Articlegold2019

Application of Instance-Based Entropy Fuzzy Support Vector Machine in Peer-To-Peer Lending Investment Decision

Poongjin Cho · Woojin Chang · Jae Wook Song

42 citations0/0 files storedIEEE Access

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Financial Distress and Bankruptcy Prediction FinTech, Crowdfunding, Digital Finance Imbalanced Data Classification Techniques

Abstract

Loan status prediction is an effective tool for investment decisions in peer-to-peer (P2P) lending market. In P2P lending market, most borrowers fulfill the repayment plan; however, some of them fail to pay back their loans. Therefore, an imbalanced classification method can be utilized to discriminate such default borrowers. In this context, the aim of this paper is to propose an investment decision model in P2P lending market which consists of fully paid loans classified via the instance-based entropy fuzzy support vector machine (IEFSVM). IEFSVM is a modified version of the existing entropy fuzzy support vector machine (EFSVM) in terms of an instance-based scheme. IEFSVM can reflect the pattern of nearest neighbors entropy with respect to the change of its size instead of fixing it in unified neighborhood size. Therefore, IEFSVM allows the class change of nearest neighbors in the determination of fuzzy membership. Applying the model to the lending club dataset, we determine loans that are predicted to be fully paid. Then, we also provide a multiple regression model to generate an investment portfolio based on non-default loans that are predicted to yield high returns. Throughout the experiment, the empirical results reveal that IEFSVM outperforms not only EFSVM but also the six other state-of-the-art classifiers including the cost-sensitive adaptive boosting, cost-sensitive random forest, EasyEnsemble, random undersampling boosting, weighted extreme learning machine, and cost-sensitive extreme gradient boosting in terms of loan status classification. Also, the investment performance of the multiple regression model using IEFSVM is higher and more robust than that of two other benchmarks. In this regard, we conclude that the proposed investment model is a decent and practical approach to support decisions in the P2P lending market.

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References

1994The Sharpe Ratio 2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2017A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring 2006Bagging and Boosting Classification Trees to Predict Churn 2017A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction 2015Determinants of Default in P2P Lending 2015Instance-based credit risk assessment for investment decisions in P2P lending

Cited by

2021Crowdlending: mapping the core literature and research frontiers 2020Research on investment decision-making model from the perspective of “Internet of Things + Big data”2021Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions 2024Profit-sensitive machine learning classification with explanations in credit risk: The case of small businesses in peer-to-peer lending 2020A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

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