Thinking Fast, Not Slow: Evidence from Peer-to-Peer Lending SWUFE, IFS Thinking Fast, Not Slow: Evidence from Peer-to-Peer Lending Li Liao, Tsinghua University Zhengwei Wang, Tsinghua University Jia Xiang, Tsinghua University Jun Yang, Indiana University

Research questions Do investors who make decisions in a very short time (under fast-thinking) tend to make mistakes? We answer such a question in a setting of P2P lending in China; The bidding process of a credit loan typically completes in a few minutes; All investors observe the characteristics of the loan and borrower, as well as the bidding process in real time; Does paying much attention to interest rates lead to better or worse outcomes: higher or lower return (abnormal IRR) and default? Do past experiences help mitigate investors’ tendency in making such mistakes under fast-thinking?

Figure 1. Sample of loans listed on the Renrendai.com (1)

Figure 1. Sample of loans listed on the Renrendai.com (2)

Figure 2. Procedure for borrowing and repaying on Renrendai

Related literature (1) Information contents in P2P lending (Prosper.com) Rational herding (Zhang and Liu 2012); Usage of soft and non-standard information for screening (Iyer et al. 2009); Certification of friendship network (Lin et al. 2013); Information in voluntary disclosure (Michels 2012); Behavioral biases in P2P lending Home bias (Lin and Viswanathan 2015); Racial bias (Pope and Sydnor 2011); Beauty premium (Revina 2008); Trust in P2P lending decisions (Duarte et al. 2012).

Related literature (2) Cognitive biases in psychology and economics Kahneman and Tversky (1974), Kahneman (2011); “Dual-system” – “System 1” and “System 2” thinking: System 1 thinking is fast, automatic, involuntary, and often unconscious; System 2 thinking is slow and effortful, and tends to be more rational. Behavioral intervention and education programs help young people slow down and reflect on their automatic thoughts reduces the rates of arrests and readmission to jail, and improves school engagement and graduation rates (Heller et al. 2017); In experimental settings, faster thinking is associated with greater risk- taking (Cella et al. 2007; DeDonno and Demaree 2008; and Candler and Pronin 2012).

Related literature (3) Limited attention in financial markets Investors pay attention to more salient details (Benartzi and Lehrer 2015); Individual investors are net buyers of attention grabbing stocks (Barber and Odean 2008); Google search frequency is associated with the attention of retail investors and predicts stock prices in the next 2 weeks (Da et al. 2011); Investor reactions to a firm’s earnings surprise are much weaker and post-announcement drift is much stronger when more firms announce earnings in the same day (Hirshleifer et al. 2009); Individual investors in the stock market learn about their ability through trading High-ability investors tend to trade more, and low-ability ones stop trading (Seru et al. 2010).

Data (1) Renrendai.com: 2012.09—2014.12 (data extracted in March, 2016); Renrendai（人人贷） was founded in May 2010, and the cumulative trading volume reached 23.0 billion CNY by November 2016; Borrowers provide: The average rate of return is 11.58% in the year 2015; Loan amounts range from 3,000 to 500,000 CNY; There are eight repayment terms for credit loans: 3 months, 6 months, 9 months, 12 months, 15 months, 18 months, 24 months, and 36 months. Lenders submit: The bidding amount is multiples of 50 CNY, and the minimum bidding amount is 50 CNY; Once the requested amount on a listing is fully funded (in 7 days), the loan is complete and the funding process stops. During the bidding process, the time and amount of bids are posted online and investors have access to the bidding information in real-time.

Nearly all loan applications that pass the audit get fully-funded; Data (2) From September 1, 2012 to December 31, 2014, there are in total 270,929 credit loan applications at Renrendai; Out of which 95.50% fail in the audit process and get withdrawn, not seen by investors; Nearly all loan applications that pass the audit get fully-funded; Out of the 11,897 fully funded loans, 10,385 have been paid off or defaulted (as of March, 2016).

Data (3) Renrendai guarantees the repayment of the principal. 90% of Renrendai loans are fully funded in under eight minutes, with the 25th percentile at 42 seconds and the 75th percentile at 180 seconds; Fee charged to borrowers by Renrendai: 0-5% upfront, depending on credit rating; 0.1-0.35% monthly on balance, depending on credit rating; 1% penalty for pre-payment.

