A refined methodological approach: Long-term stock market forecasting with XGBoost
One critical research gap that this study fills is artificial intelligence (AI) and machine learning applications that predict equity market index total returns using long-term prediction horizons, and by experimenting with Extreme Gradient Boosting (XGBoost). The presented models achieved significa...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-07-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2025-0027 |
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| _version_ | 1849242954429890560 |
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| author | Sürek Rojen Erik Lau Wee-Yeap |
| author_facet | Sürek Rojen Erik Lau Wee-Yeap |
| author_sort | Sürek Rojen Erik |
| collection | DOAJ |
| description | One critical research gap that this study fills is artificial intelligence (AI) and machine learning applications that predict equity market index total returns using long-term prediction horizons, and by experimenting with Extreme Gradient Boosting (XGBoost). The presented models achieved significantly higher accuracy rates than the majority class rate, and they obtained better predictive scores in all metrics than the logistic regression model. The best-performing model had a 100% accuracy rate when negative returns were predicted with a p-value of 0.05121. The evidence from this study suggests that XGBoost, a neglected algorithm in the literature, can enable more empirically informed long-term portfolio management decisions regarding overall equity exposure. Moreover, a literature contribution of this study is a refined methodological approach for prospective studies when implementing binary classifiers of prospective stock market returns for enhanced real-life economic utility for investors. The constructed models generated probabilities of whether the S&P 500 will have positive or negative total returns, including dividend payouts, in the subsequent 12 months. The predictive metrics of these models were evaluated against traditional logit models and whether the accuracy rates statistically significantly exceeded the majority class rate. |
| format | Article |
| id | doaj-art-c29fb468aaf8480ca183fc27a9607ee3 |
| institution | Kabale University |
| issn | 2191-026X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-c29fb468aaf8480ca183fc27a9607ee32025-08-20T03:59:39ZengDe GruyterJournal of Intelligent Systems2191-026X2025-07-013418234610.1515/jisys-2025-0027A refined methodological approach: Long-term stock market forecasting with XGBoostSürek Rojen Erik0Lau Wee-Yeap1Faculty of Business and Economics, Universiti Malaya, 50603 Kuala Lumpur, MalaysiaFaculty of Business and Economics, Universiti Malaya, 50603 Kuala Lumpur, MalaysiaOne critical research gap that this study fills is artificial intelligence (AI) and machine learning applications that predict equity market index total returns using long-term prediction horizons, and by experimenting with Extreme Gradient Boosting (XGBoost). The presented models achieved significantly higher accuracy rates than the majority class rate, and they obtained better predictive scores in all metrics than the logistic regression model. The best-performing model had a 100% accuracy rate when negative returns were predicted with a p-value of 0.05121. The evidence from this study suggests that XGBoost, a neglected algorithm in the literature, can enable more empirically informed long-term portfolio management decisions regarding overall equity exposure. Moreover, a literature contribution of this study is a refined methodological approach for prospective studies when implementing binary classifiers of prospective stock market returns for enhanced real-life economic utility for investors. The constructed models generated probabilities of whether the S&P 500 will have positive or negative total returns, including dividend payouts, in the subsequent 12 months. The predictive metrics of these models were evaluated against traditional logit models and whether the accuracy rates statistically significantly exceeded the majority class rate.https://doi.org/10.1515/jisys-2025-0027computational financequantitative financeai and machine learningstock market returnpredictive modelg17c53c49 |
| spellingShingle | Sürek Rojen Erik Lau Wee-Yeap A refined methodological approach: Long-term stock market forecasting with XGBoost Journal of Intelligent Systems computational finance quantitative finance ai and machine learning stock market return predictive model g17 c53 c49 |
| title | A refined methodological approach: Long-term stock market forecasting with XGBoost |
| title_full | A refined methodological approach: Long-term stock market forecasting with XGBoost |
| title_fullStr | A refined methodological approach: Long-term stock market forecasting with XGBoost |
| title_full_unstemmed | A refined methodological approach: Long-term stock market forecasting with XGBoost |
| title_short | A refined methodological approach: Long-term stock market forecasting with XGBoost |
| title_sort | refined methodological approach long term stock market forecasting with xgboost |
| topic | computational finance quantitative finance ai and machine learning stock market return predictive model g17 c53 c49 |
| url | https://doi.org/10.1515/jisys-2025-0027 |
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