Algorithms for portfolio management based on the Newton method

Agarwal, Amit; Hazan, Elad; Kale, Satyen; Schapire, Robert E.

doi:10.1145/1143844.1143846

Cited by 204 publications

(170 citation statements)

References 21 publications

(34 reference statements)

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“…Recently, convex optimization has been applied to resolve the portfolio selection problem (Agarwal et al 2006;Agarwal and Hazan 2005;Hazan 2006;Hazan et al 2007). Examples include Online Newton Step (ONS) strategy (Agarwal et al 2006), which aims to maximize the expected logarithmic cumulative wealth (approximated using historical price relatives) and to minimize the variation of the expected portfolio.…”

Section: Learning To Select Portfoliomentioning

confidence: 99%

“…Examples include Online Newton Step (ONS) strategy (Agarwal et al 2006), which aims to maximize the expected logarithmic cumulative wealth (approximated using historical price relatives) and to minimize the variation of the expected portfolio. ONS exploits the second order information of the log wealth function and applies it to the online scenario.…”

Section: Learning To Select Portfoliomentioning

confidence: 99%

“…The first one is NYSE dataset, one "standard" dataset pioneered by Cover (1991) and followed by several other researchers (Singer 1997;Helmbold et al 1996;Borodin et al 2004;Agarwal et al 2006;Györfi et al 2006. This dataset contains 5651 daily price relatives of 36 stocks 5 in New York Stock Exchange (NYSE) for a 22-year period from Jul.…”

Section: Experimental Testbed On Real Datamentioning

confidence: 99%

“…Following previous studies (Cover 1991;Helmbold et al 1996;Agarwal et al 2006), we relax this constraint in the model and evaluate it empirically in Sect. 5.4.5.…”

Section: Practical Issuesmentioning

confidence: 99%

“…EG: Exponential Gradient (EG) algorithm with the best parameter η = 0.05 as suggested by Helmbold et al (1996); 6. ONS: Online Newton Step (ONS) with the parameters suggested by Agarwal et al (2006), that is, η = 0, β = 1, γ = 1 8 ; 7. SP: Switching Portfolios with parameter γ = 1 4 as suggested by Singer (1997) …”

Section: Comparison Approachesmentioning

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: Experimental Testbed On Real Datamentioning

confidence: 99%

“…Following previous studies (Cover 1991;Helmbold et al 1996;Agarwal et al 2006), we relax this constraint in the model and evaluate it empirically in Sect. 5.4.5.…”

Section: Practical Issuesmentioning

confidence: 99%

Section: Comparison Approachesmentioning

confidence: 99%

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PAMR: Passive aggressive mean reversion strategy for portfolio selection

et al. 2012

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Universal Portfolios

Ordentlich¹

2010

Encyclopedia of Quantitative Finance

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The theory of universal portfolios forgoes the stochastic models and associated optimality criteria of classical portfolio optimization, and instead seeks universal portfolio selection strategies that perform well relative to all strategies in a target class of allocation strategies, for all possible sequences of asset price changes. This article reviews the theory as specialized to the target class of constant rebalanced portfolios. In this case, universal portfolios have been found that asymptotically achieve the rate of return of the best constant rebalanced portfolio in hindsight, irrespective of asset price behavior.

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Robust regret optimal control

Liu,

Seiler

2024

Intl J Robust & Nonlinear

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SummaryThis paper presents a synthesis method for robust, regret optimal control. The plant is modeled in discrete‐time by an uncertain linear time‐invariant (LTI) system. An optimal non‐causal controller is constructed using the nominal plant model and given full knowledge of the disturbance. Robust regret is defined relative to the performance of this optimal non‐causal control. It is shown that a controller achieves robust regret if and only if it satisfies a robust performance condition. DK‐iteration can be used to synthesize a controller that satisfies this condition and hence achieve a given level of robust regret. The approach is demonstrated two examples: (i) a single‐input, single‐output (SISO) classical design, and (ii) an active suspension for a quarter car model. The SISO example is simple but is intended to provide insight about the robust regret control design. The quarter car example compares the robust regret controllers against regret controllers designed without plant uncertainty.

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Algorithms for portfolio management based on the Newton method

Cited by 204 publications

References 21 publications

PAMR: Passive aggressive mean reversion strategy for portfolio selection

PAMR: Passive aggressive mean reversion strategy for portfolio selection

Universal Portfolios

Robust regret optimal control

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