PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE
Accurately predicting stock market trends remains a significant challenge for investors due to its dynamic nature. This study explores the performance of Gradient Boosting models, including XGBoost, XGBoost Random Forest, CatBoost, and Gradient Boosting Scikit-Learn, in predicting stock market trend...
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| Format: | Article |
| Language: | English |
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Universitas Pattimura
2025-04-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/16208 |
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| _version_ | 1849237894465585152 |
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| author | Delvian Christoper Kho Hindriyanto Dwi Purnomo Hendry Hendry |
| author_facet | Delvian Christoper Kho Hindriyanto Dwi Purnomo Hendry Hendry |
| author_sort | Delvian Christoper Kho |
| collection | DOAJ |
| description | Accurately predicting stock market trends remains a significant challenge for investors due to its dynamic nature. This study explores the performance of Gradient Boosting models, including XGBoost, XGBoost Random Forest, CatBoost, and Gradient Boosting Scikit-Learn, in predicting stock market trends such as sideways movement, uptrends, downtrends, and volatility. Using four datasets from the Indonesia Stock Exchange, the research integrates technical, fundamental, and sentiment data, encompassing 37 features. Modeling and testing are conducted using Orange tools and Python, with performance evaluated through metrics such as Mean Absolute Percentage Error (MAPE), R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results indicate that XGBoost and XGBoost Random Forest consistently outperform other models in predicting stock price movements. These findings highlight the potential of Gradient Boosting models in providing accurate and reliable predictions, offering valuable insights for investors, financial analysts, and researchers to enhance investment strategies and adapt to market fluctuations effectively. |
| format | Article |
| id | doaj-art-fbff47aeb10a4ee6927e32efeec21ceb |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-fbff47aeb10a4ee6927e32efeec21ceb2025-08-20T04:01:48ZengUniversitas PattimuraBarekeng1978-72272615-30172025-04-011921393140810.30598/barekengvol19iss2pp1393-140816208PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGEDelvian Christoper Kho0Hindriyanto Dwi Purnomo1Hendry Hendry2Information System Study Program, Fakulty of Information Technology, Universitas Kristen Satya Wacana , IndonesiaInformation System Study Program, Fakulty of Information Technology, Universitas Kristen Satya Wacana , IndonesiaInformation System Study Program, Fakulty of Information Technology, Universitas Kristen Satya Wacana , IndonesiaAccurately predicting stock market trends remains a significant challenge for investors due to its dynamic nature. This study explores the performance of Gradient Boosting models, including XGBoost, XGBoost Random Forest, CatBoost, and Gradient Boosting Scikit-Learn, in predicting stock market trends such as sideways movement, uptrends, downtrends, and volatility. Using four datasets from the Indonesia Stock Exchange, the research integrates technical, fundamental, and sentiment data, encompassing 37 features. Modeling and testing are conducted using Orange tools and Python, with performance evaluated through metrics such as Mean Absolute Percentage Error (MAPE), R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results indicate that XGBoost and XGBoost Random Forest consistently outperform other models in predicting stock price movements. These findings highlight the potential of Gradient Boosting models in providing accurate and reliable predictions, offering valuable insights for investors, financial analysts, and researchers to enhance investment strategies and adapt to market fluctuations effectively.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/16208catboostgradient boostingperformance analysisrandom foreststock predictionxgboost |
| spellingShingle | Delvian Christoper Kho Hindriyanto Dwi Purnomo Hendry Hendry PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE Barekeng catboost gradient boosting performance analysis random forest stock prediction xgboost |
| title | PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE |
| title_full | PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE |
| title_fullStr | PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE |
| title_full_unstemmed | PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE |
| title_short | PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE |
| title_sort | performance analysis of gradient boosting models variants in predicting the direction of stock closing prices on the indonesia stock exchange |
| topic | catboost gradient boosting performance analysis random forest stock prediction xgboost |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/16208 |
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