Textual sentiment of comments and collapse of P2P platforms: Evidence from China's P2P market

Wang, Chao; Zhang, Yue; Zhang, Weiguo; Gong, Xue

doi:10.1016/j.ribaf.2021.101448

Cited by 12 publications

(4 citation statements)

References 33 publications

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“…One of the papers written by Zhang W. related to this issue is entitled “Textual sentiment of comments and collapse of P2P platforms: Evidence from China's P2P market” which aims to explore the opinion and sentiment of comments from investors listed on the peer-to-peer lending platform and their effects against potential failures of the platform. The study's empirical results show that the investor community's positive comments on the platform indicate a lower probability of failure ( Wang et al., 2021 ).…”

Section: Resultsmentioning

confidence: 99%

A Bibliometric mapping of peer-to-peer lending research based on economic and business perspective

Kholidah

Hijriah²,

Mawardi³

et al. 2022

Heliyon

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Section: Resultsmentioning

confidence: 99%

A Bibliometric mapping of peer-to-peer lending research based on economic and business perspective

Kholidah

Hijriah²,

Mawardi³

et al. 2022

Heliyon

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“…Many researchers deploy machine learning techniques to forecast whether a P2P lending platform loan can be repaid or when repayment is due. The machine learning technique is applied to increase the accuracy of spotting defaults, such as Logistic Regression [30]- [32], Decision Tree [33], Neural Networks [34]- [36], Support Vector Machine [37], Random Forest [38]- [40], Gradient Boosting Decision Trees [14], [25], [41], and Convolutional Neural Networks [42], [43]. Other algorithms, such as naïve bayes [44][45], C4.5 [46], [47], and K-Nearest Neighbor [48], [49], can also be used as they have the potential to do classification tasks.…”

Section: The Evaluation Of Credit Risk In Peer-to-peer Lending Platformsmentioning

confidence: 99%

Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization

Dasril

Muslim

Hakim

et al. 2023

regist. j. ilm. teknol. sist. inf.

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The credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. The machine learning algorithm that can be used to evaluate credit risk as LightGBM, however, the results in evaluating P2P lending need to be improved. The aim of this research is to improve the accuracy of the LightGBM algorithm by combining the Particle Swarm Optimization (PSO) algorithm. The novelty developed in this research is combining LightGBM with PSO for large data from the Lending Club Dataset which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% of accuracy, 90.514% of Recall, and 97.754% of NPV respectively. The combination of LightGBM and PSO shows better results.

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“…Li (2020) indicated that decision makers with lower realization ability for textual messages normally have a higher chance of reaching irrational judgments on investments, pension funds, and savings. Zhang et al (2020) and Wang et al (2021) considered textual information to develop a credit risk prediction model. They concluded that the model with textual information performs better than the model without textual information.…”

Section: Textual Information Adopted In the Decision-making Processmentioning

confidence: 99%

Valuing and Risk Analysis for Supply Chain Management

Lin¹,

Chang

Hsu

2023

Journal of Global Information Management

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This research presents the contribution of profitability and asset utilization to a firm's value generation from a financial market viewpoint via slack-based measure network data envelopment analysis (SBM-NDEA). Despite its superiority in performance measurement, SBM-NDEA has a limitation when confronted with new added datasets as it lacks predictive ability. To overcome this, the authors integrate twin support vector machine into it. A manager's attitude toward risks also plays an essential role in efficiency improvement and value generation, but numerical messages do not convey such information. Textual messages with elastic natures thus bring information beyond just numerical messages. To assist users in quantifying risk types, the authors introduced an advanced text analyzer to conjecture a manager's attitude toward each risk. The results show that the performance evaluation model with forecasting capability can shift the manager's role from monitoring the past to planning the future. This study also demonstrates that the model with textual information reaches superior forecasting performance.

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Textual sentiment of comments and collapse of P2P platforms: Evidence from China's P2P market

Cited by 12 publications

References 33 publications

A Bibliometric mapping of peer-to-peer lending research based on economic and business perspective

A Bibliometric mapping of peer-to-peer lending research based on economic and business perspective

Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization

Valuing and Risk Analysis for Supply Chain Management

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