashion Retailer

next chapter FinTech Econometrics: Privacy Preservation and the Wisdom of the Crowd

Refer to FDIC website: https://www.fdic.gov/bank/statistical/stats/2020mar/fdic.pdf

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

There was a lack of well-established credit bureau scores in China during the sample period of this study.

It is equivalent to US$ 7.2, based on the currency exchange rate on December 31, 2016.

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.

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.

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.

To identify the structural parameters, a total of 6170 observations of 617 microbanks with non-varying response are dropped.

Statistics were done using R 3.4.0 (R Core Team, 2017), with the bife (v0.7; Stammann et al., 2020) and survival (v 2.41–3; Therneau et al., 2017) packages.

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.

For simplicity, we focus on the first occurrence of the shock in this study.

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.

We vary the cutoffs such as 33%, 50% and 66%. The size of treatment group is decreasing, but the results are almost consistent.

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.

Adams, R. M., & Gramlich, J. (2016). Where are all the new banks? The role of regulatory burden in new Bank formation. Review of Industrial Organization, 48(2), 181–208. CrossRef

Amel, D. F., & Liang, J. N. (1997). Determinants of entry and profits in local banking markets. Review of Industrial Organization, 12(1), 59–78. CrossRef

Baltensperger, E. (1980). Alternative approaches to the theory of the banking firm. Journal of Monetary Economics, 6(1), 1–37. CrossRef

Bao, Y., Hilary, G., & Ke, B. (2021). Artificial intelligence and fraud detection. In V. Babich, J. Birge, & G. Hilary (Eds.), Innovative technology at the interface of finance and operations (Springer series in supply chain management, forthcoming). Springer Nature.

Barth, J. R., Caprio, G., & Levine, R. (2005). Rethinking bank regulation: Till angels govern. Cambridge University Press. CrossRef

Berger, A. N., Bonime, S. D., Goldberg, L. G., & White, L. J. (2004). The dynamics of market entry: The effects of mergers and acquisitions on entry in the banking industry. Journal of Business, 77(4), 797–834. CrossRef

Besanko, D., & Thakor, A. V. (1992). Banking deregulation: Allocational consequences of relaxing entry barriers. Journal of Banking and Finance, 16(5), 909–932. CrossRef

Boute, R. N., Gijsbrechts, J., & Van Mieghem, J. A. (2021). Digital lean operations: Smart automation and artificial intelligence in financial services. In V. Babich, J. Birge, & G. Hilary (Eds.), Innovative technology at the interface of finance and operations (Springer series in supply chain management, forthcoming). Springer Nature.

Byrum, J. (2021). AI in financial portfolio management: Practical considerations and use cases. In V. Babich, J. Birge, & G. Hilary (Eds.), Innovative technology at the interface of finance and operations (Springer series in supply chain management, forthcoming). Springer Nature.

Cetorelli, N., & Gambera, M. (2001). Banking market structure, financial dependence and growth: International evidence from industry data. Journal of Finance, 56(2), 617–648. CrossRef

Cohen, E. (1997). Athenian economy and society: A banking perspective. Princeton University Press.

Demirguc-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2018). The global Findex database 2017: Measuring financial inclusion and the Fintech revolution. The World Bank. CrossRef

DeYoung, R. (1999). Birth, growth, and life or death of newly chartered banks. Economic Perspectives, 23(3), 18–35.

Elliehausen, G. (1998). The cost of Bank regulation: A review of the evidence. Federal Reserve Bulletin, 84(4), 252.

Fama, E. F. (1980). Banking in the theory of finance. Journal of Monetary Economics, 6(1), 39–57. CrossRef

Freixas, X., & Rochet, J. C. (2008). Microeconomics of banking (2nd ed.). The MIT Press.

Gail, M. H., Lubin, J. H., & Rubinstein, L. V. (1981). Likelihood calculations for matched case-control studies and survival studies with tied death times. Biometrika, 68(3), 703–707. CrossRef

Guiso, L., Sapienza, P., & Zingales, L. (2004). Does local financial development matter? The Quarterly Journal of Economics, 119(3), 929–969. CrossRef

Hart, O. D., & Jaffee, D. M. (1974). On the application of portfolio theory to depository financial intermediaries. The Review of Economic Studies, 41(1), 129–147. CrossRef

Iyer, R., Khwaja, A. I., Luttmer, E. F. P., & Shue, K. (2016). Screening peers softly: inferring the quality of small borrowers. Management Science 62(6), 1554–1577.

Jayaratne, J., & Strahan, P. E. (1998). Entry restrictions, industry evolution, and dynamic efficiency: Evidence from commercial banking. Journal of Law and Economics, 41(1), 239–273. CrossRef

Klein, M. A. (1971). A theory of the banking firm. Journal of Money, Credit and Banking, 3(2), 205–218. CrossRef

Kou, S. (2021). FinTech econometrics: Privacy preservation and the wisdom of the crowd. In V. Babich, J. Birge, & G. Hilary (Eds.), Innovative technology at the interface of finance and operations (Springer series in supply chain management, forthcoming). Springer Nature.

Lee, Y. Y., & Yom, C. (2016). The entry, performance, and risk profile of de novo banks. FDIC Center for Financial Research Paper, WP 2016-03.

Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), 17–35. CrossRef

Lin, M., & Viswanathan, S. (2016). Home Bias in online investments: An empirical study of an online Crowdfunding market. Management Science, 62(5), 1393–1414. CrossRef

McWilliams, J. (2018, December 6). We can do better on De Novos (opinion). American Banker. https://www.americanbanker.com/opinion/fdic-chairman-jelena-mcwilliams-we-can-do-better-on-de-novos

Monti, M. (1972). Deposit, credit and interest rate determination under alternative bank objective functions. In Mathematical methods in investment and finance (pp. 431–454). North-Holland.

Pyle, D. H. (1971). On the theory of financial intermediation. The Journal of Finance, 26(3), 737–747. CrossRef

Santomero, A. M. (1984). Modeling the banking firm: A survey. Journal of Money, Credit and Banking, 16(4), 576–602. CrossRef

Stammann, A., Heiss, F., & Mcfadden, D. (2016). Estimating fixed effects logit models with large panel data. Beiträge zur Jahrestagung des Vereins für Socialpolitik 2016: Demographischer Wandel.

Stammann, A., Czarnowske, D., Heiss, F., & McFadden, D. (2020). bife: Binary Choice Models with Fixed Effects. R package version 0.7. https://cran.r-project.org/web/packages/bife/index.html

Taggart, R. A. (1984). Modeling the banking firm: Comment. Journal of Money, Credit and Banking, 16(4), 612–616. CrossRef

Therneau, T. M., Lumley, T., Elizabeth, A., & Cynthia, C. (2017). survival: Survival Analysis. R package version 2.41-3. https://cran.r-project.org/web/packages/survival/index.html

Zhang, J., & Liu, P. (2012). Rational herding in microloan markets. Management Science, 58(5), 892–912. CrossRef

Title: Microbanks in Online Peer-to-Peer Lending: A Tale of Dual Roles
Authors: Jussi Keppo
Tuan Q. Phan
Tianhui Tan
Publisher: Springer International Publishing
Book: Innovative Technology at the Interface of Finance and Operations
Print ISBN: 978-3-030-81944-6

Electronic ISBN: 978-3-030-81945-3

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-81945-3_9