research-article

Enhancing investment decisions in P2P lending: an investor composition perspective

Authors:

Guishi DengAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 292 - 300

https://doi.org/10.1145/2020408.2020458

Published: 21 August 2011 Publication History

Abstract

P2P lending, as a novel economic lending model, has imposed new challenges about how to make effective investment decisions. Indeed, a key challenge along this line is how to align the right information with the right people. For a long time, people have made tremendous efforts in establishing credit records for the borrowers. However, information from investors is still under-explored for improving investment decisions in P2P lending. To that end, we propose a data driven investment decision-making framework, which exploits the investor composition of each investment for enhancing decisions making in P2P lending. Specifically, we first build investor profiles based on quantitative analysis of past performances, risk preferences, and investment experiences of investors. Then, based on investor profiles, we develop an investor composition analysis model, which can be used to select valuable investments and improve the investment decisions. To validate the proposed model, we perform extensive experiments on the real-world data from the world's largest P2P lending marketplace. Experimental results reveal that investor composition can help us evaluate the profit potential of an investment and the decision model based on investor composition can help investors make better investment decisions.

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Jiang SZhou WGuo YXiong H(2024)Multiple financial analyst opinions aggregation based on uncertainty-aware quality evaluationEuropean Journal of Operational Research10.1016/j.ejor.2024.08.024Online publication date: Aug-2024
https://doi.org/10.1016/j.ejor.2024.08.024
Sulastri RJanssen M(2023)Challenges in designing an inclusive Peer-to-peer (P2P) lending systemProceedings of the 24th Annual International Conference on Digital Government Research10.1145/3598469.3598475(55-65)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.1145/3598469.3598475
Wang YNi X(2023)A Survey of Machine Learning Methodologies for Loan Evaluation in Peer-to-Peer (P2P) LendingData Analytics for Management, Banking and Finance10.1007/978-3-031-36570-6_1(1-49)Online publication date: 5-Jun-2023
https://doi.org/10.1007/978-3-031-36570-6_1
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Index Terms

Enhancing investment decisions in P2P lending: an investor composition perspective
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 August 2011

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Jiang SZhou WGuo YXiong H(2024)Multiple financial analyst opinions aggregation based on uncertainty-aware quality evaluationEuropean Journal of Operational Research10.1016/j.ejor.2024.08.024Online publication date: Aug-2024
https://doi.org/10.1016/j.ejor.2024.08.024
Sulastri RJanssen M(2023)Challenges in designing an inclusive Peer-to-peer (P2P) lending systemProceedings of the 24th Annual International Conference on Digital Government Research10.1145/3598469.3598475(55-65)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.1145/3598469.3598475
Wang YNi X(2023)A Survey of Machine Learning Methodologies for Loan Evaluation in Peer-to-Peer (P2P) LendingData Analytics for Management, Banking and Finance10.1007/978-3-031-36570-6_1(1-49)Online publication date: 5-Jun-2023
https://doi.org/10.1007/978-3-031-36570-6_1
Zhang LWu XZhao HCheng FLiu Q(2022)Personalized Recommendation in P2P Lending Based on Risk-Return Management: A Multi-Objective PerspectiveIEEE Transactions on Big Data10.1109/TBDATA.2020.29934468:4(1141-1154)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TBDATA.2020.2993446
Liu YMa HJiang YLi Z(2022)Learning to recommend via random walk with profile of loan and lender in P2P lendingExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114763174:COnline publication date: 6-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2021.114763
Dimitropoulos P(2022)A Review of Internet Financing Through Peer-to-Peer Lending: A Cross-Country Comparative AnalysisBusiness Advancement through Technology Volume I10.1007/978-3-031-07769-2_4(73-96)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-3-031-07769-2_4
Guo YJiang SZhou WLuo CXiong H(2021)A predictive indicator using lender composition for loan evaluation in P2P lendingFinancial Innovation10.1186/s40854-021-00261-17:1Online publication date: 22-Jun-2021
https://doi.org/10.1186/s40854-021-00261-1
Umarovich JBakhtiyorovich R(2021)Modeling the decision-making process of lenders based on blockchain technology2021 International Conference on Information Science and Communications Technologies (ICISCT)10.1109/ICISCT52966.2021.9670211(1-5)Online publication date: 3-Nov-2021
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Liu CMing YXiao YZheng WHsu C(2021)Finding the Next Interesting Loan for Investors on a Peer-to-Peer Lending PlatformIEEE Access10.1109/ACCESS.2021.31035109(111293-111304)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3103510
Liu YMa HJiang YLi Z(2021)Modelling risk and return awareness for p2p lending recommendation with graph convolutional networksApplied Intelligence10.1007/s10489-021-02680-0Online publication date: 31-Jul-2021
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