Work

Using deep learning technology to predict the financial health status of enterprises in low-carbon economy

Year: 2025

Type: article

Abstract: In the context of a low-carbon economy, the forecasting of corporate financial risk necessitates a more intricate methodology to address the challenges. This study is predicated on a financial risk forecasting indicator system, incorporating relevant metrics associated with the low-carbon economy. It employs the Long Short-Term Memory (LSTM) model for modeling purposes and integrates the Multi-Strategy Gray Wolf Optimization (MSGWO) algorithm to construct a forecasting model named MSGWO–LSTM. This ensemble harnesses the formidable representational capabilities of deep learning alongside the parameter adjustment advantages of optimization algorithms, enhancing the accuracy and stability of financial risk predictions.

Source: International Journal of Low-Carbon Technologies

DOI: 10.1093/ijlct/ctaf098

Author: Xiaoling Zha

Institution: Jiangsu Vocational College of Finance and Economics

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,886