Default prediction in P2P lending from high-dimensional data based on machine learning
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DOI: 10.1016/j.physa.2019.122370
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References listed on IDEAS
- Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2018. "The role of punctuation in P2P lending: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 634-643.
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Cited by:
- Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
- Liu, Wanan & Fan, Hong & Xia, Meng, 2023. "Tree-based heterogeneous cascade ensemble model for credit scoring," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1593-1614.
- Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
- Pang, Professor Sulin & Hou, Xianyan & Xia, Lianhu, 2021. "Borrowers’ credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
- Shen, Feng & Zhang, Xin & Wang, Run & Lan, Dao & Zhou, Wei, 2022. "Sequential optimization three-way decision model with information gain for credit default risk evaluation," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1116-1128.
- Ramezani, Zahra & Pourdarvish, Ahmad, 2021. "Transfer learning using Tsallis entropy: An application to Gravity Spy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
- João A. Bastos, 2022. "Predicting Credit Scores with Boosted Decision Trees," Forecasting, MDPI, vol. 4(4), pages 1-11, November.
- Xiwen Cui & Shaojun E & Dongxiao Niu & Bosong Chen & Jiaqi Feng, 2021. "Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
- Hülya Yürekli & Öyküm Esra Yiğit & Okan Bulut & Min Lu & Ersoy Öz, 2022. "Exploring Factors That Affected Student Well-Being during the COVID-19 Pandemic: A Comparison of Data-Mining Approaches," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
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Keywords
Default prediction; High-dimensional data; Imbalanced data; Machine learning; P2P lending;All these keywords.
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