Handling uncertainty through confidence intervals in portfolio optimization

Solares, Efrain; Coello, Carlos A. Coello; Fernández, Eduardo; Navarro, Jorge

doi:10.1016/j.swevo.2018.08.010

Cited by 23 publications

(32 citation statements)

References 33 publications

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“…That is, he/she considers more valuable the information about the worst scenarios that could happen when selecting a portfolio; thus, he/she would select high values for looking for MOL2NET, 2018, 4, http://sciforum.net/conference/mol2net-04 6 protection against those scenarios (see Figure 1(a) of Ref. (Solares et al, 2018)). On the other hand, if the investor is lowly risk-averse, he/she would prefer to make a decision based on intervals that tend to the expected return (see Figure 1(b) of Ref.…”

Section: Handling Uncertainty Through Confidence Intervals In Portfolmentioning

confidence: 99%

“…From all these objectives, the most outstanding one is maximization of the portfolio's return/profit (Solares et al, 2018). The return of a given portfolio is the arithmetic difference between the buy-cost and the sell-cost of the portfolio.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we will describe two papers that address the latter situation; namely, Refs. (Solares et al, 2018) and (Fernandez et al, 2018a).…”

Section: Introductionmentioning

confidence: 99%

“…The main objectives in Refs. (Solares et al, 2018) and (Fernandez et al, 2018a) are to present approaches capable to find the most satisfactory portfolio from the investor's perspective when many objective functions are considered. In those works, the investor's behavior facing risk, the estimations of the portfolios' future returns, and the risk of not attaining those returns are all represented through probabilistic confidence intervals.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

<strong>Portfolio optimization with incorporation of preferences and many criteria</strong>

Solares¹,

Fernández²,

Navarro³

2018

Proceedings of MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th Edition

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Portfolio optimization is one of the most addressed areas in operational research, mainly because of its practical relevance and interesting theoretical challenges. Recently, Solares et al. (2018) have proposed using probabilistic confidence intervals as criteria to select the most convenient portfolio. An approach following this idea allows the investor to consider not only the expected impact of the portfolios but also the risk of not obtaining that expected impact. Moreover, it identifies the behavior of the investor in presence of risk and gives her/him support depending on her/his own preferences. On the other hand, there are situations where the investor is not satisfied with the knowledge provided by probabilistic information (e.g., such information is precarious or the investor gives importance to other information, such as financial data). In this case, the investor may be interested in considering many criteria in order to select the most convenient portfolio.

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Section: Handling Uncertainty Through Confidence Intervals In Portfolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Here, we will describe two papers that address the latter situation; namely, Refs. (Solares et al, 2018) and (Fernandez et al, 2018a).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

<strong>Portfolio optimization with incorporation of preferences and many criteria</strong>

Solares¹,

Fernández²,

Navarro³

2018

Proceedings of MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th Edition

Self Cite

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“…Interval analysis is another approach that has been recently applied to model imperfect knowledge in project portfolio selection and stock portfolio optimization (c.f. [30][31][32][33][34][35]). Interval analysis combined with outranking methods was recently applied to portfolio optimization in [24,26].…”

Section: Introductionmentioning

confidence: 99%

An Interval-Based Evolutionary Approach to Portfolio Optimization of New Product Development Projects

Fernández

Gómez-Santillán

Rangel-Valdez

et al. 2019

Mathematical Problems in Engineering

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The growth of large enterprises in the manufacturing market commonly depends on good New Product Development (NPD) projects; these projects represent a strategy to overcome competitors inside a competitive environment. The management of such projects is usually complex and involves risk due to the changing and conflicting environment. The approaches that tackle the problem lack an explicit consideration of the DM’s attitude facing uncertainty and imprecision related to the risk and particularly in the presence of time-interdependencies. This paper proposes a model of the time-related effects, under imperfect knowledge, and their influence in choosing optimal NPD portfolios. The proposed approach is an interval-based method to solve NPD portfolio optimization problems under different forms of imperfect knowledge. This approach has the advantage of a unified and simple way to model the different sources of imprecision, vagueness, uncertainty, and arbitrariness. The attitude of the DM facing the imperfect knowledge is adjusted by using some meaningful parameters. The research focuses particularly in creating a method useful for risk-averse DMs. The proposal was tested through an experimental design that compared the results achieved by the new method against the expected value in portfolios. The results revealed that high levels of conservatism might prevent wasting resources in failed projects.

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Evaluation of strategy portfolios

Wang,

Kresta,

Tichý

2024

Comput Manag Sci

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People usually create a portfolio in order to diversify the risk coming from individual investments. To get a high yield with a good level of diversification, investors usually seek professional advice from portfolio managers. However, the true performance of an optimized portfolio usually depends on the correctness of the estimates of the distribution of future returns, which is often a matter of luck rather than skill. Thus, the optimization models may not be better than randomly selected portfolios. Our aim is to find how the so-called strategy portfolios, i.e., portfolios obtained by some decision optimized for a long-run horizon, perform compared to a benchmark, namely, a random investment, under specific market conditions. For this purpose, we evaluate several portfolio strategies over two periods of crisis: the subprime mortgage crisis and the Covid-19 pandemic, as well as run a moving window analysis over a longer horizon. In each case, the results are compared with the performance of random-weight portfolios. We find that if the strategy is minimization, the portfolios perform well; however, for the maximization of the objectives, the results are rather mixed.

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Handling uncertainty through confidence intervals in portfolio optimization

Cited by 23 publications

References 33 publications

<strong>Portfolio optimization with incorporation of preferences and many criteria</strong>

<strong>Portfolio optimization with incorporation of preferences and many criteria</strong>

An Interval-Based Evolutionary Approach to Portfolio Optimization of New Product Development Projects

Evaluation of strategy portfolios

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