Bachelorarbeit, 2022
43 Seiten, Note: 1,7
1 Introduction
1.1 Motivation
1.2 Literature Review
1.3 Bondora Overview
1.4 Lending on Bondora
1.5 Does Bondora fulfil the classic P2P promises?
2 Main Findings
2.1 The Datasets
2.2 Dummy Regressions
2.3 Primary Market Regression
2.4 Secondary Market Regression
3 Closing Section
3.1 Conclusion
3.2 Limitation
This thesis examines the driving factors of pricing behavior within the peer-to-peer (P2P) lending platform Bondora, specifically analyzing mechanisms in both its primary and secondary markets through regression analysis of borrower and loan characteristics.
1.3 Bondora Overview
This chapter gives an insight into the Bondora P2P platform and classifies it in terms of characteristics and processes. Bondora Capital OÜ was founded on March 11th, 2008, and was firstly available for investors in 2009 (Bondora Blog, 2018, March 12th). The company is headquartered in Tallinn, the capital of Estonia, and issued loans amounting to € 615,2 million, according to its statement (Bondora Statistics). Bondora issues loans to borrowers from Estonia, Spain, Finland, and Slovakia only, but lenders from all over the world can lend money to the borrowers on Bondora, through the platform. The parent company of Bondora Capital OÜ, Bondora AS, is regulated by two authorities, namely the Estonian Financial Supervisory Authority for all loans originating from European countries since 21.03.2016 and the Finish Financial Supervisory Authority for loans originating from Finland (Bondora Support [1]).
To borrow money, an applicant sends a loan application, in which personal data, like contact details, socio-demographic data, employment details, income details, and outstanding debts are revealed. Bondora then evaluates, if the loan application fits the company´s lending policy regarding restrictions on age, income, and over-indebtedness of the applicant. If the policy check is positive, the borrower’s credit score is calculated, and a credit offer is made (Bondora Support [2]). Since 2020 the process of customer identification and fraud detection is sourced out to the company Onfindo (Bondora Support [3]). Bondora collects and verifies the applicant´s data simultaneously or immediately after the external identification of the customer and the fraud check. Furthermore, behavioral data from third-party data providers, social networks, and server protocols are considered. All previously collected data is included in the Bondora-Rating. The expected loss after the recovery procedure, within one year, is used to create an expected lifetime cash flow curve. Data collected in periods of an economic downturn are weighted stronger to reflect loan performance in an adverse economic environment (Bondora Support [4]). The interest rate is based on this expected lifetime cash flow. A loan may have a default risk of 10%, which would result in a D-rating. But if it is expected that a lot of money can be recovered during the recovery process, the loan may get a C-Rating (Bondora Rating [5]).
1 Introduction: Provides the motivation for P2P lending, a literature review on relevant research, an overview of the Bondora platform, and an examination of whether Bondora fulfills classic P2P promises.
2 Main Findings: Describes the primary and secondary market datasets, conducts dummy regressions on borrower characteristics, and provides in-depth regression analyses for both markets.
3 Closing Section: Summarizes the study’s findings regarding price determinants and acknowledges limitations encountered during the analysis.
P2P lending, Bondora, interest rate, secondary market, discount rate, regression analysis, default risk, pricing behavior, expected loss, financial technology, borrower characteristics, information asymmetry, asset mispricing, loan fulfillment, credit scoring.
The work investigates the factors that drive pricing behavior for loans on the P2P platform Bondora, distinguishing between original loan issuance in the primary market and trading in the secondary market.
The research covers P2P lending mechanisms, financial regression modeling, loan pricing factors, and the socio-economic determinants of creditworthiness.
The study seeks to identify which specific variables dictate interest rate settings on the primary market and discount rate variations on the secondary market for P2P loans.
The author employs quantitative empirical analysis, specifically performing various regression models (including dummy regressions and z-standardized coefficients) on two large datasets retrieved from Bondora.
The main section analyzes borrower demographics (gender, education, employment) in relation to interest rates, performs regression analysis on primary and secondary market behavior, and tests for normal distribution of model residuals.
Key terms include P2P lending, Bondora, regression analysis, interest rates, discount rates, pricing behavior, and default risk.
Bondora's platform sets interest rates based on an internal rating system, which is heavily influenced by calculated expected loss and predicted recovery success rates rather than direct lender-borrower negotiation.
The study concludes that secondary market pricing is often influenced by irrationality and cognitive limitations, as the regression model explains only a small fraction (1%) of the variance in discount rates.
Der GRIN Verlag hat sich seit 1998 auf die Veröffentlichung akademischer eBooks und Bücher spezialisiert. Der GRIN Verlag steht damit als erstes Unternehmen für User Generated Quality Content. Die Verlagsseiten GRIN.com, Hausarbeiten.de und Diplomarbeiten24 bieten für Hochschullehrer, Absolventen und Studenten die ideale Plattform, wissenschaftliche Texte wie Hausarbeiten, Referate, Bachelorarbeiten, Masterarbeiten, Diplomarbeiten, Dissertationen und wissenschaftliche Aufsätze einem breiten Publikum zu präsentieren.
Kostenfreie Veröffentlichung: Hausarbeit, Bachelorarbeit, Diplomarbeit, Dissertation, Masterarbeit, Interpretation oder Referat jetzt veröffentlichen!

