Update search
Filter
- All
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
Filter
- All
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
Filter
- All
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
Filter
- All
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
Filter
- All
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
Filter
- All
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- EISBN
- ISSN
- EISSN
- Issue
- Volume
- References
NARROW
Format
Journal
Type
Date
Availability
1-2 of 2
Keywords: Machine learning
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Journal:
China Finance Review International
China Finance Review International 1–21.
Published: 20 March 2026
.... Design/methodology/approach The study employs a quantitative research design using a comprehensive sample of Chinese financial analyst reports. Machine learning techniques for facial analysis are applied to assess the trustworthiness of analysts, and the relationship between facial trustworthiness...
Journal Articles
Journal:
China Finance Review International
China Finance Review International (2025) 15 (2): 409–441.
Published: 05 December 2024
..., suffers from limited coverage, particularly before 2016, leading to low statistical power and sample selection bias. To address this, we employ machine learning methodologies to forecast the carbon emissions of firms that do not disclose such information, nearly quadrupling the sample size. Our findings...
Includes: Supplementary data