Figure 3. Probability density distribution of Ln(Duration) from 2012 to 2014

Loan characteristics and market conditions Variable Obs. Mean S.D. p1 p25 p50 p75 p99 Duration (Seconds) 10,385 290.930 1,581.505 4 42 80 180 2,972 Default 0.175 0.380 1 Interest (%) 12.704 2.204 10 11 12 13 20 Amount (¥) 25,371.750 39,667.600 3,000 8,000 14,000 27,000 200,000 Term (Months) 10.301 7.076 3 6 9 36 Rm 0.037 0.068 -0.117 -0.004 0.025 0.063 0.247 Rf (%) 2.924 0.355 2.55 2.75 2.8 4.25

Borrower characteristics Variable Obs. Mean S.D. p1 p25 p50 p75 p99 HR 10,385 0.712 0.453 1 Male 0.873 0.333 Age 32.889 7.024 23 28 32 37 52 Bachelor 0.298 0.457 MasterOrAbove 0.023 0.151 Employ(3–5yrs) 0.220 0.414 Employ(5yrs+) 0.347 0.476 Income(¥5,000–10,000) 0.267 0.442 Income(¥10,000–20,000) 0.140 0.348 Income(¥20,000–50,000) 0.143 0.350 Income(¥50,000+) 0.130 0.336 Ln(Income/debt) 1.233 1.038 1.339 1.929 3.773 House 0.555 0.497 Mortgage 0.217 0.412 Car 0.408 0.492 CarLoan 0.080 0.272

Characteristics of fast loans vs. other loans   Difference Fast Loans Other Loans t-statistics p-value Interest Rate (%) 13.711 12.360 28.236 0.000 Term (months) 10.568 10.209 2.253 0.024 Ln(Amount) (¥) 9.276 9.752 -23.572 Rm 0.019 0.043 -15.379 Rf (%) 2.949 2.916 4.074 HR 0.787 0.686 10.030 Male 0.882 0.869 1.745 0.081 Age 31.313 33.427 -13.479 Bachelor 0.291 0.301 -0.960 0.337 MasterOrHigher 0.022 -0.689 0.491 Employ(3–5yrs) 0.208 0.224 -1.686 0.092 Employ(5yrs+) 0.304 0.361 -5.275 Income(¥5,000–10,000) 0.310 0.253 5.749 Income(¥10,000–20,000) 0.127 0.145 -2.234 0.026 Income(¥20,000–50,000) 0.095 0.160 -8.268 Income(¥50,000+) 0.061 0.153 -12.327 Ln(Income/Debt) 1.163 1.257 -4.026

Empirical results Greater sensitivity of the bidding duration to interest rate in the fast- thinking group; Loans in the fast-thinking group have a lower return and higher default rate; Abnormal IRR and interest rate are negatively correlated in the fast- thinking group, and positively correlated in the slow-thinking group; Investors learn to avoid participating in the fast-thinking group; Introduction of mobile app in July, 2014: More noticeable interest rate on a smaller screen; Biding duration becomes even more sensitive to interest rate – chasing for high yield; Two experiments: attention and learning.

IRR and Abnormal IRR IRR Abnormal IRR We calculate the Internal rate of return(IRR) for each loan: 0=𝐴𝑚𝑜𝑢𝑛𝑡+ 𝑡=1 𝑇 𝑅𝑒𝑝𝑎𝑦𝑚𝑒𝑛𝑡 𝐶𝑎𝑠ℎ 𝐹𝑙𝑜𝑤 (1+𝐼𝑅𝑅) 𝑡 where the repayment cash flow is the monthly repayment cash flow from the borrower. Note: guaranteed principal payment by the platform. We regress IRR on borrower and loan characteristics using the following regression: 𝐼𝑅𝑅 𝑖 =𝛼+𝛽∗ 𝐶ℎ𝑎𝑟𝑎𝑐𝑡𝑒𝑟𝑖𝑠𝑡𝑖𝑐 𝑖 + 𝜀 𝑖 Loan and market characteristics include Interest, Ln(amount), Term, Rm, Rf. Borrower characteristics include HR, Male, Age, Bachelor, Master_or_above, Employ, Income, Ln(Income_debt_ratio), House, Mortgage, Car, Car_loan, and Verification Dummies. We use the loans whose due date are during the 30 days prior to the beginning time of loan i to construct the rolling window predicting model and predict the expected IRR of loan i. Abnormal IRR is the difference between the actual IRR and expected IRR.

