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

Ajay Byanjankar, Jozsef Mezei, Xiaolu Wang

    Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

    2 Citeringar (Scopus)
    69 Nedladdningar (Pure)

    Sammanfattning

    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.

    OriginalspråkEngelska
    Titel på värdpublikationTrends and Innovations in Information Systems and Technologies - Volume 2, WorldCIST 2020
    RedaktörerÁlvaro Rocha, Hojjat Adeli, Luís Paulo Reis, Sandra Costanzo, Irena Orovic, Fernando Moreira
    FörlagSpringer, Cham
    Sidor471-481
    ISBN (elektroniskt)978-3-030-45691-7
    ISBN (tryckt)978-3-030-45690-0
    DOI
    StatusPublicerad - 2020
    MoE-publikationstypA4 Artikel i en konferenspublikation
    EvenemangKES International Conference on Intelligent Decision Technologies -
    Varaktighet: 1 jan. 2020 → …

    Publikationsserier

    NamnAdvances in Intelligent Systems and Computing
    Volym1160 AISC
    ISSN (tryckt)2194-5357
    ISSN (elektroniskt)2194-5365

    Konferens

    KonferensKES International Conference on Intelligent Decision Technologies
    Period01/01/20 → …

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