Data and key variables 1 Platform details

In document Essays on Behavioral Finance in the Digital Age (Page 96-100)

Since I am really convinced by the security, I see more opportunities than risks, especially after the noticeable share price loss before my initial investment

III- 88 1. Introduction

2. Data and key variables 1 Platform details

We obtain data from one of the leading online peer-to-peer lending platforms in Europe, which operates in Germany. To register to this online market for unsecured personal loans, borrowers and investors first share their personal data, the accuracy of which is verified by the platform based on the applicants’ official ID. As part of the registration process, applicant borrowers also authorize the platform to retrieve their individual credit rating from Germany’s FICO-equivalent, i.e. the credit rating agency Schufa.2 Applicant

2 Schufa is Germany’s quasi-monopolist in the provision of consumer credit ratings and maintains scores for approximately 70 million Germans, i.e. 85% of the country’s total population (Schufa Holding AG, 2019). Schufa analyzes consumers’ financial behavior in order to assess their default risk and computes a score which takes one of 15 different values ranging from A (‘excellent’) to P (‘very poor’). The corresponding algorithm is a trade secret of Schufa.

CZAJA/RITTER/STOLPER Among peers: the impact of homophily in online investment


borrowers featuring a Schufa score below K (corresponding to a probability of default in excess of 27.01%) are denied access to the platform.

Upon successful registration, the borrower can submit a loan application by determining the amount, duration, category as well as a free-text header shown in the listing. Moreover, she can choose to add an individual loan description and a picture of (i) herself, (ii) the project or object to be funded or, alternatively, (iii) a random illustration displayed along with the loan application. If no edits are made, the platform automatically assigns a loan description based on the loan category and adds a random pictogram. Importantly, the platform also sets the loan’s interest rate, which predominantly derives from the borrower’s Schufa credit rating. Borrowers have no means of adjusting the interest rate on their own.3

Investors can browse all published loan applications, with the most recently accepted loans showing up first. As is common on online peer-to-peer lending platforms, loans need not be funded entirely by a single investor. Instead investors place a bid of at least 250 € per loan for as many loans as they wish to hold a stake in.4 Given the comparably high minimum bid amount (on, e.g., minimum bids are as low as $50) and a mean bid amount of 436 €, we are confident that investors do not simply use ‘play money’

but instead use this online lending marketplace as a serious investment vehicle. The platform allows a 14-day period to have investors fund the loan. In case of full funding within 14 days or less, the loan amount is immediately paid out to the borrower. If more

3 Note that this means of interest rate determination differs from auction-like mechanisms on other large online peer-to-peer lending platforms. On, e.g., investors specify the minimum interest rate at which they are willing to lend. Funds from different investors are then pooled to determine the lowest possible interest rate the borrower will pay. At 0.7895, Borrower credit score is highly correlated with Loan interest rate, but does not explain it entirely.

Interest rate calculation is not fully reproducible since the platform does not disclose the relevant algorithm.

Competitors state that borrower information such as user data collected online and earlier repayments feed into the calculation of interest rates. Such additional data is unavailable for the platform under review.

4 Note that the platform under review offers two types of investment: the investor may either choose for herself which loan to invest in (self-directed investment). Alternatively, the investor determines the amount of money to be invested, minimum borrower credit scores and a target return on the investment. Based on these parameters, the platform automatically assembles a loan basket (delegated investment). Since we are interested in how homophily potentially affects individuals’ investment behavior, we omit delegated investments.

CZAJA/RITTER/STOLPER Among peers: the impact of homophily in online investment


than 75% of the loan is fund via investors, the platform bridges the funding gap and co-finances the loan. Otherwise, the loan is delisted.

The platform provides investors with a wide range of information on borrowers and their loan applications. Specifically, each loan application includes the loan amount and respective interest rate, its duration, current financing status along with the number of submitted bids as well as an assignment to one of the preset loan categories and, if applicable, a description of the loan and the borrower’s profile picture.5 Moreover, each loan application discloses the borrower’s age, gender and state of residence as well as her Schufa credit score to provide investors with an indication of her default risk. Borrowers cannot bypass the publication of this information. The platform does not offer a private chat function, which ensures that all interactions between investors and borrowers are captured in our dataset. Moreover, borrowers’ names or residential addresses are not disclosed either. Thus, investors have no means to enforce debt redemption by, e.g., paying a private visit to borrowers and therefore, geographic proximity to the borrower should not be a rational input parameter to the investor’s decision on which loan to fund (c.f. Lin and Viswanathan, 2016). Instead, any subsequent repayment issues or defaults are handled directly by the platform and an associated debt collection agency.

