Enhancing economic cycle forecasting based on interpretable machine learning and news narrative sentiment

The growing prevalence of uncertainty in global events poses significant challenges to economic cycle forecasting, emphasizing the need for more robust predictive models. This study addresses this gap by developing a novel forecasting framework that integrates multiple uncertainty indices to improve accuracy, stability, and interpretability, particularly during uncertainty shocks. To achieve this, several methodological innovations were implemented. First, news sentiment-based uncertainty indices were incorporated as candidate variables to capture uncertainty dynamics. Second, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO) was employed for efficient variable selection, mitigating the curse of dimensionality in small samples. Third, the multi-objective Lichtenberg algorithm (MOLA) was applied to optimize the prediction window size, ensuring model robustness. Additionally, a MOLA-based extreme gradient boosting (MOLA-XGBoost) model was developed to fine-tune hyperparameters across dimensions of prediction accuracy, stability, and directional consistency. Finally, SHapley Additive exPlanations (SHAP) theory was used to enhance model interpretability. This study forecasts China's economic cycle using multiple indicators, demonstrating that the proposed approach consistently delivers accurate and robust predictions even under uncertainty shocks. The findings highlight the crucial role of uncertainty indices in improving economic forecasts, offering new insights and methodologies for predictive modeling in volatile environments. • Proposed MOLA-XGBoost model for economic cycle forecasting • Implemented intelligent selection for prediction window size and model parameters • Multiple uncertainty indices are used to improve prediction accuracy. • MOLA-XGBoost shows excellent predictive performance and robustness for economic cycle forecasting and is interpretable.