Table 2. Marginal effect of Fast on the performance of loans (1)   Pre-Match Post-Match AbnormalIRR Default Fast -0.231*** 0.031*** -0.176*** 0.024*** (-5.264) (7.830) (-2.810) (4.444) Interest 0.033*** 0.005*** 0.010 0.009*** (3.463) (5.283) (0.696) (7.204) Ln(Amount) 0.397*** 0.036*** -0.030 0.072*** (10.617) (10.469) (-0.496) (13.737) Term -0.123*** 0.025*** -0.210*** 0.028*** (-10.919) (24.751) (-10.227) (16.089) Rm -0.084*** 0.003*** -0.088*** 0.004*** (-32.163) (11.295) (-13.645) (6.381) Rf 0.092 -0.181*** 2.583*** -0.284*** (0.426) (-9.183) (6.507) (-8.352) HR -0.591*** 0.294*** -0.968*** 0.276*** (-18.282) (99.884) (-12.768) (42.500) Male -0.070 0.018*** -0.334*** 0.039*** (-1.616) (4.645) (-3.687) (5.028) Age -0.006** 0.002*** -0.045*** 0.006*** (-2.517) (8.443) (-8.156) (12.860) Bachelor 0.082** -0.031*** 0.317*** (2.425) (-10.174) (4.637) (-15.007) MasterOrAbove 0.284*** -0.063*** 0.722*** -0.069*** (2.844) (-6.914) (3.519) (-3.951) Employ(3–5yrs) -0.034 -0.014*** 0.236*** -0.037*** (-0.844) (-3.851) (3.196) (-5.828) Employ(5yrs+) -0.093** -0.010*** -0.171** -0.017*** (-2.389) (-2.679) (-2.291) (-2.658)

Table 2. Marginal effect of Fast on the performance of loans (2)   Pre-Match Post-Match AbnormalIRR Default Income(¥5,000–10,000) 1.073*** 0.005 -0.804*** 0.103*** (11.228) (0.612) (-5.359) (8.044) Income(¥10,000–20,000) 0.173*** -0.026*** 0.751*** -0.056*** (2.995) (-4.900) (7.375) (-6.432) Income(¥20,000–50,000) 0.016 -0.010 0.814*** -0.043*** (0.222) (-1.527) (6.100) (-3.766) Income(¥50,000+) -0.220** -0.033*** 1.167*** -0.115*** (-2.232) (-3.725) (6.738) (-7.753) Ln(Income/debt) 0.446*** 0.023*** -0.521*** 0.065*** (8.335) (4.684) (-6.245) (9.077) House -0.065* -0.003 -0.282*** 0.015** (-1.862) (-1.024) (-3.695) (2.300) Mortgage 0.324*** -0.039*** 0.457*** -0.046*** (8.013) (-10.707) (5.480) (-6.435) Car 0.009*** 0.481*** 0.011 (0.146) (2.654) (5.033) (1.306) CarLoan -0.046 0.027*** -0.691*** 0.033*** (-0.816) (5.373) (-5.459) (3.003) Constant -4.182*** -0.200*** -3.605*** -0.645*** (-5.854) (-3.077) (-2.882) (-6.011) Verification Fixed Effects YES Observations 62,054 24,290 R-squared 0.054 0.253 0.275 0.422

Introduction of quantile regression 𝐿𝑛(𝐷𝑢𝑟𝑎𝑡𝑖𝑜𝑛) 𝑖 𝜏 =𝛼+𝛽∗ 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑖 𝜏 +𝛾∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑖 𝜏 + 𝜀 𝑖 𝜏 (1) 𝛽 𝜏 = 𝑎𝑟𝑔𝑚𝑖𝑛 𝛽 𝑖∈{𝑖: 𝑌 𝑖 ≥ 𝑋 𝑖 ′ 𝛽} 𝜏 𝑌 𝑖 − 𝑋 𝑖 ′ 𝛽 + 𝑖∈{𝑖: 𝑌 𝑖 < 𝑋 𝑖 ′ 𝛽} (1−𝜏) 𝑌 𝑖 − 𝑋 𝑖 ′ 𝛽 𝜏∈(0,1) (2) Equation (2) assigns a weight of τ to data points above the fit line, and a weight of 1-τ to data points below the fit line. For any 𝜏∈(0,1), we can calculate 𝛽 𝜏 , the coefficient estimate under τ-quantile regression. 𝛽 𝜏 measures the marginal effect of x on y at the τ-quantile of y. OLS Regression Quantile Regression Loss Function Squared Loss Absolute Loss

