PAMR: Passive aggressive mean reversion strategy for portfolio selection

Li, Bin; Zhao, Peilin; Hoi, Steven C. H.; Gopalkrishnan, Vivekanand

doi:10.1007/s10994-012-5281-z

Cited by 172 publications

(91 citation statements)

References 67 publications

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“…Although some recently proposed mean reversion algorithms [46,45] have achieved promising results on many real datasets, they might perform extremely poorly on certain datasets, such as the DJIA dataset [8]. Comparing with Borodin et al [8], which exploits multi-period correlation, we found that the assumption of single-period prediction may attribute to the performance degradation.…”

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confidence: 61%

“…Li et al [46] proposed Passive Aggressive Mean Reversion (PAMR) strategy, which exploits the mean reversion property with the Passive Aggressive (PA) online learning [53,17]. The main idea of PAMR is to design a loss function of (b; x t ) in order to reflect the mean reversion property, that is, if the expected return based on last price relative is larger than a pre-defined threshold, the loss will linearly increase; otherwise, the loss is zero.…”

Section: Follow-the-loser Approachesmentioning

confidence: 99%

“…show that OLMAR not only can achieve better performance (in terms of cumulative wealth) than existing algorithms, and but also can avoid the significant performance degradation on certain datasets, such as the DJIA dataset [8,46].…”

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confidence: 97%

“…Comparing with Borodin et al [8], which exploits multi-period correlation, we found that the assumption of single-period prediction may attribute to the performance degradation. On the other hand, as illustrated in existing studies [46,45], Borodin et al [8] cannot fully exploit the potential of (multi-period) mean reversion for its heuristical nature.…”

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confidence: 98%

“…One important property exploited by many existing studies [8,46,45] is the mean reversion property, which assumes poor performing stocks will perform well in the subsequent periods and vice versa. Although some recently proposed mean reversion algorithms [46,45] have achieved promising results on many real datasets, they might perform extremely poorly on certain datasets, such as the DJIA dataset [8].…”

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confidence: 99%

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Moving average reversion strategy for on-line portfolio selection

Hoi

Sahoo

et al. 2015

Artificial Intelligence

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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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confidence: 61%

Section: Follow-the-loser Approachesmentioning

confidence: 99%

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confidence: 97%

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confidence: 98%

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confidence: 99%

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Moving average reversion strategy for on-line portfolio selection

Hoi

Sahoo

et al. 2015

Artificial Intelligence

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123

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Online Linearized Confidence‐Weighted Learning on a Budget

Wu,

Lin,

et al. 2024

Advanced Intelligent Systems

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Online learning aims to solve a sequence of consecutive prediction tasks by leveraging the knowledge gained from previous tasks. Linearized confidence‐weighted (LCW) learning is the first online learning algorithm introducing the concept of weight confidence into the prediction model through distributions over weights. It provides the flexibility for weights to update their values at different scales. The kernel trick in machine learning can be applied to LCW for a better prediction performance. However, the kernel‐based LCW algorithm is subject to the curse of kernelization which makes it vulnerable to the unlimited growth of the prediction model in runtime and memory consumption. In this study, we present the budgeted LCW (BLCW) algorithm which puts a limit on the growth by a predefined budget with optimization. Consequently, BLCW performs the LCW update and then reduces the information loss by projection. Based on the resource perspective that reinterprets LCW in terms of resources and utilization degrees, we demonstrated that BLCW approximates the kernel‐based LCW algorithm. We evaluate four budget maintenance strategies and suggest that the mean removal is the most stable. By various numerical experiments on real datasets, we demonstrate that BLCW performs competitively and effectively when compared to leading budgeted online algorithms.

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Active learning with support vector machines

Kremer

Pedersen

Igel

2014

WIREs Data Min & Knowl

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In machine learning, active learning refers to algorithms that autonomously select the data points from which they will learn. There are many data mining applications in which large amounts of unlabeled data are readily available, but labels (e.g., human annotations or results coming from complex experiments) are costly to obtain. In such scenarios, an active learning algorithm aims at identifying data points that, if labeled and used for training, would most improve the learned model. Labels are then obtained only for the most promising data points. This speeds up learning and reduces labeling costs. Support vector machine (SVM) classifiers are particularly well-suited for active learning due to their convenient mathematical properties. They perform linear classification, typically in a kernel-induced feature space, which makes expressing the distance of a data point from the decision boundary straightforward. Furthermore, heuristics can efficiently help estimate how strongly learning from a data point influences the current model. This information can be used to actively select training samples. After a brief introduction to the active learning problem, we discuss different query strategies for selecting informative data points and review how these strategies give rise to different variants of active learning with SVMs.

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PAMR: Passive aggressive mean reversion strategy for portfolio selection

Cited by 172 publications

References 67 publications

Moving average reversion strategy for on-line portfolio selection

Moving average reversion strategy for on-line portfolio selection

Online Linearized Confidence‐Weighted Learning on a Budget

Active learning with support vector machines

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