Why do peer-to-peer (P2P) lending platforms fail? The gap between P2P lenders' preferences and the platforms’ intentions
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ABSTRACT: Supplementary Information
The online version contains supplementary material available at 10.1007/s10660-021-09489-6.
SUBMITTER: Klein G
PROVIDER: S-EPMC8210520 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background - With the recent evolution of Financial Technology (FinTech), 11 peers to peer (P2P) lending platforms have been regulated by the Securities Commission in Malaysia since 2016. P2P lending platforms offer new investment opportunities to individual investors to earn higher rates on return than what traditional lenders usually provide. However, individual investors may face higher potential risks of default from their borrowers. Therefore, individual investors need to understand the potential exposure to such P2P lending platforms to make an effective investment decision. This study aims to explore the potential risk exposures that individual investors may experience at Malaysia's licensed P2P lending platforms. Methods - Based on data collected manually from nine P2P lending platforms over five months, relationships between interest rates and various risk classifying factors such as credit rating, industry, business stage, loan purpose, and loan duration are examined. Results- This study shows that loans with a similar credit rating and with or without similar loan purpose; and a business stage may offer investors significantly different interest rates. In addition, loans with shorter durations may provide investors with higher interest rates than those with longer durations. Finally, loans issued by companies from the same industry appeared to be charged with similar interest. These findings are valuable to investors to prepare themselves before making their investments at the P2P lending platforms. Conclusion- With first hand-collected data, this study provides an original insight into Malaysia's current P2P lending platforms. Findings obtained for relationships between interest rates and risk classifying factors such as credit rating, industry, business stage, loan purpose and loan duration are valuable to investors of Malaysian P2P lending platforms.
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Project description:Background: Since 2016, the Securities Commission (SC) in Malaysia has given licenses to only eleven P2P lending platforms. Such lending platforms are expected to disrupt the lending services of traditional lenders in the coming years. However, being still in their infant stages, it is essential to know the extent to which such platforms are made known to potential investors out there. This study aims to examine the awareness level of the eleven P2P lending platforms among Malaysian adults. The study also explores if past investment experience and financial knowledge would influence such awareness from Malaysian adults. Methods: A sample of 335 Malaysian individuals was used for this study. An online questionnaire was designed with three main parts: demographic, financial literacy, and P2P lending awareness. Using IBM SPSS Statistics 26, frequency, descriptive, normality, Pearson coefficients and multiple regression analyses were carried out. Results: Although seven out of ten respondents have good knowledge in three areas of finance: compounding rate, inflation and diversification, only 14.33% had a good and excellent awareness level of P2P lending. Thus, one would expect lesser awareness about P2P lending among Malaysian adults whose financial literacy is poor or zero. Test results from multiple regression analysis suggest that past lending experiences positively affect the awareness of P2P lending in Malaysia, but not the financial literacy. Conclusions: The awareness about P2P lending among Malaysian adults is too low, despite their high level of education and financial literacy. No investing experience and not knowing any existing P2P lending in the country may be the reason for this low awareness. Therefore, for P2P lending to thrive in Malaysia, the eleven P2P lending platforms need to be promoted aggressively in various social media outlets.
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Project description:Peer-to-peer lending is hypothesized to help equalize economic opportunities for the world's poor. We empirically investigate the "flat-world" hypothesis, the idea that globalization eventually leads to economic equality, using crowdfinancing data for over 660,000 loans in 220 nations and territories made between 2005 and 2013. Contrary to the flat-world hypothesis, we find that peer-to-peer lending networks are moving away from flatness. Furthermore, decreasing flatness is strongly associated with multiple variables: relatively stable patterns in the difference in the per capita GDP between borrowing and lending nations, ongoing migration flows from borrowing to lending nations worldwide, and the existence of a tie as a historic colonial. Our regression analysis also indicates a spatial preference in lending for geographically proximal borrowers. To estimate the robustness for these patterns for future changes, we construct a network of borrower and lending nations based on the observed data. Then, to perturb the network, we stochastically simulate policy and event shocks (e.g., erecting walls) or regulatory shocks (e.g., Brexit). The simulations project a drift towards rather than away from flatness. However, levels of flatness persist only for randomly distributed shocks. By contrast, loss of the top borrowing nations produces more flatness, not less, indicating how the welfare of the overall system is tied to a few distinctive and critical country-pair relationships.
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Project description:We aggregate the United States (US) state-level data with LendingClub's loan book covering the period from 2008 to 2019. LendingClub is a FinTech lending company that provides loans through a technology-driven platform. It was one of the pioneering and leading US peer-to-peer (P2P) lending platforms. Our dataset consists of over two million observations (N=2,703,430) with diverse loan, borrowers and state-specific features. We provide the description of variables, descriptive statistics, and STATA code with the full dataset. The US possesses significant cross-state variation in terms of economic and demographic characteristics while having risk-sharing policies at the federal level to protect states' creditworthiness. This unique feature of our combined database creates an ideal opportunity to explore the P2P lending market within the context of macroeconomic variables. As the dataset covers a 12-year period for all US states, it enables further cross-sectional and longitudinal analyses of the FinTech lending market.
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