Robust Median Reversion Strategy for Online Portfolio Selection

Huang, Daren; Zhou, Jianjiang; Li, Bin; Hoi, Steven C. H.; Zhou, Shuigeng

doi:10.1109/tkde.2016.2563433

Cited by 83 publications

(42 citation statements)

References 29 publications

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“…As existing mean reversion algorithms do not consider noises and outliers in the data, they often suffer from estimation errors, which lead to nonoptimal portfolios and subsequent poor performance in practice. To handle the noises and outliers, Huang et al [2013] proposed to exploit mean reversion via robust L 1 -median estimator, and designed a novel portfolio selection strategy called Robust Median Reversion (RMR).…”

Section: Robust Median Reversionmentioning

confidence: 99%

Online Portfolio Selection

Li¹,

Hoi²

2018

Self Cite

101

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Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining, etc. This article aims to provide a comprehensive survey and a structural understanding of published online portfolio selection techniques. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and "Meta-Learning Algorithms". In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the Capital Growth theory in order to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state-of-the-art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research directions.

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Section: Robust Median Reversionmentioning

confidence: 99%

Online Portfolio Selection

Li¹,

Hoi²

2018

Self Cite

101

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“…Solving this optimization problem gives us the following solutions. (Li et al's proposition 4.1) [19].…”

Section: S T Pr‰} • W ≤ Yš ≥ €mentioning

confidence: 97%

“…In the 2000s, follow the Loser strategies, contrarian strategies, were developed as an alternative to the follow the Leader strategies. The passive aggressive mean reversion strategy, the Confidence Weighted Mean Reversion strategy, the anti-correlation strategy and Robust Median Reversion strategy are typical contrarian strategies that have proven to be much better than follow the Winner strategies [15][16][17][18][19].…”

Section: Related Workmentioning

confidence: 99%

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Effectiveness of F-SCORE on the Loser Following Online Portfolio Strategy in the Korean Value Stocks Portfolio

Jeong¹,

Kim²

2019

AJTAB

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This paper compares the effectiveness of six online portfolio strategies when they are applied to the Korean value stock portfolio. Firstly, using F-SCORE of Piotroski the value stock portfolio is divided into buying group and selling group. Then the six loser following online portfolio strategies are applied for each group and the whole portfolio. RMR strategy for the whole stock portfolio is far superior to the other strategies in terms of the total cumulative return, Sharpe ratio and Calmar ratio. This implies that value stock portfolio has mean reverting or trend following properties that can be utilized by various machine learning techniques.

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“…Such drawback is clearly indicated by its performance in the DJIA dataset [7,8,46], and motivates the proposed new algorithms in this article. Finally, we note that an early short version of this work has been published in the conference proceedings of ICML [42], and a recent robust variant in this series was also published in IJCAI [39].…”

Section: Follow-the-loser Approachesmentioning

confidence: 99%

Moving average reversion strategy for on-line portfolio selection

Hoi

Sahoo

et al. 2015

Artificial Intelligence

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109

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Please cite this article in press as: B. Li et al., Moving Average Reversion Strategy for On-Line Portfolio Selection, Artificial Intelligence (2015), http://dx. AbstractOn-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interests from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock price relatives 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 AverageReversion" (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 data sets and source codes of this work publicly available at our project website: http://OLPS.stevenhoi.org/.

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Robust Median Reversion Strategy for Online Portfolio Selection

Cited by 83 publications

References 29 publications

Online Portfolio Selection

Online Portfolio Selection

Effectiveness of F-SCORE on the Loser Following Online Portfolio Strategy in the Korean Value Stocks Portfolio

Moving average reversion strategy for on-line portfolio selection

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