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...
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| Published in: | Technological forecasting & social change Vol. 215; p. 124094 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Inc
01.06.2025
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| ISSN: | 0040-1625 |
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| Abstract | 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.
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•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. |
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| AbstractList | 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.
[Display omitted]
•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. |
| ArticleNumber | 124094 |
| Author | Chen, Xihui Haviour Sun, Weixin Wang, Yong Zhang, Li Hoang, Yen Hai |
| Author_xml | – sequence: 1 givenname: Weixin surname: Sun fullname: Sun, Weixin email: sunweixin@stumail.dufe.edu.cn organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 2 givenname: Yong surname: Wang fullname: Wang, Yong email: ywang@dufe.edu.cn organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 3 givenname: Li surname: Zhang fullname: Zhang, Li organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 4 givenname: Xihui Haviour surname: Chen fullname: Chen, Xihui Haviour email: X.chen@keele.ac.uk organization: Keele Business School, Keele University, UK – sequence: 5 givenname: Yen Hai surname: Hoang fullname: Hoang, Yen Hai email: yenhh@ueh.edu.vn organization: School of Banking, University of Economics HCM City, Viet Nam |
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| Keywords | Uncertainty indices Interpretable machine learning News narrative sentiment Prediction Economic cycle |
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