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- W3019484080 abstract "Purpose The purpose of this study is to evaluate the performance of the ensemble learning models, such as the Random Forest and Extreme Gradient Boosting models, in predicting the direction of the Japan real estate investment trusts (J-REITs) at different return horizons, based on input obtained from various technical indicators. Design/methodology/approach This study measures the predictability of J-REITs with technical indicators by using different horizons of REITs' return and machine learning models. The ensemble learning models includes Random Forest and Extreme Gradient Boosting models while the return horizons of REITs ranging from 1 to 300 days. The results were further split into individual years to check for the consistency of the performance across time. Findings The Extreme Gradient Boosting appears to be the best method in improving forecast accuracy but not the trading return. A wider return horizons platform seemed to deliver a relatively better performance in both forecast accuracy and trading return, when compared to the return horizon of one. Practical implications It is recommended that the Extreme Gradient Boosting and Random Forest model be considered by practitioners for back-testing trading model. In addition, selecting different return horizons so as to achieve a better performance in trading/investment should also be considered. Originality/value The predictability of J-REITs using technical indicators was compared among different returns horizons and the models (Extreme Gradient Boosting and Random Forest)." @default.
- W3019484080 created "2020-05-01" @default.
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- W3019484080 date "2020-04-24" @default.
- W3019484080 modified "2023-09-24" @default.
- W3019484080 title "Performing technical analysis to predict Japan REITs' movement through ensemble learning" @default.
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- W3019484080 doi "https://doi.org/10.1108/jpif-01-2020-0007" @default.
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