Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent Systems in Accounting Finance & Management, 18(2–3), 59–88.
Angilella, S., & Mazzù, S. (2015). The financing of innovative SMEs: A multicriteria credit rating model. European Journal of Operational Research, 244(2), 540–554.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. JMLR.org.
Cornée, S. (2017). The relevance of soft information for predicting small business credit default: Evidence from a social bank. Journal of Small Business Management. doi: 10.1111/jsbm.12318 .
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447–1465.
Dorfleitner, G., Priberny, C., Schuster, S., Stoiber, J., Weber, M., Castro, I. D., et al. (2016). Description-text related soft information in peer-to-peer lending—Evidence from two leading european platforms. Journal of Banking & Finance, 64, 169–187.
Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating credit risk and loan performance in online peer-to-peer (p2p) lending. Applied Economics, 47(1), 54–70.
Finlay, S. (2011). Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research, 210(2), 368–378.
Friedman, N., Dan, G., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2–3), 131–163.
Gao, Q., & Lin, M. (July 15, 2016). Economic value of texts: Evidence from online debt crowdfunding. Available at SSRN: doi: 10.2139/ssrn.2446114 .
Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2015). Instance-based credit risk assessment for investment decisions in p2p lending. European Journal of Operational Research, 249(2), 417–426.
Hajek, P., & Michalak, K. (2013). Feature selection in corporate credit rating prediction. Knowledge-Based Systems, 51(1), 72–84.
Harris, T. (2013). Quantitative credit risk assessment using support vector machines: Broad versus narrow default definitions. Expert Systems with Applications, 40(11), 4404–4413.
Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847–856.
Iyer, R., Khwaja, A. I., Luttmer, E. F., & Shue, K. (2015). Screening peers softly: Inferring the quality of small borrowers. Management Science, 62(6), 1554–1577.
Hájek, P. (2011). Municipal credit rating modelling by neural networks. Decision Support Systems, 51(1), 108–118.
Kruppa, J., Schwarz, A., Arminger, G., & Ziegler, A. (2013). Consumer credit risk: Individual probability estimates using machine learning. Expert Systems with Applications, 40(13), 5125–5131.
Kruppa, J., Ziegler, A., & König, I. R. (2012). Risk estimation and risk prediction using machine-learning methods. Human Genetics, 131(10), 1639–1654.
Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine Learning, 59(1–2), 161–205.
Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136.
Liberti, J. M., & Petersen, M. A. (2017). Information: Hard and Soft. Working Paper.
Lin, M., Prabhala, N. R., & Viswanathan, S. (2013). Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1), 17–35.
Malekipirbazari, M., & Aksakalli, V. (2015). Risk assessment in social lending via random forests. Expert Systems with Applications, 42(10), 4621–4631.
Michels, J. (2012). Do unverifiable disclosures matter? Evidence from peer-to-peer lending. The Accounting Review, 87(4), 1385–1413.
Paul, S. (2014). Creditworthiness of a borrower and the selection process in micro-finance: A case study from the urban slums of India. Margin: The Journal of Applied Economic Research, 8(1), 59–75.
Pope, D. G., & Sydnor, J. R. (2011). What’s in a picture? Evidence of discrimination from prosper.com. Journal of Human Resources, 46(1), 53–92.
Puro, L., Teich, J. E., Wallenius, H., & Wallenius, J. (2010). Borrower decision aid for people-to-people lending. Decision Support Systems, 49(1), 52–60.
Shao, H., Ju, X., Wu, C., Xu, J., & Liu, M. (2012). Research on commercial bank credit risk evaluation model based on the integration of the probability distribution theory and the bp neural network technology. International Journal of Advancements in Computing Technology, 4(22), 115–128.
Thomas, L. C. (2010). Consumer finance: Challenges for operational research. Journal of the Operational Research Society, 61(1), 41–52.
Wang, G., Ma, J., Huang, L., & Xu, K. (2012). Two credit scoring models based on dual strategy ensemble trees. Knowledge-Based Systems, 26, 61–68.
Wang, S., Qi, Y., Fu, B., & Liu, H. (2016). Credit risk evaluation based on text analysis. International Journal of Cognitive Informatics & Natural Intelligence, 10(1), 1–11.
Wei, X., & Croft, W. B. (2006). LDA-based document models for ad-hoc retrieval. In International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 178–185). ACM.