Analyzing Peer-to-Peer Lending Secondary Market: What Determines the Successful Trade of a Loan Note?

Ajay Byanjankar; Jozsef Mezei; Xiaolu Wang

doi:10.1007/978-3-030-45691-7_44

Analyzing Peer-to-Peer Lending Secondary Market: What Determines the Successful Trade of a Loan Note?

Ajay Byanjankar, Jozsef Mezei, Xiaolu Wang

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

58 Downloads (Pure)

Abstract

Predicting loan default in peer-to-peer (P2P) lending has been a widely researched topic in recent years. While one can identify a large number of contributions predicting loan default on primary market of P2P platforms, there is a lack of research regarding the assessment of analytical methods on secondary market transactions. Reselling investments oﬀers a valuable alternative to investors in P2P market to increase their proﬁt and to diversify. In this article, we apply machine learning algorithms to build classiﬁcation models that can predict the success of secondary market oﬀers. Using data from a leading European P2P platform, we found that random forests oﬀer the best classiﬁcation performance. The empirical analysis revealed that in particular two variables have signiﬁcant impact on success in the secondary market: (i) discount rate and (ii) the number of days the loan had been in debt when it was put on the secondary market.

Original language	English
Title of host publication	Trends and Innovations in Information Systems and Technologies - Volume 2, WorldCIST 2020
Editors	Álvaro Rocha, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, Irena Orovic, Fernando Moreira
Publisher	Springer, Cham
Pages	471-481
ISBN (Electronic)	978-3-030-45691-7
ISBN (Print)	978-3-030-45690-0
DOIs	https://doi.org/10.1007/978-3-030-45691-7_44
Publication status	Published - 2020
MoE publication type	A4 Article in a conference publication
Event	KES International Conference on Intelligent Decision Technologies - Duration: 1 Jan 2020 → …

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1160 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	KES International Conference on Intelligent Decision Technologies
Period	01/01/20 → …

Access to Document

10.1007/978-3-030-45691-7_44

worldcist-2020.pdfAccepted author manuscript, 327 KBLicence: All rights reserved

https://urn.fi/URN:NBN:fi-fe202201147601

Cite this

Byanjankar, A., Mezei, J., & Wang, X. (2020). Analyzing Peer-to-Peer Lending Secondary Market: What Determines the Successful Trade of a Loan Note? In Á. Rocha, H. Adeli, L. P. Reis, S. Costanzo, I. Orovic, & F. Moreira (Eds.), Trends and Innovations in Information Systems and Technologies - Volume 2, WorldCIST 2020 (pp. 471-481). (Advances in Intelligent Systems and Computing; Vol. 1160 AISC). Springer, Cham. https://doi.org/10.1007/978-3-030-45691-7_44

Byanjankar, Ajay ; Mezei, Jozsef ; Wang, Xiaolu. / Analyzing Peer-to-Peer Lending Secondary Market: What Determines the Successful Trade of a Loan Note?. Trends and Innovations in Information Systems and Technologies - Volume 2, WorldCIST 2020. editor / Álvaro Rocha ; Hojjat Adeli ; Luís Paulo Reis ; Sandra Costanzo ; Irena Orovic ; Fernando Moreira. Springer, Cham, 2020. pp. 471-481 (Advances in Intelligent Systems and Computing).

@inproceedings{2751716776b14c26b1861e3ec81c98c6,

title = "Analyzing Peer-to-Peer Lending Secondary Market: What Determines the Successful Trade of a Loan Note?",

abstract = "Predicting loan default in peer-to-peer (P2P) lending has been a widely researched topic in recent years. While one can identify a large number of contributions predicting loan default on primary market of P2P platforms, there is a lack of research regarding the assessment of analytical methods on secondary market transactions. Reselling investments oﬀers a valuable alternative to investors in P2P market to increase their proﬁt and to diversify. In this article, we apply machine learning algorithms to build classiﬁcation models that can predict the success of secondary market oﬀers. Using data from a leading European P2P platform, we found that random forests oﬀer the best classiﬁcation performance. The empirical analysis revealed that in particular two variables have signiﬁcant impact on success in the secondary market: (i) discount rate and (ii) the number of days the loan had been in debt when it was put on the secondary market.",

author = "Ajay Byanjankar and Jozsef Mezei and Xiaolu Wang",

year = "2020",

doi = "10.1007/978-3-030-45691-7_44",

language = "English",

isbn = "978-3-030-45690-0",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer, Cham",

pages = "471--481",

editor = "{\'A}lvaro Rocha and Hojjat Adeli and Reis, {Lu{\'i}s Paulo} and Sandra Costanzo and Irena Orovic and Fernando Moreira",

booktitle = "Trends and Innovations in Information Systems and Technologies - Volume 2, WorldCIST 2020",

note = "KES International Conference on Intelligent Decision Technologies ; Conference date: 01-01-2020",

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Byanjankar, A, Mezei, J & Wang, X 2020, Analyzing Peer-to-Peer Lending Secondary Market: What Determines the Successful Trade of a Loan Note? in Á Rocha, H Adeli, LP Reis, S Costanzo, I Orovic & F Moreira (eds), Trends and Innovations in Information Systems and Technologies - Volume 2, WorldCIST 2020. Advances in Intelligent Systems and Computing, vol. 1160 AISC, Springer, Cham, pp. 471-481, KES International Conference on Intelligent Decision Technologies, 01/01/20. https://doi.org/10.1007/978-3-030-45691-7_44

Analyzing Peer-to-Peer Lending Secondary Market: What Determines the Successful Trade of a Loan Note? / Byanjankar, Ajay; Mezei, Jozsef ; Wang, Xiaolu.
Trends and Innovations in Information Systems and Technologies - Volume 2, WorldCIST 2020. ed. / Álvaro Rocha; Hojjat Adeli; Luís Paulo Reis; Sandra Costanzo; Irena Orovic; Fernando Moreira. Springer, Cham, 2020. p. 471-481 (Advances in Intelligent Systems and Computing; Vol. 1160 AISC).

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review