Table 3. Marginal effect of Interest on Ln(Duration) at different quantile of Ln(Duration)   Ln(Duration) OLS Regression Quantile Regression 0.05 Quantile 0.25 quantile 0.75 Quantile 0.95 Quantile Interest -0.202*** -0.245*** -0.180*** -0.185*** -0.159*** (-40.133) (-21.447) (-39.821) (-26.446) (-10.340) Ln(Amount) 0.502*** 0.511*** 0.383*** 0.528*** 0.597*** (20.876) (9.303) (17.576) (16.584) (9.876) Term 0.073*** 0.029** 0.034*** 0.093*** 0.134*** (12.720) (2.422) (6.494) (12.977) (10.064) Rm 2.572*** 1.449*** 3.180*** 2.930*** 2.241*** (14.237) (3.548) (19.307) (12.214) (4.976) Rf -1.109*** -0.274 -0.417*** -1.589*** -2.214*** (-10.439) (-1.297) (-4.293) (-11.811) (-9.002) HR -0.117*** -0.020 -0.036 -0.174*** -0.299*** (-4.260) (-0.370) (-1.463) (-4.915) (-4.306) Male -0.023 -0.058 -0.018 (-0.783) (-1.009) (-0.865) (-0.483) (-0.519) Age 0.004** 0.002 0.005*** 0.003 0.001 (2.193) (0.572) (3.515) (1.171) (0.323) Bachelor -0.051** -0.030 -0.015 -0.077*** -0.072 (-2.263) (-0.656) (-0.739) (-2.699) (-1.313) MasterOrAbove -0.208*** -0.187 -0.280*** -0.365** (-3.195) (-1.528) (-1.000) (-3.423) (-2.403) Employ(3–5yrs) -0.007 0.006 -0.095 (-0.263) (-0.454) (-0.654) (0.196) (-1.505) Employ(5yrs+) 0.023 -0.012 -0.002 0.028 0.031 (0.936) (-0.232) (-0.106) (0.865) (0.515)

Table 3. Marginal effect of Interest on Ln(Duration) at different quantile of Ln(Duration) (2)   Ln(Duration) OLS Regression Quantile Regression 0.05 Quantile 0.25 quantile 0.75 Quantile 0.95 Quantile Income(¥5,000–10,000) 0.105* -0.084 0.120** 0.022 0.319** (1.699) (-0.578) (2.116) (0.273) (2.157) Income(¥10,000–20,000) 0.174** -0.161 0.162** 0.096 0.447** (2.201) (-0.866) (2.224) (0.945) (2.338) Income(¥20,000–50,000) 0.262*** -0.144 0.219** 0.169 0.694*** (2.749) (-0.648) (2.495) (1.372) (3.028) Income(¥50,000+) 0.329*** -0.282 0.315*** 0.234 0.857*** (2.790) (-1.022) (2.889) (1.524) (2.962) Ln(Income/debt) -0.092*** 0.051 -0.090*** -0.030 -0.243*** (-2.779) (0.665) (-2.968) (-0.699) (-3.001) House -0.032 0.010 -0.048** -0.081 (-1.255) (0.186) (-2.075) (-0.912) (-1.306) Mortgage -0.085*** -0.100* -0.034 -0.074** -0.142** (-3.119) (-1.865) (-1.368) (-2.152) (-2.167) Car -0.020 -0.040 -0.005 -0.001 -0.011 (-0.685) (-0.693) (-0.185) (-0.021) (-0.151) CarLoan -0.012 -0.006 0.018 (-0.314) (-0.086) (0.514) (-0.226) (-0.378) Constant 7.254*** 4.051*** 5.791*** 8.858*** 10.220*** (16.715) (4.585) (14.615) (15.995) (9.609) Verification Fixed Effects YES Day-of-Week Fixed Effects Hour-of-Day Fixed Effects Number of Observations 10,385 Adjusted/Pseudo- R2 0.399 0.207 0.220 0.269 0.295

