Improving Financial Distress Prediction Using Financial Network-Based Information and GA-Based Gradient Boosting Method

Jiaming Liu, Chong Wu () and Yongli Li ()
Additional contact information
Jiaming Liu: Harbin Institute of Technology
Chong Wu: Harbin Institute of Technology
Yongli Li: Northeastern University

Computational Economics, 2019, vol. 53, issue 2, No 16, 872 pages

Abstract: Abstract Previous studies on financial distress prediction have chiefly used financial indicators which derived from financial statements as explanatory variables, so some potentially useful information that contained in the financial network was not considered. The listed companies can be represented as a complex financial network which the firms are regarded as nodes and the links account for stock returns correlation. The purpose of this study is to investigate whether network-based variables can improve the predictive power of financial distress prediction. Therefore, this study proposed a genetic algorithm (GA) approach to parameter selection in gradient boosting decision tree and integrated network-based variables for financial distress prediction. In order to verify the prediction capability of network-based variables and GA-based gradient boosting method in financial distress prediction, empirical study based on Chinese listed firms’ real data is employed, and comparative analysis is conducted. The experiment results indicate that the introduction of network-based variables and GA-based gradient boosting method for financial distress prediction can enhance predictive performance in terms of accuracy, recall, precision, F-score, type I error, and type II error.

Keywords: Financial distress prediction; Financial network; Network-based variable; Gradient boosting method; Genetic algorithm (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9768-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:53:y:2019:i:2:d:10.1007_s10614-017-9768-3

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9768-3

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().