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A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading

文献类型：期刊论文

作者Li, Yuze1,2; Wang, Shouyang2,3; Wei, Yunjie3; Zhu, Qing4
刊名IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
出版日期2021-12-01
卷号8期号:6页码:1357-1368
关键词Gold Forecasting Autoregressive processes Predictive models Signal resolution Deep learning Mathematical model Algorithmic trading bidirectional gated recurrent unit (BiGRU) gold futures price forecasting variational mode decomposition (VMD)
ISSN号2329-924X
DOI10.1109/TCSS.2021.3084847
英文摘要The gold market plays a vital role in the world economy. Due to its complex and nonstationary nature, predicting the price of gold is particularly challenging. In this study, a new hybrid forecasting approach named variational mode decomposition (VMD)-iterated cumulative sums of squares (ICSS)-bidirectional gated recurrent unit (BiGRU) is proposed by integrating BiGRU deep learning model, VMD, and iterated cumulative sum of squares algorithm. The forecasting framework is able to extract the inner factors and patterns within the gold futures market movements, decompose its correlation with external markets and detect shifts within market conditions in order to accurately predict price movements in the gold futures market. The experimental results show that the hybrid forecasting approach can improve the prediction performance significantly in comparison to the benchmarks. Furthermore, we extend the proposed hybrid forecasting approach to generate trading strategies and test trading performance of the gold futures market. The testing results over an out-of-sample period of 11 years (2008-2019) indicate that the strategy generated based on the prediction of the proposed approach displays high levels of consistency in generating positive returns and outperforms several other common trading strategies under various market conditions. The approach also shows consistent better results when generalized to the spot gold market, providing practical guidance for minimizing investment risk and hedging strategies in the gold commodity market.
资助项目National Natural Science Foundation of China[71801213] ; National Natural Science Foundation of China[71988101]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000724478300012
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/59649]  
专题中国科学院数学与系统科学研究院
通讯作者Wei, Yunjie
作者单位1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Ctr Forecasting Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
4.Shaanxi Normal Univ, Int Business Sch, Xian 710000, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yuze,Wang, Shouyang,Wei, Yunjie,et al. A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2021,8(6):1357-1368.
APA Li, Yuze,Wang, Shouyang,Wei, Yunjie,&Zhu, Qing.(2021).A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,8(6),1357-1368.
MLA Li, Yuze,et al."A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 8.6(2021):1357-1368.

入库方式： OAI收割

来源：数学与系统科学研究院

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