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Analyzing default risk among P2P platforms based on the LAS-STACK method by considering multidimensional signals under specific economic contexts

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Abstract

P2P platform default risk seriously affects the returns of investors, which may cause systemic financial risks. The existing literature mostly focuses on borrower risk, ignoring the research on P2P platform default risk. This paper uses signal theory and data mining-related methods to study the default risk prediction of P2P platforms that integrate soft and hard information signals in different economic environments. First, using the cluster analysis method, the macroeconomic environment of P2P platforms is studied. Second, from the perspective of signal costs, signal theory is used to analyze the impacts of soft and hard information risk signals on platform default in different economic environments. Finally, by integrating the lasso and stacking methods, a LAS-STACK model is proposed to study the prediction of P2P platform default risk in the high-dimensional unbalanced data context. The conclusions of this paper show that the fusion of soft and hard information can better predict the default risk of P2P platforms, especially during periods with low economic levels. Additionally, the LAS-STACK model has a better prediction ability for the P2P platform default risk in the high-dimensional unbalanced data context. This study can improve the ability of regulators and P2P platforms to warn and manage default risks in a specific economic environment and protect investors' returns.

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Abbreviations

P2P:

Peer-to-peer lending

LAS-STACK:

It is produced by fusion of lasso and stacking

FA:

Hard information features

FB:

Soft information features

FC:

Macroeconomic features

ICP:

A license to operate a website for an internet content provider

SVM:

Support vector machine

OS:

The oversampling method

US:

The undersampling method

SMOTE:

Synthetic minority oversampling technique

CSL:

Cost-sensitive learning

RF:

Random forest

TP:

True positive

TN:

True negative

FP:

False positive

FN:

False negative

AA:

Average accuracy

FPR:

False positive rate (also called Type I error)

FNR:

False negative rate (also called Type II error)

TPR:

True positive rate

ROC:

Receiver operating characteristic curve

AUC:

Area under ROC curve

SSE:

Sum of squared errors within clusters

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Acknowledgements

The authors have declared that no conflicts of interest exist. I would like to declare on behalf of my coauthors that the work described is original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part. This research is supported by the Science Foundation of the Ministry of Education of China (No. 18YJC630082), the National Natural Science Foundation of China (No. 71731005), and the Natural Science Foundation of Anhui Province (No. 1908085QG307).

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Authors and Affiliations

  1. School of Business, Anhui University, Hefei, China

    Kun Liang & Chen Zhang

  2. School of Management, Hefei University of Technology, Hefei, China

    Cuiqing Jiang

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  1. Kun Liang
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Correspondence to Kun Liang.

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Liang, K., Zhang, C. & Jiang, C. Analyzing default risk among P2P platforms based on the LAS-STACK method by considering multidimensional signals under specific economic contexts. Electron Commer Res 22, 77–111 (2022). https://doi.org/10.1007/s10660-021-09505-9

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