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Investment Risk Appraisal
Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. This
approach may account for what occurs most of the time in the market, but the picture it presents does not reflect the reality, as the
major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy
approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the
data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each
investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK
companies and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we
discuss the grounds for classical asset pricing model revision and argue that the demand for relaxed assumptions appeals for another
approach to modelling the market environment
A multiobjective credibilistic portfolio selection model. Empirical study in the Latin American Integrated Market
[EN] This paper extends the stochastic mean-semivariance model to a fuzzy multiobjective model, where apart from return and risk, also liquidity is considered to measure the performance of a portfolio. Uncertainty of future return and liquidity of each asset are modeled using L-R type fuzzy numbers that belong to the power reference function family. The decision process of this novel approach takes into account not only the multidimensional nature of the portfolio selection problem but also realistic constraints by investors. Particularly, it optimizes the expected return, the semivariance and the expected liquidity of a given portfolio, considering cardinality constraint and upper and lower bound constraints. The constrained portfolio optimization problem resulting is solved using the algorithm NSGA-II. As a novelty, in order to select the optimal portfolio, this study defines the credibilistic Sortino ratio as the ratio between the credibilistic risk premium and the credibilistic semivariance. An empirical study is included to show the effectiveness and efficiency of the model in practical applications using a data set of assets from the Latin American Integrated Market.García García, F.; Gonzalez-Bueno, J.; Guijarro, F.; Oliver-Muncharaz, J. (2020). A multiobjective credibilistic portfolio selection model. Empirical study in the Latin American Integrated Market. Enterpreneurship and Sustainability Issues. 8(2):1027-1046. https://doi.org/10.9770/jesi.2020.8.2(62)S102710468
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data
Article ID 314728Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors
in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide
their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes
harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics
where a large number of irrelevant features are involved.This paper provides a methodology for feature selection in classification
of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and
maximise the classifier quality measure (e.g., accuracy).The proposed methodology makes use of self-adaptation by applying the
feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier.This work was partially supported by the Portuguese Foundation for Science and Technology under Grant PEst-C/CTM/LA0025/2011 (Strategic Project-LA 25-2011-2012) and by the Spanish Ministerio de Ciencia e Innovacion, under the project "Gestion de movilidad efficiente y sostenible, MOVES" with Grant Reference TIN2011-28336
Interval-valued upside potential and downside risk portfolio optimisation
A novel interval optimisation approach is developed to include
imprecise forecasts into the portfolio selection process for investors
measuring upside potential and downside risk as deviations from a
target return. Crisp scenarios are substituted by interval scenarios and
the resulting interval optimisation problem is solved in a tractable
manner by means of a bi-objective formulation exploiting a partial
order relation between intervals. Four utility case studies involving
assets from the F.T.S.E. M.I.B. Index are considered to illustrate how
impreciseness can be efficiently handled in portfolio management
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