Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market

Jiaqi Kuang; Xudong Ji; Peng Cheng; Vasileios Bill Kallinterakis

doi:10.3390/systems11030133

Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market

Jiaqi Kuang^*, Xudong Ji, Peng Cheng, Vasileios Bill Kallinterakis

^*Corresponding author for this work

Department of Accounting

Research output: Contribution to journal › Article › peer-review

Abstract

This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform’s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.

Original language	English
Article number	133
Journal	Systems
Volume	11
Issue number	3
DOIs	https://doi.org/10.3390/systems11030133
Publication status	Published - Mar 2023

Keywords

asymmetry effect
media sentiment
peer-to-peer lending market
social media sentiment

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10.3390/systems11030133

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title = "Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market",

abstract = "This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform{\textquoteright}s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.",

keywords = "asymmetry effect, media sentiment, peer-to-peer lending market, social media sentiment",

author = "Jiaqi Kuang and Xudong Ji and Peng Cheng and Kallinterakis, {Vasileios Bill}",

note = "Publisher Copyright: {\textcopyright} 2023 by the authors.",

year = "2023",

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doi = "10.3390/systems11030133",

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T1 - Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market

AU - Kuang, Jiaqi

AU - Ji, Xudong

AU - Cheng, Peng

AU - Kallinterakis, Vasileios Bill

PY - 2023/3

Y1 - 2023/3

N2 - This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform’s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.

AB - This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform’s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.

KW - asymmetry effect

KW - media sentiment

KW - peer-to-peer lending market

KW - social media sentiment

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U2 - 10.3390/systems11030133

DO - 10.3390/systems11030133

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AN - SCOPUS:85151133656

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VL - 11

JO - Systems

JF - Systems

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M1 - 133

ER -

Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market

Abstract

Keywords

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