Figure 3. Marginal effect of Interest on Ln(Duration) at different quantiles of Ln(Duration)

Additional analyses Investor learning (Table 4) Introduction of the mobile app (Table 5) Two experiments (Tables 6 and 7)

Table 4. Summary statistics Panel A. Entire sample Variable Obs. Mean S.D. p1 p25 p50 p75 p99 Bidt 288,944 0.176 0.381 1 AccuBidst 25.939  66.368  2 5 19 439 MonthsFromFirstbidt (Months) 12.348  9.327  4 10 18 34 MonthlyBidAmountt-1 (¥) 944.249  9,461.214  56,950 Ln(1+MonthlyBidAmount)t-1 (¥) 1.590 2.962 9.793 WeightedAverageReturnt-1 (%) 8.964 6.327 12.524 13.803 16.396 ProportionDefaultt-1 (%) 5.074  17.371  100 Activeness (%) 67.191  30.479  13.693 38.372 77.778

Table 4. Summary statistics Sub-sample (including only those months in which investors lend) Variable Obs. Mean S.D. p1 p25 p50 p75 p99 ΔLn(Duration)t (Seconds) 50,420 0.327 1.003 -1.709  -0.343 0.211 0.882 3.357 ProportionFastBidst (%) 16.000 30.988 16.667 100 AccuBidst 46.893 100.941 2 5 13 39 562 MonthsFromFirstbidt (Months) 9.341 8.329 1 3 7 35 MonthlyBidAmountt-1(¥) 3,764.13 21,374.89 300 1,850 57,000 MonthlyBidAmountt(¥) 3,999.51 19350.54 50 200 700 2,450 53,800 Ln(1+MonthlyBidAmount)t-1 (¥) 4.549 3.642 5.707 7.523 10.951 WeightedAverageReturnt-1 (%) 11.109 4.861 11 13.000 13.981 16.532 ProportionDefaultt-1 (%) 5.189 14.696 3.922 Activeness (%) 77.459 26.746 12.828  60.606 87.901

Introduction of learning models 𝑃𝑟( 𝐵𝑖𝑑 𝑖,𝑡 =1)=𝛷( 𝛼 𝑖 + 𝛽 1 𝐸𝑥𝑝𝑟𝑖𝑒𝑛𝑐𝑒 𝑖,𝑡 +𝛽 2 𝐸𝑥𝑝𝑟𝑖𝑒𝑛𝑐𝑒 𝑖,𝑡 2 +𝛿 𝑋 𝑖,𝑡 + 𝜀 𝑖,𝑡 ) In the simple learning Model, the intercept is held constant across all investors. The model with individual heterogeneity allows the constant to differ across investors. The probit model is estimated each month, and the inverse Mills ratios for each month are computed separately from each of these models. The regressions in the second stage are of the form: ∆𝑦 𝑖,𝑡 =𝛽 ∆𝑋 𝑖,𝑡−1 + 𝜌 3 𝐼 𝑡=3 𝜆 3 +⋯+ 𝜌 28 𝐼 𝑡=28 𝜆 28 + 𝜀 𝑖,𝑡 , where 𝜆 3 ,.., 𝜆 28 are the inverse Mills ratios from the cross-sectional probit model in each of the 3rd to the 28th month. The table reports estimates of the first-stage probit model with data from all of the months of the sample pooled together. The second-stage regressions are estimated with all the variables in first differences, except the inverse Mills ratios.