2.2 Sample

We draw on detailed records of loan applications and corresponding investor bids for the twelve-year period from March 2007 until October 2018. Our dataset includes a total of 13,721 loan applications and 64,730 bids.

Panel A of Table III-1 reports summary statistics of these loans and associated bids.6 The median loan amounts to 5,000 € and carries an interest rate of 6.5% and a duration of 60 months. Loan amounts vary considerably from 500 up to 50,000 € and interest rates also spread widely from 2.0% up to 18.0%. The mean funding status of 97.7% 14 days

5 Loans may be assigned to any of a total of 22 different loan categories. In the vein of Lin and Viswanathan (2016), we group these categories in seven broader clusters (i) debt consolidation, (ii) higher education, (iii) car purchase, (iv) home improvement, (v) startup capital, (vi) leisure, and (vii) other. We compare either of these subject clusters to the remaining pool of unassigned loans.

6 Table III-A.1 in Appendix III-A provides descriptions of all variables used in the analysis.

CZAJA/RITTER/STOLPER Among peers: the impact of homophily in online investment


after loan publication shows a high investment interest and implies that the platform hardly ever tops off investor money to fill a funding gap.

Panel B of Table III-1 provides descriptive statistics of investors and borrowers in our sample. In October 2018, the median borrower is aged 49 years and applies for a single loan amounting to 5,250 €. The median investor is slightly younger (47 years) and submits six bids summing up to a total investment of 2,000 €. At 25% female borrowers and 7% female investors, gender proportions are comparable to what has been documented in prior research on peer-to-peer lending platforms (Barasinska et al., 2011;

Dorfleitner et al., 2016; Duarte et al., 2012).7 Moreover, Barasinska et al. (2011) show for Germany that users of online peer-to-peer lending platforms have become increasingly similar to borrowers on the regular market for consumer credit with respect to gender, age and geographic dispersion of their places of residence. We square our data with demographics obtained from the latest wave of Germany’s major household survey, the Socio-Economic Panel (SOEP), and corroborate the findings of Barasinska et al., (2011).

Table III-1: Summary statistics

This table presents the descriptive statistics of our sample. See Table III-A.1 in Appendix III-Afor detailed variable descriptions.

N Mean SD Min p25 Median p75 Max

Panel A: Loans and bids

Loan amount 13,721 8,116 8,887 500.0 1,500 5,000 10,500 50,000 Loan interest rate 13,721 7.300 3.039 2.000 5.000 6.500 9.000 18.00 Loan duration 13,721 51.25 15.65 36.00 36.00 60.00 60.00 84.00 Loan financing status 13,721 97.74 12.13 0.000 100.0 100.0 100.0 100.0

Bid amount 64,730 435.7 429.4 250.0 250.0 250.0 500.0 19,000

Bids per loan 64,730 17.74 18.91 0.000 5.000 11.00 24.00 133.0 Panel B: Borrowers and investors

Borrower age 11,705 49.26 12.66 20.00 40.00 49.00 57.00 100.0

Borrower credit score 13,721 3.437 2.348 1.000 1.000 3.000 5.000 10.00 Number loans per borrower 11,705 1.150 0.529 1.000 1.000 1.000 1.000 19.00 Total bid amount per borrower 11,705 9,756 11,719 500.0 1,500 5,250 13,500 111,000

Investor age 7,386 48.08 11.79 19.00 39.00 47.00 56.00 93.00

Number bids per investor 7,386 10.75 17.65 1.000 2.000 6.000 14.00 452.0 Total funding per investor 7,386 4,594 8,537 250.0 750.0 2,000 5,250 180,500

7 See Table III-B.1 in Appendix III-B for a detailed breakdown of borrowers and investors by gender.

CZAJA/RITTER/STOLPER Among peers: the impact of homophily in online investment


In document Essays on Behavioral Finance in the Digital Age (Page 96-100)