Prediction of financial distress: An empirical study of listed Chinese companies using data mining
By Ruibin Geng, Indranil Bose, Xi Chen. European Journal of Operational Research, 2015
455 citations recorded by the source corpus.
Subfields: Accounting, Artificial Intelligence, Finance
View Paper: https://www.semanticscholar.org/paper/3417b3e49c1d00019a28c02ae01f110c7b9ffcff
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Accounting
In Artificial Intelligence
- J. Tomczak
- Maciej Ziȩba
- Sebastian Klaudiusz Tomczak
- Luis A. Lastras
- Sebastian Nowozin
- Max Welling
- Stefano Ermon
In Finance
Citations in the corpus
- Research on financial early warning of mining listed companies based on BP neural network model (2021, unknown): “…and reasonable financial early warning model with ideal prediction accuracy, so that managers of listed companies can find financial risks as soon as possible, and make judgments on the company ’ s financial status and operation status (Shen et al., 2020; Geng et al., 2015; Zhou et al., 2015).”
- Big data techniques in auditing research and practice: Current trends and future opportunities (2018, unknown): “Additional research supports their approach and suggests that neural networks perform better for financial distress modelling than decision trees and alternative approaches such as support vector machines (Geng, Bose, & Chen, 2015).”
- Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction (2016, methodology): “This approach applies neural networks to the bankruptcy prediction (Bell et al., 1990; Koster et al., 1991; Cadden, 1991; Tam, 1991; Coats and Fant, 1991; Salchenberger et al., 1992; Tam and Kiang, 1992; Wilson and Sharda, 1994; Serrano-Cinca, 1996; Zhang et al., 1999; Geng et al., 2015).”
- Artificial neural networks in business: Two decades of research (2016, unknown): “Financial distress & bankruptcy (76) [8, 13, 16, 17, 30, 31, 38, 40, 60, 62, 64, 65, 74, 77, 78, 86, 90, 97, 98, 107, 128, 129, 147, 145, 158, 159, 167, 168, 176, 177, 179, 180, 181, 183, 185, 189, 193, 194, 201, 203, 204, 212, 213, 218, 220, 223, 227, 243, 253, 254, 257, 266, 274, 288, 308, 318, 32”