Learning model with heterogeneity Table 4. Learning models with individual heterogeneity and survival controls Panel B. Experience measured by the total number of loans on Renrendai(1)   Simple learning model Learning model with heterogeneity Selection models (1st Stage) 2nd Stage Dependent variable Ln(Duration)t Proportion FastBidst Bidt= 1 ΔLn(Duration)t ΔProportion AccuBidst 3.01e-4* -0.004 4.917e-4** -0.001 0.013*** 0.006*** -0.188*** (1.655) (-0.689) (1.971) (-0.106) (65.337) (3.635) (-3.157) AccuBidsSquaredt -1.55e-6** 1.94e-5 -1.66e-06* 3.65e-6 -3.59e-5*** -1.43e-5** 4.38e-4* (-2.376) (0.864) (-1.762) (0.132) (-50.567) (-2.150) (1.948) Ln(MonthlyBidAmount)t-1 -0.028*** 0.450*** -0.022*** 0.391*** 0.163*** 0.005*** -0.019 (-26.405) (12.576) (-20.118) (10.685) (161.846) (2.986) (-0.372) WeightedAverageReturnt-1 -0.006*** 0.159*** -0.005*** 0.153*** 0.031*** 0.003 -0.081 (-6.343) (5.175) (-4.814) (4.826) (46.103) (1.267) (-1.085) ProportionDefaultt-1 -4.51e-4* -0.001*** 0.001 -0.002*** 0.049** (-1.811) (-0.062) (-2.582) (0.149) (-9.884) (-4.157) (2.471) Activeness 0.008*** (57.336) Constant 0.467*** 1.412* 0.393*** 2.029** -2.716*** (18.967) (1.669) (15.805) (2.382) (-96.582)

Learning model with heterogeneity Table 4. Learning models with individual heterogeneity and survival controls Panel B. Experience measured by the total number of loans on Renrendai (2)   Simple learning model Learning model with heterogeneity Selection models (1st Stage) 2nd Stage Dependent variable Ln(Duration)t Proportion FastBidst Bidt= 1 ΔLn(Duration)t ΔProportion Inverse Mills ratios ( 𝜆 3 - 𝜆 28 ): Control Observations 50,420 288,944 42,600 R-squared 0.337 0.216 0.251 0.138 0.079 Individual fixed effects No Yes Time fixed effects Log likelihood -100800.73 Joint test of ρt =0 (t=3,…,28) F(25, 42569) 268.37 144.40 Pr > F 0.000

Learning model with heterogeneity Table 4. Learning models with individual heterogeneity and survival controls Panel C. Experience measured by the # of months lapsed from first investment at Renrendai (1)   Simple learning model Learning model with heterogeneity Selection models (1st Stage) 2nd Stage Dependent variable Ln(Duration)t Proportion FastBidst Bidt= 1 ΔLn(Duration)t ΔProportion MonthsFromFirstbidt 0.008*** -0.043 0.011*** -0.035 0.009*** 0.302*** -6.011*** (5.712) (-0.883) (7.547) (-0.709) (4.708) (18.510) (-10.842) MonthsFromFirstBidSquaredt -1.27e-4*** -0.002 -1.537e-4*** -0.003** -1.818e-4*** -0.002*** 0.050*** (-2.801) (-1.563) (-3.251) (-2.024) (-4.581) (-6.893) (4.110) Ln(MonthlyBidAmount)t-1 -0.026*** 0.419*** -0.018*** 0.351*** 0.182*** -0.006*** 0.175*** (-24.867) (11.739) (-16.757) (9.460) (180.470) (-3.486) (3.023) WeightedAverageReturnt-1 -0.008*** 0.193*** -0.007*** 0.186*** 0.040*** -0.004 0.035 (-8.669) (6.210) (-7.223) (5.848) (60.611) (-1.593) (0.466) ProportionDefaultt-1 -1.817e-4 -0.005 -3.743e-4 -0.003 0.035* (-0.728) (-0.586) (-1.452) (-0.353) (-12.439) (-2.982) (1.764) Activeness 0.003*** (9.443) Constant 0.436*** 1.601* 0.246*** 2.037** -2.136*** (17.329) (1.850) (2.619) (2.341) (-46.891)

Introduction of the mobile app Renrendai launched its mobile app on July 30, 2014; The screen of a mobile phone is much smaller; Interest rate is much more salient: Located near the top In the middle of the screen, and Shown in orange font (only information not in black) Credit rating not shown on the screen; The updated status such as “99% funded” also attracts investor attention.

