An Overview of Clustering Methods in The Financial World
These authors contributed equally
- DOI
- 10.2991/aebmr.k.220307.084How to use a DOI?
- Keywords
- Clustering; Financial application; Machine learning
- Abstract
This paper reviews the widely used clustering methods: K-mean, MST, and the hierarchical approach, along with its application in financial fields. Specifically, it includes a discussion of the application in credit scoring, stock market, portfolio selection, and trading strategy. Moreover, significant challenges and future research directions are also being identified. It is founded that clustering could have wide application in varied financial fields, however, challenges might be solved by future in-depth research. This paper aims at helping other researchers to select the best-fit algorithms to conduct their research and provide an analytical base for people who have an interest in this topic. Additionally, business analysts and quantitative researchers might be able to leverage the insights.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Geyang Tang AU - Rujian Tian AU - Bingdi Wu PY - 2022 DA - 2022/03/26 TI - An Overview of Clustering Methods in The Financial World BT - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) PB - Atlantis Press SP - 524 EP - 529 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220307.084 DO - 10.2991/aebmr.k.220307.084 ID - Tang2022 ER -