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Metaheuristic-based portfolio optimization in peer-to-peer lending platforms

Author

Listed:
  • Hadis Abbasi

    (Islamic Azad University
    Birmingham City University)

  • Shahrooz Bamdad

    (Birmingham City University)

  • Morteza Rahimi

    (Birmingham City University
    Kharazmi University)

Abstract

In recent years, researchers have paid increasing attention to Peer to Peer lending market. In this lending method, the lenders don’t usually have enough knowledge to understand how much money must be allocated in each loan. So, it is important to help investors to assess the loans. According to the needs of investors, generally, a bi-objective optimization model is considered that maximizes return and minimizes risk. In current work, internal rate of return as return and probability of default as risk variable is considered. First, an artificial neural network is used to evaluate the return. Then, we apply logistic regression to predict the probability of loan default. After providing the model, due to its complexity, one of the essential issues is solving it. In this study, the model is solved by metaheuristic algorithms. This is done from different points of view, including non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and Pareto envelope-based selection algorithm II. Finally, from the perspective of an investor, the best algorithm is introduced.

Suggested Citation

  • Hadis Abbasi & Shahrooz Bamdad & Morteza Rahimi, 2024. "Metaheuristic-based portfolio optimization in peer-to-peer lending platforms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 3629-3642, August.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:8:d:10.1007_s13198-023-02074-0
    DOI: 10.1007/s13198-023-02074-0
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    References listed on IDEAS

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    1. Yam, Sheung Chi Phillip & Yang, Hailiang & Yuen, Fei Lung, 2016. "Optimal asset allocation: Risk and information uncertainty," European Journal of Operational Research, Elsevier, vol. 251(2), pages 554-561.
    2. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    3. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    4. Ghatak, Maitreesh, 1999. "Group lending, local information and peer selection," Journal of Development Economics, Elsevier, vol. 60(1), pages 27-50, October.
    5. Golnoosh Babaei & Shahrooz Bamdad, 2021. "A New Hybrid Instance-Based Learning Model for Decision-Making in the P2P Lending Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 419-432, January.
    6. Marc S. Paolella, 2015. "Multivariate asset return prediction with mixture models," The European Journal of Finance, Taylor & Francis Journals, vol. 21(13-14), pages 1214-1252, November.
    7. Saurav Raj & Sheila Mahapatra & Chandan Kumar Shiva & Biplab Bhattacharyya, 2021. "Implementation and Optimal Sizing of TCSC for the Solution of Reactive Power Planning Problem Using Quasi-Oppositional Salp Swarm Algorithm," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 10(2), pages 74-103, April.
    8. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
    9. Sheila Mahapatra & Nitin Malik & Saurav Raj & Mohan Krishna Srinivasan, 2022. "Constrained optimal power flow and optimal TCSC allocation using hybrid cuckoo search and ant lion optimizer," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 721-734, April.
    10. Anuj Kumar & Sangeeta Pant & Mangey Ram & Shshank Chaube, 2019. "Multi-objective grey wolf optimizer approach to the reliability-cost optimization of life support system in space capsule," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(2), pages 276-284, April.
    11. Cuiqing Jiang & Zhao Wang & Ruiya Wang & Yong Ding, 2018. "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, Springer, vol. 266(1), pages 511-529, July.
    12. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    13. Selcuk Bayraci, 2017. "Application of profit-based credit scoring models using R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 3-28, December.
    14. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    15. Vahid Baradaran & Amir Hossein Hosseinian, 2020. "A bi-objective model for redundancy allocation problem in designing server farms: mathematical formulation and solution approaches," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(5), pages 935-952, October.
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