Classification-Based Consensus Decision-Making for Portfolio Selection Considering Overconfidence
- DOI
- 10.2991/978-94-6239-830-6_22How to use a DOI?
- Keywords
- Portfolio; Fund team; Classification-based consensus; Overconfidence; Stock pool
- Abstract
Constructing a rational stock portfolio selection model is critical for enhancing investment returns. Given the negative impact of fund team members’ overconfidence on portfolio selection, we propose a classification-based consensus decision-making method and a minimum adjustment classification-based consensus model that consider overconfidence. By identifying overconfident members and providing adjustment recommendations, we aim to achieve group consensus on stock classifications. The case study and analysis indicate that overconfidence affects stock classifications, and identifying overconfident members effectively improves group classification-based consensus level. The research findings provide a theoretical basis for fund teams to identify overconfident members, and offer methodological guidance for fund companies to facilitate consensus-reaching in portfolio selection.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Faxin Cheng AU - Nan Liu AU - Tiantian Hao PY - 2026 DA - 2026/11/14 TI - Classification-Based Consensus Decision-Making for Portfolio Selection Considering Overconfidence BT - Proceedings of the 6th International Conference on Internet Finance and Digital Economy (ICIFDE 2026) PB - Atlantis Press SP - 270 EP - 285 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6239-830-6_22 DO - 10.2991/978-94-6239-830-6_22 ID - Cheng2026 ER -