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Content
Introduction Literature review Aim of study Methodology Empirical results Conclusion
Herding in P2P Lending Market: Rational Inference or Irrational - - PowerPoint PPT Presentation
Herding in P2P Lending Market: Rational Inference or Irrational - - PowerPoint PPT Presentation
Herding in P2P Lending Market: Rational Inference or Irrational Trust? Pei Ping, Zhang Ke Department of Finance and Insurance Business School Nanjing University 2016/5/22 1 Content Introduction Literature review Aim of study
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Introduction
Feature of P2P lending
Massive lenders
Social aspect
Unprofessional lenders
Advantages of analyzing herding in P2P
Controlled variable
Pre-fixed price
Discern rational herding from irrational
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Literature review
Definition and classification of herding
Herding is everyone doing what everyone else is doing, even when their private information suggests doing something quite different.
Rational and irrational
Herding in P2P lending market
What we have known
What is unknown: no credit score system, first 24 hours, auto-bid
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Aim of Study
To examine lenders’ behavior in
the circumstance of no widely accepted credit score system
the first few hours of bidding process
the condition of both auto and manual bidding
Research question
The existence of herding
The type of herding
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Methodology
Identify herding
bidi,t denote the number of biddings that loan i receives during its tth hour
amounti, t−1 denote the amount of prior biddings that loan i has received in its (t-1)th hour
Time varying Xi,t captures the time effects. It includes: , , ℎ
, , Day-of-Week, Start-day, Month
Time unvarying Zi captures the loan fixed effects. It includes: grade, term of loan, rate, overdue, no_paid, success bidi,t=β1bidi, t−1+β2amounti, t−1+γXi,t+δZi+μi+ei,t
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Methodology
Distinguish rational herding from irrational
bid_mi,t−1 denote the number of manual biddings that loan i receives during its (t-1)th hour
bid_ai,t−1 denote the number of auto biddings that loan i receives during its (t-1)th hour
amount_mi,t−1 denote the amount of prior manual biddings that loan i has received in its (t-1)th hour
amount_ai,t−1 denote the amount of prior auto biddings that loan i has received in its (t-1)th hour bid_mi,t=β1bid_mi,t−1+β2bid_ai,t−1+β3amount_mi,t−1+ β4amount_ai,t−1+γXi,t+δZi+μi+ei,t
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Empirical results
Data and summary statistics
We collect all the loan requests posted on Renrendai platform from October 2010 to January 2015.
The initial dataset contains 454,584 loan requests.
Then we excluded all loans without any bids, which are 334,377 loan requests.
Our final dataset therefore includes a total of 120,207 loan requests which have received 4,856,413 biddings.
It is notable that 113,718 out of 120,207 loan requests are fully funded in 24 hours after they are first posted on the platform.
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Empirical results
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Empirical results
Existence of herding
We find that the lag bid has a significant positive effect
- n bid when the impact of percent funded on bidding is
controlled.
The herding effect is much higher after we control the time limit effect.
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Empirical results
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Empirical results
Classification of herding
When control the effect of percent funded and time limit, we find both lag bid_m and lag bid_a have significant effect on bid_m.
It suggests that herding in P2P market consists of both rational and irrational herding.
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Empirical results
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Empirical results
Robustness check
Alternative definition of bidding: bidding amount instead of bidders number
GMM method to estimate dynamic formulation
VIF test for multicollinearity
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Conclusion
We find that lenders appear to imitate others'
behavior and herding exists in the P2P market when we control for the percent funded and time limit effect.
Besides rational herding, there are significant
evidence that lenders would follow others' behavior blindly and ignore the information they
- btain.
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Future research
Different level of disclosure on multiple P2P
lending platforms and the type of investor’s herding behavior.
Social media, Natural Language Processing
(NLP) method and investor behavior in P2P market.
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