PhD Thesis Defense, Huan Tang, Finance

Congratulations to Dr Huan Tang, Finance specialization, who successfully defended her Doctoral thesis at HEC Paris, on August 27, 2020. Huan joined the London School of Economics as Assistant Professor.

Huan Tang is amongst the winners of the 2020 AQR Top Finance Graduate Award which rewards the six best students in finance in the world selected by a jury of absolutely top academics in the field of financial economics. Read more here. 

Thesis Topic: 
Three essays on FinTech

Supervisor: 
Johan Hombert Associate Professor, HEC Paris

Jury members:   
Viral Acharya Professor, NYU Stern School of Business
Xavier Giroud, Associate professor, Columbia Business School   
Denis Gromb, Professor, HEC Paris 

Johan Hombert Associate Professor, HEC Paris, Supervisor

Abstract: 
This three-chapter thesis investigates the benefit and cost of financial technology (FinTech) for consumers and firms. The first chapter studies whether, in the consumer credit market, peer-to-peer (P2P) lending platforms serve as substitutes for banks or instead as complements. I develop a conceptual framework and derive testable predictions to distinguish between these two possibilities. Using a regulatory change as an exogenous shock to bank credit supply, I find that P2P lending is a substitute for bank lending in terms of serving infra-marginal bank borrowers yet complements bank lending with respect to small loans. These results indicate that the credit expansion resulting from P2P lending likely occurs only among borrowers who already have access to bank credit. 
 
This second chapter focuses on the potential cost of FinTech --- privacy intrusion. In particular, I study the value of privacy, for individuals, using data from large-scale field experiments that vary disclosure requirements for loan applicants and loan terms on an online peer-to-peer lending platform in China. I find that loan applicants attach positive value to personal data: Lower disclosure requirements significantly increase the rate at which applications are completed. I quantify the monetary value of personal data— and the welfare effect of various disclosure policies—by developing a structural model that links individuals’ disclosure, borrowing, and repayment decisions. Using detailed application-level data, I estimate that social network ID and employer contact are valued at 230 RMB (i.e., $33, or 70% of the average daily salary in China); for successful borrowers, this accounts for 8% of the average net present value of a loan. Requiring answers to these application questions reduces borrower welfare by 13% and costs the platform $0.50 in expected revenue per applicant. 
 
In the last chapter, I turn to investigate the benefit of FinTech for firms. This chapter is in collaboration with Paul Beaumont (McGill University), AnneSophie Lawniczak (Banque de France), and Eric Vansteenberghe (Paris School of Economics). We evaluate the tradeoff for small business between borrowing from crowd-funding platforms and traditional banks. To do so, we link the universe of Fintech SME loans in France to the credit registry at Banque de France (the French central bank) to obtain a comprehensive credit history of SMEs borrowing from Fintech platforms. The main finding is that following a successful Fintech loan application, SMEs experience a significant increase in bank credit. This result is robust to the inclusion of a control group of SMEs with successful bank loan applications. Importantly, the increase in bank credit is only present for long-term categories, which require collateral. This suggests that FinTech platforms may expand credit access for SMEs by relaxing their collateral constraints. 

Key words: FinTech, financial intermediation, consumer credit, SMEs