Moving average reversion strategy for on-line portfolio selection
Li, Bin1; Hoi, Steven C. H.2; Sahoo, Doyen2; Liu, Zhi-Yong3
AbstractOn-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line portfolio selection strategy named "On-Line Moving Average Reversion" (OLMAR), which exploits MAR via efficient and scalable online machine learning techniques. From our empirical results on real markets, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Finally, we have made all the datasets and source codes of this work publicly available at our, project website: (C) 2015 Elsevier B.V. All rights reserved.
KeywordPortfolio Selection On-line Learning Mean Reversion Moving Average Reversion
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000351791900004
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Cited Times:16[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Wuhan Univ, Econ & Management Sch, Wuhan 430072, Peoples R China
2.Singapore Management Univ, Sch Informat Syst, Singapore 178902, Singapore
3.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Li, Bin,Hoi, Steven C. H.,Sahoo, Doyen,et al. Moving average reversion strategy for on-line portfolio selection[J]. ARTIFICIAL INTELLIGENCE,2015,222:104-123.
APA Li, Bin,Hoi, Steven C. H.,Sahoo, Doyen,&Liu, Zhi-Yong.(2015).Moving average reversion strategy for on-line portfolio selection.ARTIFICIAL INTELLIGENCE,222,104-123.
MLA Li, Bin,et al."Moving average reversion strategy for on-line portfolio selection".ARTIFICIAL INTELLIGENCE 222(2015):104-123.
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