Figure 4. A sample loan listed on Renrendai mobile app

Table 5. Sensitivity of duration to interest rate under fast thinking around mobile app introduction   Ln(Funding Duration) 0.1 Quantile 0.15 quantile 0.20 Quantile 0.25 Quantile Interest Rate -0.196*** -0.187*** -0.180*** -0.184*** (-23.586) (-25.282) (-23.312) (-25.570) Interest Rate×61-90daysBeforeMobileAPP 0.032 0.023 0.017 -0.001 (1.219) (1.009) (0.721) (-0.036) Interest Rate×31-60daysBeforeMobileAPP 0.015 0.011 0.006 0.014 (0.579) (0.481) (0.261) (0.660) Interest Rate×1-30daysBeforeMobileAPP -0.035 -0.027 -0.021 (-1.300) (-1.109) (-0.814) (-0.880) Interest Rate×1-30daysAfterMobileAPP -0.084*** -0.079*** -0.072*** -0.056** (-3.311) (-3.532) (-3.078) (-2.576) Interest Rate×31-60daysAfterMobileAPP -0.107*** -0.092*** -0.120*** -0.115*** (-3.430) (-3.310) (-4.141) (-4.287) Interest Rate×61-90daysAfterMobileAPP 0.028 0.026 (0.823) (0.848) (0.456) (0.950) Interest Rate×Over90daysAfterMobileAPP -0.004 -0.012 -0.036** -0.049*** (-0.234) (-0.861) (-2.385) (-3.503)

Experiment on attention Experiment conducted in Jan. 2017 at PBCSF, Tsinghua University; 60 participants are divided into two groups of 30 (by even or odd student ID); Trained together for 30 minutes using 50 loans randomly drawn from loans listed during 10/4/2014 and 11/3/2013, 30 days before a random date near the middle of our sample period, 11/4/2013; Given loan and borrower characteristics, and loan performance; No communications allowed among participants. Fast-thinking group was required to choose one out of five loans within 42 seconds; Slow-thinking group had more time to select one loan from the same five loans; Investment was conducted separately for two groups.

Table 6. Experiment on attention: Loan characteristics under fast vs. slow thinking   Difference Fast thinking Slow thinking t-statistics p-value Interest Rate (%) 16.267 15.067 3.016 0.004 Term (Months) 14.9 14.1 0.316 0.753 Amount (¥) 9,000 7,700 1.573 0.121 HR 0.267 0.067 2.121 0.038

Experiment on learning Experiment conducted in June 2017 at PBCSF, Tsinghua University; 60 different participants divided randomly into two groups of 30 (by even or odd student ID); Trained together for 30 minutes using 50 loans randomly drawn from loans listed during 10/4/2014 and 11/3/2013; In Round 1, all participants select one out of five given loans from 11/4/2013 to invest; Group 1 participants were surveyed before knowing selected loan performance, and before round 2 investment; Group 2 participants were surveyed after round 2 investment.

Figure 5. A screen shot of survey questions from Survey Monkey

Table 7. Experiment on learning (1) Panel A. The difference in the percentage of factor selected between Groups 1 and 2 Group 1 Group 2 t-statistics p-value Interest Rate 50.00% 13.30% 3.266 0.002 Credit Rating 23.30% 66.70% -3.685 0.001 Term 16.70% 10.00% 0.75 0.456 Amount 6.70% -0.46 0.647 Others 3.30% 0.00% 1.000 0.332 Intuition Score 5.47 4.07 3.55 Panel B. The difference in decision time between the first and second rounds Groups 1 and 2 Group 1 Group 2 First Round 112.833 113.3 112.367 Second Round 97.033 102 92.067 t-value 2.474 1.161 2.430 p-value 0.015 0.250 0.018

Table 7. Experiment on learning (2) Panel C. The impact of default on the investing style of investors Ln(Decision Time) Interest Rate Credit Rating Intuition Score   Groups 1 and 2 Group 2 Default 0.564*** 0.488*** -0.400*** 0.550*** -1.950*** (4.948) (3.006) (-3.528) (3.485) (-3.503) Constant 4.164*** 4.100*** 0.400*** 0.300** 5.700*** (50.795) (30.957) (4.320) (2.328) (12.540) Observations 60 30 R-squared 0.297 0.244 0.308 0.302 0.305

Your comments and suggestions are greatly appreciated. Conclusion We examine behavioral biases in decision making under fast-thinking using a unique setting of P2P lending in China – Renrendai (人人贷); Investors tend to over-weigh interest rates and under-weigh risk under fast-thinking, which results in lower abnormal IRR and higher default rate; Mobile app exacerbates the tendency of chasing for yield; More experienced investors are less prone to making such mistakes. Your comments and suggestions are greatly appreciated.