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

Xiaoling Zha

International Journal of Low-Carbon Technologies, 2025, vol. 20, 1488-1494

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.

Keywords: low-carbon economy; deep learning; Gray Wolf Optimization; financial risk (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:1488-1494.

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