Explainable machine learning to predict the cost of capital - Bussmann, Niklas; Giudici, Paolo; Tanda, Alessandra; Yu, Ellen Pei-Yi (Frontiers in Artificial Intelligence, 2025)
This study investigates the impact of financial and non-financial factors on a firm's ex-ante cost of capital, which is the reflection of investors' perception on a firm's riskiness. Departing from previous literature, we apply the XGBoost algorithm and two explainable Artificial Intelligence methods, namely the Shapley value approach and Lorenz Model Selection to a sample of more than 1,400 listed companies worldwide. Results confirm the relevance of key financial indicators such as firm size, ROE, firm portfolio risk, but also individuate firm's non-financial features and country's institutional quality as relevant predictors for the cost of capital. These results suggest the importance of non-financial indicators and country institutional quality on the firm's ex-ante cost of equity that expresses investors' risk perception. Our findings pave the way for future investigations on the impact of ESG and country factors in predicting the cost of capital.