通过集成学习进行技术分析,预测日本REITs的走势

IF 1.6 Q3 BUSINESS, FINANCE Journal of Property Investment & Finance Pub Date : 2020-04-24 DOI:10.1108/jpif-01-2020-0007
Wei Kang Loo
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引用次数: 2

摘要

本研究的目的是评估集成学习模型(如随机森林和极端梯度增强模型)在预测日本房地产投资信托基金(J-REITs)在不同回报水平下的表现,基于从各种技术指标获得的输入。设计/方法/方法本研究采用不同的REITs收益视界和机器学习模型,用技术指标衡量J-REITs的可预测性。集成学习模型包括随机森林模型和极端梯度增强模型,REITs的回报期限为1 ~ 300天。研究结果被进一步划分为不同的年份,以检验不同时期的表现是否一致。结果表明:在提高预测精度方面,极值梯度提升法是最好的方法,但在提高交易收益方面效果不佳。与一个回报范围相比,一个更宽的回报范围平台似乎在预测准确性和交易回报方面都提供了相对更好的表现。实践意义建议从业者在对交易模型进行回测时考虑极端梯度增强和随机森林模型。此外,为了在交易/投资中获得更好的表现,还应该考虑选择不同的回报视野。运用技术指标对J-REITs的可预测性进行了不同收益水平和模型(极端梯度增强和随机森林)的比较。
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Performing technical analysis to predict Japan REITs' movement through ensemble learning
PurposeThe 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/approachThis 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.FindingsThe 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 implicationsIt 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/valueThe predictability of J-REITs using technical indicators was compared among different returns horizons and the models (Extreme Gradient Boosting and Random Forest).
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来源期刊
CiteScore
3.50
自引率
23.10%
发文量
33
期刊介绍: Fully refereed papers on practice and methodology in the UK, continental Western Europe, emerging markets of Eastern Europe, China, Australasia, Africa and the USA, in the following areas: ■Academic papers on the latest research, thinking and developments ■Law reports assessing new legislation ■Market data for a comprehensive review of current research ■Practice papers - a forum for the exchange of ideas and experiences
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