A user guide of CART and random forests with applications in FinTech and InsurTech
Abstract In the realm of financial data analytics, machine learning techniques, particularly classification and regression trees (CARTs) and random forests, have shown remarkable efficiency. This article serves as a user guide for these methods, with an emphasis on their applicability and effectiveness in analyzing datasets in FinTech and InsurTech. In particular, we present several numerical examples and empirical studies, and demonstrate their superiority in handling data with a variety of input features, offering insights into their potential applications in the industries.
Authors
- Yongzhao Chen (ORCID: https://orcid.org/0000-0002-5725-2765)
- Sheung Chi Phillip Yam (ORCID: https://orcid.org/0000-0002-4380-0919)
- Ka Chun Cheung
- Ross Zhengyao Sun
Institutions
- Chinese University of Hong Kong (HK)
- Hang Seng University of Hong Kong (HK)
- University of Hong Kong (HK)
Publication Details
- Journal
- Japanese Journal of Statistics and Data Science
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1007/s42081-024-00258-x
- Citations
- 3
- Primary Topic
- Data Mining Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.34
Funders
- San Diego Supercomputer Center
- Hang Seng University of Hong Kong
- Deutscher Akademischer Austauschdienst
- University Grants Committee
- Chinese University of Hong Kong