Machine Learning Identification of Capital Structure Resilience in New Energy Firms under Energy Market Volatility Shocks

Abstract

Global energy markets continue to fluctuate under geopolitical conflicts, supply demand mismatch, and the pressure of low carbon transition. These conditions place stronger external pressure on the financing stability and capital structure adjustment capacity of new energy firms. Photovoltaic and lithium battery firms usually have high R&D expenditure, long capacity expansion cycles, and strong demand for debt financing. High energy price volatility may affect their capital structure resilience through market expectations, financing costs, and cash flow pressure. Unbalanced panel data are constructed from A share listed photovoltaic and lithium battery firms from 2014 to 2024. High volatility energy price shocks are defined when the quarterly return volatility of Brent, WTI, or natural gas prices exceeds the 75th percentile of the sample period. Capital structure resilience labels are then constructed according to post shock leverage, interest coverage ratio, and cash to short term debt ratio. Logistic regression, random forest, XGBoost, and LightGBM are used for classification. SHAP is used to explain key feature contributions. The results show that XGBoost performs well in AUC, F1 score, and recall of low resilience firms. Short term debt ratio, cash to short term debt ratio, operating cash flow volatility, and Brent price volatility are important variables for distinguishing high resilience firms from low resilience firms. The analysis provides an explainable quantitative basis for financial risk identification, debt structure optimization, and investment screening under energy market shocks.