Robust portfolio optimization with a hybrid heuristic algorithm

Fastrich, Björn; Winker, Peter

doi:10.1007/s10287-010-0127-2

Cited by 19 publications

(11 citation statements)

References 28 publications

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“…This is in line with what [12] found, that heuristic models lead to overall superior results over the MVO approach; however, [12] further concludes that portfolio compositions from heuristic approaches are more stable. [3] also concludes that employing techniques that lead to running optimization over created scenarios and different risk measures other than variance, offers an improvement to the traditional mean-variance optimization models.…”

Section: Conclusion and Recommendationssupporting

confidence: 86%

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Portfolio Optimization under Threshold Accepting: Further Evidence from a Frontier Market

Masese¹,

Othieno²,

Njenga³

2017

JMF

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Several improvements and alternatives to Mean Variance Optimization (MVO) have been suggested and used since its inception in 1952. The improvements have mostly included addition of constraints to the traditional MVO model, using alternative risk measures and using non risk-reward models. This paper seeks to compare MVO against the Threshold Accepting model, which is a general optimization model, in portfolio selection. Using data on 29 stocks in the Kenyan stock market we compare the relative performance of the two models using performance measures such as the Sharpe Ratio, Sortino Ratio and Information Ratio. We find that the Threshold Accepting (TA) model outperforms the Mean-Variance Optimization model though MVO yields similar results when we use monthly or weekly data but the latter is observed as a more consistent model. The TA model has portfolios with generally more superior risk-adjusted returns for the full period and during periods of high volatility in the stock market performance market. This observation implies that relatively more attention should be given to the TA model rather than relying entirely on the classical MVO approach.

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Section: Conclusion and Recommendationssupporting

confidence: 86%

“…It looks for solutions by optimizing the set objective function through reiterations that improve the solution at each subsequent iteration hence meeting any set constraints [8]. TA has been recommended as an optimization model that leads to better optimization results over classical optimization approaches [12].…”

Section: Introductionmentioning

confidence: 99%

Portfolio Optimization under Threshold Accepting: Further Evidence from a Frontier Market

Masese¹,

Othieno²,

Njenga³

2017

JMF

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“…A robust portfolio, the one that optimizes the worst-case performance concerning with all possible values the mean vector and covariance matrix. The worst-case for robust optimization probably happened in the uncertainty sets (see, for example, Goldfarb & Iyengar, 2003;Tütüncü & Koenig, 2004;Engels, 2004;Garlappi, Uppal, & Wang, 2007;Lu, 2011).…”

Section: Introductionmentioning

confidence: 99%

An empirical comparison between robust estimation and robust optimization to mean-variance portfolio

Supandi

Rosadi

Abdurakhman

2017

J. Mod. App. Stat. Meth.

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Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Recently, there are two approaches designed to reduce the effect of estimation error: robust statistics and robust optimization. Two different robust portfolios were examined by assessing the outof-sample performance and the stability of optimal portfolio compositions. The performance of the proposed robust portfolios was compared to classical portfolios via expected return, risk, and Sharpe Ratio. The aim is to shed light on the debate concerning the importance of the estimation error and weights stability in the portfolio allocation problem, and the potential benefits coming from robust strategies in comparison to classical portfolios.

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“…It is critical that Modern portfolio theory is a theory that is different from any theories of asset pricing. Therefore, it should be understood that the validity of modern portfolio theory does not depend on the asset pricing theory, which is not clear to many critics of MPT [7] [8]. Markowitz developed the mean-variance theory as a prescriptive theory, not a descriptive one.…”

Section: Introductionmentioning

confidence: 99%

Review on the Modern portfolio theory and optimization model

Hou¹,

Li²,

Zhou³

2017

Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)

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Abstract:The field of portfolio optimization and risk management are the bases to develop and improve the financial markets and also the financial decisions. It has narrow and broad part of theory. A narrow theory refers to the Modern portfolio theory (MPT); and another version of the theory generalizes the classical theory to accommodate various alternative theories, including the capital asset pricing model and effective securities market theory of capital market theory. At the same time, because of the traditional efficient market hypothesis cannot explain market anomalies, the portfolio theory is facing the challenge of irrational market movements. Then behavioral finance was introduced to tackle the problem. The Topic of modern portfolio theory and the optimization model of development are reviewed in this paper. The analytical models outline the development trend of related fields, pointing out the direction for future research. The ideas introduced in this article forms the foundations of what is now popularly referred to as Modern Portfolio Theory (MPT).

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Robust portfolio optimization with a hybrid heuristic algorithm

Cited by 19 publications

References 28 publications

Portfolio Optimization under Threshold Accepting: Further Evidence from a Frontier Market

Portfolio Optimization under Threshold Accepting: Further Evidence from a Frontier Market

An empirical comparison between robust estimation and robust optimization to mean-variance portfolio

Review on the Modern portfolio theory and optimization model

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