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The Predicting Power of Soft Information on Defaults in the Chinese P2P Lending Market

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  • Yao Wang

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)

  • Zdenek Drabek

    (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic)

  • Zhengwei Wang

    (Tsinghua University, PBC School of Finance)

Abstract

Online peer to peer lending (P2P)– allows people who want to borrow money to submit their applications on the platform and individual investors can make bids on the loan listings. The quality of information in credit appraisal becomes paramount in this market. The existing research to assess the role of what is known as soft information in P2P markets has so far been very limited and, inconclusive due to differences in approaches and methodological limitations. The aim of the paper is to discuss the role of soft information channels in predicting defaults in the P2P lending market and to assess the importance of soft information in the Fintech companies’ credit analysis. Using a unique data of the Chinese P2P lending platform RRDai.com and new approach based on sets of hard and soft information, we compare the predicting performance of soft information, hard information and the combined role of both hard and soft information. We show that soft information can provide a valuable input in credit appraisal. The predicting power of soft information in our test was high, and together with hard information it can even help improve the loan performance. In exceptional situations characterized by the absence of hard financial data, soft information could be used, with caution, as an alternative.

Suggested Citation

  • Yao Wang & Zdenek Drabek & Zhengwei Wang, 2018. "The Predicting Power of Soft Information on Defaults in the Chinese P2P Lending Market," Working Papers IES 2018/20, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jan 2018.
  • Handle: RePEc:fau:wpaper:wp2018_20
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    Keywords

    Soft Information; P2P Lending; Fintech; Microfinance; Credit Analysis; Empirical Study;
    All these keywords.

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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