Benchmark-based evaluation of portfolio performance: a characterization

Alekseev, Aleksandr; Sokolov, Mikhail V.

doi:10.1007/s10436-016-0286-4

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Quantitative and finance theory (Alekseev and Sokolov, 2016, p. 431) obtain confirmatory results that “justify the use of benchmark-based portfolio evaluation”. Typical portfolio performance measures include factor models, model-free metrics, and index-based analysis.…”

Section: Methodsmentioning

confidence: 89%

Emerging market analysis of passive and active investing under bear and bull market conditions

Gopane,

Moyo,

Setaka

2023

JCMS

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PurposeStirred by scant regard for market phases in portfolio performance assessments, the current paper investigates the active versus passive investment strategies under the bull and bear market conditions in emerging markets focusing on South Africa as a case study.Design/methodology/approachMethodologically, the measures of Jensen's alpha and Treynor index are applied to the monthly returns of 20 funds from January 2010 to June 2022.FindingsThe results are enlightening; though they contradict developed market evidence, they are consistent with emerging market trends. The findings show that actively managed funds outperform the market benchmark and passive investing style under bear and normal market conditions. Passive investment strategy outperforms both market benchmark and actively investing style under bull market conditions.Practical implicationsIn the face of improved market efficiency, increased liquidity and recent technological impact, the findings of this study have practical application. The study outcomes should inform and update global investors, especially asset managers interested in emerging markets; however, the limitations of the study should also be considered.Originality/valueWhile limited studies consider market conditions when comparing and contrasting the performance of passive versus active investing, such consideration is lacking in emerging markets. The current study corrects this literature imbalance.

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Section: Methodsmentioning

confidence: 89%

Emerging market analysis of passive and active investing under bear and bull market conditions

Gopane,

Moyo,

Setaka

2023

JCMS

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“…We propose a data-driven framework to compute optimal multi-period dynamic strategies for outperforming a general stochastic benchmark target, which is an important portfolio management problem with immediate practical applications. There is a large extant literature on techniques for constructing portfolios which outperform a stochastic benchmark, e.g., (Browne, 1999(Browne, , 2000Tepla, 2001;Basak et al, 2006;Davis and Lleo, 2008;Lim and Wong, 2010;Oderda, 2015;Alekseev and Sokolov, 2016;Samo and Vervuurt, 2016;Al-Aradi and Jaimungal, 2018).…”

Section: Introductionmentioning

confidence: 99%

Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target

Ni¹,

Li²,

Carroll³

2020

Preprint

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We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective function. The proposed framework is illustrated with the asset allocation problem in the accumulation phase of a defined contribution pension plan, with the goal of achieving a higher terminal wealth than a stochastic benchmark. We demonstrate that the data-driven approach is capable of learning an adaptive asset allocation strategy directly from historical market returns, without assuming any parametric model of the financial market dynamics. Following the optimal adaptive strategy, investors can make allocation decisions simply depending on the current state of the portfolio. The optimal adaptive strategy outperforms the benchmark constant proportion strategy, achieving a higher terminal wealth with a 90% probability, a 46% higher median terminal wealth, and a significantly more right-skewed terminal wealth distribution. We further demonstrate the robustness of the optimal adaptive strategy by testing the performance of the strategy on bootstrap resampled market data, which has different distributions compared to the training data.

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“…An excellent exposition on the construction and the uses of benchmarks can be found, for example, in Siegel (2003). An axiomatic and economic approach to index number theory which demonstrates that the existence of a benchmark naturally arises from a few basic axioms and is tightly linked to the economic theory, can be found inAlekseev and Sokolov (2016).…”

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confidence: 99%

Portfolio performance under benchmarking relative loss and portfolio insurance: From omega ratio to loss aversion

Nguyen²

2023

ASTIN Bull.

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We study an optimal investment problem under a joint limited expected relative loss and portfolio insurance constraint with a general random benchmark. By making use of a static Lagrangian method in a complete market setting, the optimal wealth and investment strategy can be fully determined along with the existence and uniqueness of the Lagrangian multipliers. Our numerical demonstration for various commonly used random benchmarks shows a trade-off between the portfolio outperformance and underperformance relative to the benchmark, which may not be captured by the widely used Omega ratio and its utility-transformed version, reflecting the impact of the benchmarking loss constraint. Furthermore, we develop a new portfolio performance measurement indicator that incorporates the agent’s utility loss aversion relative to the benchmark via solving an equivalent optimal asset allocation problem with a benchmark-reference-based preference. We show that the expected utility performance is well depicted by looking at this new portfolio performance ratio, suggesting a more suitable portfolio performance measurement under a limited loss constraint relative to a possibly random benchmark.

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Benchmark-based evaluation of portfolio performance: a characterization

Cited by 6 publications

References 36 publications

Emerging market analysis of passive and active investing under bear and bull market conditions

Emerging market analysis of passive and active investing under bear and bull market conditions

Optimal Asset Allocation For Outperforming A Stochastic Benchmark Target

Portfolio performance under benchmarking relative loss and portfolio insurance: From omega ratio to loss aversion

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