A compact mean-variance-skewness model for large-scale portfolio optimization and its application to the NYSE market

Ryoo, Hong Seo

doi:10.1057/palgrave.jors.2602168

Cited by 20 publications

(7 citation statements)

References 12 publications

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“…Different risk measures providing a finer characterization of investment risk, beyond the MV perspective that relies solely on the variance of returns. Over the years, different risk measures have been introduced focusing on a more detailed description of the returns distribution with higherorder moments (skewness and kurtosis; Jondeau & Rockinger, 2006;Ryoo, 2007), tail-risk measures (value-at-risk, conditional value-at-risk; Jorion, 2009;Rockafellar & Uryasev, 2002), and other risk-return performance measures (e.g., omega ratio; Kapsos, Christofides, & Rustem, 2014). Cardinality constrained asset allocation, involving portfolios consisting of a fixed maximum number of assets selected automatically through an optimization model from a given pool of options (Bertsimas & Shioda, 2009;Chang, Meade, Beasley, & Sharaiha, 2000;Woodside-Oriakhi, Lucas, & Beasley, 2011).…”

Section: Capital Allocationmentioning

confidence: 99%

Computational approaches and data analytics in financial services: A literature review

Andriosopoulos

Doumpos

Pardalos

et al. 2019

Journal of the Operational Research Society

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The level of modeling sophistication in financial services has increased considerably over the years. Nowadays, the complexity of financial problems and the vast amount of data require an engineering approach based on analytical modeling tools for planning, decision making, reporting, and supervisory control. This article provides an overview of the main financial applications of computational and data analytics approaches, focusing on the coverage of the recent developments and trends. The overview covers different methodological tools and their uses in areas, such as portfolio management, credit analysis, banking, and insurance.

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Section: Capital Allocationmentioning

confidence: 99%

Computational approaches and data analytics in financial services: A literature review

Andriosopoulos

Doumpos

Pardalos

et al. 2019

Journal of the Operational Research Society

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“…After obtaining a starting point, the method involved using a steepest descent algorithm and a variable metric algorithm to obtain an optimal solution. The incorporation of a third moment of R, the skewness, as seen in [124] reduces the number of variables considerably and the resulting model can be used to solve large scale problems. An active set method is proposed [135] to solve large scale versions of (3.3).…”

Section: Mean-variance Modelmentioning

confidence: 99%

Mean-Variance Portfolio Optimization : Eigendecomposition-Based Methods

Mayambala¹

2015

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Modern portfolio theory is about determining how to distribute capital among available securities such that, for a given level of risk, the expected return is maximized, or for a given level of return, the associated risk is minimized. In the pioneering work of Markowitz in 1952, variance was used as a measure of risk, which gave rise to the wellknown mean-variance portfolio optimization model. Although other mean-risk models have been proposed in the literature, the mean-variance model continues to be the backbone of modern portfolio theory and it is still commonly applied. The scope of this thesis is a solution technique for the mean-variance model in which eigendecomposition of the covariance matrix is performed.The first part of the thesis is a review of the mean-risk models that have been suggested in the literature. For each of them, the properties of the model are discussed and the solution methods are presented, as well as some insight into possible areas of future research.The second part of the thesis is two research papers. In the first of these, a solution technique for solving the mean-variance problem is proposed. This technique involves making an eigendecomposition of the covariance matrix and solving an approximate problem that includes only relatively few eigenvalues and corresponding eigenvectors. The method gives strong bounds on the exact solution in a reasonable amount of computing time, and can thus be used to solve large-scale mean-variance problems.The second paper studies the mean-variance model with cardinality constraints, that is, with a restricted number of securities included in the portfolio, and the solution technique from the first paper is extended to solve such problems. Near-optimal solutions to large-scale cardinality constrained mean-variance portfolio optimization problems are obtained within a reasonable amount of computing time, compared to the time required by a commercial general-purpose solver.v Populärvetenskaplig sammanfattningFör den som har kapital att investera kan det vara svårt att avgöra vilka investeringar som är mest fördelaktiga. Till stöd för beslutet kan matematiska modeller användas och denna avhandling handlar om hur man kan beräkna lösningar till sådana modeller. De investeringsalternativ som betraktas är finansiella instrument som är föremål för daglig handel, som aktier och obligationer.En investerare placerar kapital i finansiella instrument eftersom de förväntas ge en god avkastning över tiden. Samtidigt är sådana placeringar alltid förknippade med risktagande. Förväntad avkastning och risk varierar kraftigt mellan olika instrument. Till exempel ger placeringar i statsobligationer typiskt mycket låg avkastning till mycket låg risk, medan placeringar i aktier i nystartade bolag som utvecklar nya läkemedel kan ge mycket hög avkastning samtidigt som risken är mycket hög.Att investera kan ses som en avvägning mellan den förväntade avkastningen och den risk som investeringen innebär, och typiskt är hög förväntad avkastning också associerade med e...

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“…For example, Chen et al (2009), suggest that as the model utilises standard deviation to measure risks, both positive and negative risks are in fact employed as variables, while in reality, investors only tend to focus on negative risks. Other critics of the model include Ryoo (2007), who argues that the MV model is limited primarily by its inability to:…”

Section: Literature Review 21 Model Overviewmentioning

confidence: 99%

Using portfolio optimisation models to enhance decision making and prediction

Ojiako

2014

Journal of Modelling in Management

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Purpose – The purpose of this paper is to analyse and compare the performances of portfolio optimisation models including Markowitz's mean-variance model (MV model), Konno and Yamazaki's mean-absolute deviation portfolio optimisation model (MAD model), Young's minimax portfolio model and the VaR model. Design/methodology/approach – Historical data on 43 constituent shares listed on the Hong Kong Hang Seng Index (HSI) covering a four-year period are obtained. The paper then tests the performance of each model under different scenarios and against different sets of historical data. Findings – The paper finds that different levels of required annual returns impact on portfolio composition, historical data have a major impact on the determination of portfolio composition and the level of required annual return impacts on how optimisation models perform. Practical implications – The paper posits that with a comprehensive understanding of the performance of each of these performance optimisation models, investors may be able to develop a better understanding of how to adjust investment risk strategies, thus preventing serious losses. Originality/value – There are two major points of value to this paper. In the first place, the paper presents an original review of portfolio optimisation models. Second, using “real” data, the paper utilises five different scenarios to test the performance of each model under different situations.

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A compact mean-variance-skewness model for large-scale portfolio optimization and its application to the NYSE market

Cited by 20 publications

References 12 publications

Computational approaches and data analytics in financial services: A literature review

Computational approaches and data analytics in financial services: A literature review

Mean-Variance Portfolio Optimization : Eigendecomposition-Based Methods

Using portfolio optimisation models to enhance decision making and prediction

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