A novel approach to select the best portfolio considering the preferences of the decision maker

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

doi:10.1016/j.swevo.2019.02.002

Cited by 27 publications

(23 citation statements)

References 46 publications

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“…Stock return forecasting has been a widely discussed task among technical analysts (Shalini et al, 2019;Picasso et al, 2019;Reschenhofer et al, 2019) and investors making discussions based on fundamental analysis principles (Xidonas et al, 2009). Some authors suggest combining these two approaches to get an optimal output of a selected portfolio (Fernandez et al, 2019;Hasuike & Mehlawat, 2018;Meng & Chen, 2018;Eiamkanitchat et al, 2016). Forecasting of a stock price should be based on appropriate methods, which, as a result, can contribute to a higher return of portfolio (Yu, 2011;Östermark, 1991).…”

Section: Introductionmentioning

confidence: 99%

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

Babirath¹,

Schmitl²,

Malec

et al. 2020

Investment Management and Financial Innovations

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The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

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Section: Introductionmentioning

confidence: 99%

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

Babirath¹,

Schmitl²,

Malec

et al. 2020

Investment Management and Financial Innovations

View full text Add to dashboard Cite

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“…The uncertainty of projects is related to the degree of precision with which the variations in outcome, resources, and work processes of projects can be forecast [21]. From the point of view of multicriteria decisions, other authors prefer to use the more general term "imperfect knowledge" (e.g., [22][23][24][25][26]); the sources of this imperfect knowledge are arbitrariness, imprecision, illdetermination, and uncertainty in data and model parameters [22]. In this sense, the term "uncertainty" is related to the values of certain data in a more or less distant future [22].…”

Section: Introductionmentioning

confidence: 99%

“…[30][31][32][33][34][35]). Interval analysis combined with outranking methods was recently applied to portfolio optimization in [24,26]. The interval approach is a natural and simple way in which to express imperfect information, it does not matter its source.…”

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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“…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%

<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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A novel approach to select the best portfolio considering the preferences of the decision maker

Cited by 27 publications

References 46 publications

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

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

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

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