Can We Learn to Beat the Best Stock

Borodin, Allan; El‐Yaniv, Ran; Gogan, Vincent

doi:10.1613/jair.1336

Cited by 172 publications

(152 citation statements)

References 21 publications

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“…It exploits the statistical information from the historical stock price relatives and adopts the classical mean reversion trading idea to transfer the wealth in the portfolio. Although it does not provide any theoretical guarantee, its empirical results (Borodin et al 2004) showed that Anticor can outperform all existing strategies in most cases. Unlike the greedy algorithm by the Anticor strategy, Li et al (2011b) very recently proposed Confidence Weighted Mean Reversion (CWMR) strategy to actively exploit the mean reversion property and the second order information of a portfolio, which produces better performance than Anticor.…”

Section: Learning To Select Portfoliomentioning

confidence: 99%

“…This idea was followed by Borodin et al (2004) in their proposal of a non-universal portfolio strategy named Anti-Correlation (Anticor). Unlike the regret minimization approaches, Anticor strategy takes advantage of the statistical properties of financial market.…”

Section: Learning To Select Portfoliomentioning

confidence: 99%

“…Besides the trend following approach, another widely adopted approach in the learning community is mean reversion (Cover and Gluss 1986;Cover 1991;Borodin et al 2004), which is also termed as contrarian approach. This approach stems from the CRP strategy (Cover and Gluss 1986), which rebalances to the initial portfolio every trading day.…”

Section: Analysis Of Existing Workmentioning

confidence: 99%

“…Among existing algorithms, CRP, UP, and Anticor adopt this trading idea. However, CRP and UP passively revert to the mean, while empirical evidence from Anticor algorithm (Borodin et al 2004) shows that active reversion to the mean may better exploit the fluctuation of financial markets and is likely to obtain a much higher profit. On the other hand, although Anticor actively reverts to the mean, it is a heuristic method based on statistical correlations to transfer the wealth within the portfolio.…”

Section: Analysis Of Existing Workmentioning

confidence: 99%

“…Rather than trading with a single stock using computational intelligence techniques, learning to select portfolio approach focuses on a portfolio, which consists of multiple assets/stocks. Several approaches for online portfolio selection, often characterized by machine learning formulations and effective optimization solutions, have been proposed in literature (Kelly 1956;Breiman 1961;Cover 1991;Ordentlich and Cover 1996;Helmbold et al 1996;Borodin and El-Yaniv 1998;Borodin et al 2000Borodin et al , 2004Stoltz and Lugosi 2005;Hazan 2006; Györfi et al 2006;Blum and Mansour 2007;Levina and Shafer 2008;. Despite being studied extensively, most approaches are limited in some aspects or the other.…”

Section: Introductionmentioning

confidence: 99%

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PAMR: Passive aggressive mean reversion strategy for portfolio selection

et al. 2012

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This article proposes a novel online portfolio selection strategy named "Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available at http://www.cais.ntu.edu.sg/~chhoi/PAMR/.

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Section: Learning To Select Portfoliomentioning

confidence: 99%

Section: Learning To Select Portfoliomentioning

confidence: 99%

Section: Analysis Of Existing Workmentioning

confidence: 99%

Section: Analysis Of Existing Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PAMR: Passive aggressive mean reversion strategy for portfolio selection

et al. 2012

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Risk-Sensitive Online Learning

Even-Dar

Kearns

Wortman

2006

Lecture Notes in Computer Science

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Abstract. We consider the problem of online learning in settings in which we want to compete not simply with the rewards of the best expert or stock, but with the best trade-off between rewards and risk. Motivated by finance applications, we consider two common measures balancing returns and risk: the Sharpe ratio [7] and the mean-variance criterion of Markowitz [6]. We first provide negative results establishing the impossibility of no-regret algorithms under these measures, thus providing a stark contrast with the returns-only setting. We then show that the recent algorithm of Cesa-Bianchi et al. [3] achieves nontrivial performance under a modified bicriteria risk-return measure, and also give a no-regret algorithm for a "localized" version of the mean-variance criterion. To our knowledge this paper initiates the investigation of explicit risk considerations in the standard models of worst-case online learning.

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