Determinants of Default in Lithuanian Peer-To-Peer Platforms

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Abstract

The article analyses the factors that determine the probability of debtors’ default in the Lithuanian peer-to-peer market. The results of this study are compared with previous research.

1. Barasinska, N., Schäfer, D. (2014). Is Crowdfunding Different? Evidence on the Relation Between Gender and Funding Success from a German Peer-To-Peer Lending Platform // German Economic Review. Vol. 15, No. 4, pp. 436–452. https://doi.org/10.1111/geer.12052

2. Cai, S., Lin, X., Xu, D., Fu, X. (2016). Judging Online Peer-to-Peer Lending Behavior: A Comparison of First-Time and Repeated Borrowing Requests // Information & Management. Vol. 53, No. 7, pp. 857–867. https://doi.org/10.1016/j.im.2016.07.006

3. Davis, K., Murphy, J. (2016). Peer to Peer Lending: Structures, Risks and Regulation. Internet access: http://kevindavis.com.au/secondpages/acadpubs/2016/JASSA%20Paper%20-%20P2P%20Lending%20-%20Davis%20and%20Murphy%20-%20final.pdf

4. Ding, J., Huang, J., Li, Y., Meng, M. (2018). Is There an Effective Reputation Mechanism in Peer-to-Peer Lending? Evidence from China // Finance Research Letters. https://doi.org/10.1016/j.frl.2018.09.015

5. Emekter, R., Tu, Y., Jirasakuldech, B., Lu, M. (2015). Evaluating Credit Risk and Loan Performance in Online Peer-to-Peer (P2P) Lending // Applied Economics. Vol. 47, No. 1, pp. 54–70. https://doi.org/10.1080/00036846.2014.962222

6. Fong, A. (2015). Regulation of Peer-to-Peer Lending in Hong Kong: State of Play // Law and Financial Markets Review. Vol. 9, No. 4, pp. 251–259. https://doi.org/10.1080/17521440.2015.1114248

7. Freedman, S., Jin, G. Z. (2017). The Information Value of Online Social Networks: Lessons from Peer-to-Peer Lending // International Journal of Industrial Organization. Vol. 51, pp. 185–222. https://doi.org/10.1016/j.ijindorg.2016.09.002

8. Funding Circle Statistics. Internet access: www.fundingcircle.com/uk/statistics/

9. Ge, R., Feng, J., Gu, B., Zhang, P. (2017). Predicting and deterring default with social media information in peer-to-peer lending // Journal of Management Information Systems. Vol. 34, No. 2, pp. 401–424. https://doi.org/10.1080/07421222.2017.1334472

10. Guo, Y., Zhou, W., Luo, C., Liu, C., Xiong, H. (2016). Instance-Based Credit Risk Assessment for Investment Decisions in P2P Lending // European Journal of Operational Research. Vol. 249, No. 2, pp. 417–426. doi.org/10.1016/j.ejor.2015.05.050

11. Lei, X. (2016). Discussion of the Risks and Risk Control of P2P in China // Modern Economy. Vol. 7, No. 04, pp. 399. doi: 10.4236/me.2016.74043

12. Lichtenwald, R. (2014). The History of Peer to Peer Lending. Peer and Social Lending. Internet access: http://peersociallending.com/news/history-peer-peer-lending/

13. Lin, M., Prabhala, N. R., Viswanathan, S. (2013). Judging Borrowers by the Company they Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending // Management Science. Vol. 59, No. 1, pp. 17–35. https://doi.org/10.1080/00036846.2016.1262526

14. Lin, X., Li, X., Zheng, Z. (2017). Evaluating Borrower’s Default Risk in Peer-to-Peer Lending: Evidence from a Lending Platform in China // Applied Economics. Vol. 49, No. 35, pp. 3538–3545. https://doi.org/10.1080/00036846.2016.1262526

15. Liu, Y., Zhou, Q., Zhao, X., Wang, Y. (2018). Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending? // Emerging Markets Finance and Trade. Vol. 54, No. 13, pp. 2982–2994. https://doi.org/10.1080/1540496X.2018.1427061

16. Ma, H. Z., Wang, X. R. (2016). Influencing Factor Analysis of Credit Risk in P2P Lending Based on Interpretative Structural Modeling // Journal of Discrete Mathematical Sciences and Cryptography. Vol. 19, No. 3, pp. 777–786. https://doi.org/10.1080/09720529.2016.1178935

17. Malekipirbazari, M., Aksakalli, V. (2015). Risk Assessment in Social Lending Via Random Forests // Expert Systems with Applications. Vol. 42, No. 10, pp. 4621–4631. https://doi.org/10.1016/j.eswa.2015.02.001

18. Mateescu, A. (2015). Peer-to-Peer Lending. Data & Society Research Institute, pp. 19–25. Internet access: https://www.datasociety.net/pubs/dcr/PeertoPeerLending.pdf

19. Milne, A., Parboteeah, P. (2016). The Business Models and Economics of Peer-to-Peer Lending. Internet access: http://dx.doi.org/10.2139/ssrn.2763682

20. Pokorna, M., Sponer, M. (2016). Social Lending and its Risks // Procedia-Social and Behavioral Sciences. Vol. 220, pp. 330–337. https://doi.org/10.1016/j.sbspro.2016.05.506

21. Republic of Lithuania Law on Consumer Credit. No. XII-1989. Internet access: https://e-seimas.lrs.lt/portal/legalAct/lt/TAD/5c534e403c5c11e68f278e2f1841c088?jfwid=-vkzfyh270

22. Riggins, F. J., Weber, D. M. (2017). Information Asymmetries and Identification Bias in P2P Social Microlending // Information Technology for Development. Vol. 23, No. 1, pp. 107–126. https://doi.org/10.1080/02681102.2016.1247345

23. Serrano-Cinca, C., Gutierrez-Nieto, B., López-Palacios, L. (2015). Determinants of Default in P2P Lending // PloS One. Vol. 10, No. 10, pp. e0139427. https://doi.org/10.1371/journal.pone.0139427

24. Wei, S. (2015). Internet Lending in China: Status Quo, Potential Risks and Regulatory Options // Computer Law & Security Review. Vol. 31, No. 6, pp. 793–809. https://doi.org/10.1016/j.clsr.2015.08.005

25. Wooldridge, J. M. (2003). Introductory Econometrics: A Modern Approach. – Australia; Cincinnati, Ohio: South-Western College Pub., c2003.

26. Zhang, Y., Li, H., Hai, M., Li, J., Li, A. (2017). Determinants of Loan Funded Successful in Online P2P Lending // Procedia Computer Science. Vol. 122, pp. 896–901. https://doi.org/10.1016/j.procs.2017.11.452

Management of Organizations: Systematic Research

Organizacijų vadyba: sisteminiai tyrimai

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