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Research on Identifying Key Indicators for Financial Risk Warning in Electric Power Enterprises Based on XGBoost Model

Year: 2025

Type: conference-paper

Abstract: Power enterprises operate in a complex environment characterized by multifaceted financial risks. This study constructs a financial risk early warning model for power enterprises, utilizing 33 financial and non-financial indicators and employing the XGBoost algorithm. The results demonstrate that the XGBoost model effectively predicts financial risks in power enterprises with high accuracy. Solvency indicators are identified as primary drivers of financial risk. Additionally, profitability metrics, operational efficiency indicators, and the Tobin's Q ratio significantly influence the financial risk profile of power enterprises.

DOI: 10.1145/3746972.3746988

Authors: Lanjun Xu, Yunbo Zhou, Zhi Li, Lifeng Liu, Liyu Xia

Institutions: Nankai University, State Grid Corporation of China (China), State Grid Zhejiang Electric Power Company (China)

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Funder: State Grid Corporation of China

OpenAlex ID: W4412822338

FWCI:

Cites: 7

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Related to: 10