Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China
Year of publication: |
2019
|
---|---|
Authors: | Tang, Lingxiao ; Cai, Fei ; Ouyang, Yao |
Published in: |
Technological forecasting & social change : an international journal. - Amsterdam : Elsevier, ISSN 0040-1625, ZDB-ID 280700-2. - Vol. 144.2019, p. 563-572
|
Subject: | Credit risk | Random forest | Energy industry | Overdraft ratio | Energiewirtschaft | Energy sector | Kreditrisiko | Forstwirtschaft | Forestry | China |
-
Šljamin, Valerij Aleksandrovič, (2009)
-
In search of distress premium in the Chinese energy sector
Zhang, Xuan, (2024)
-
Economic trends in the transition into a circular bioeconomy
Kircher, Manfred, (2022)
- More ...
-
Growth and Transformation of Emerging Powers : Research on BRICS Economies
Ouyang, Yao, (2019)
-
Growth and transformation of emerging powers : research on BRICS economies
Ouyang, Yao, (2019)
-
A new model for intuitionistic fuzzy multi-attributes decision making
Ouyang, Yao, (2016)
- More ...