This paper deals with fuzzy optimization schemes for managing a portfolio in the framework of risk–return trade-off. Different models coexist to select the best portfolio according to their respective objective functions and many of them are linearly constrained. We are concerned with the infeasible instances of such models. This infeasibility, usually provoked by the conflict between the desir...

This paper develops a general equilibrium, continuous time model where portfolio constraints generate mispricing between redundant securities. Constrained consumption-portfolio optimization techniques are adapted to incorporate redundant, possibly mispriced, securities. Under logarithmic preferences, we provide explicit conditions for mispricing and closed-form expressions for all economic quan...

The problem of portfolio selection in investment concerns with minimizing the risk for a prespecified level of return. In this paper, the constraint on the level of return is fuzzified and the technique of fuzzy evolutionary programming is employed to select an optimal portfolio of securities with low risk and with highly acceptable level of total return. Experimental results show the method is...

We study Spectral Measures of Risk from the perspective of portfolio optimization. We derive exact results which extend to general Spectral MeasuresMφ the Pflug—Rockafellar—Uryasev methodology for the minimization of α—Expected Shortfall. The minimization problem of a spectral measure is shown to be equivalent to the minimization of a suitable function which contains additional parameters, but ...

In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A newpossibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming...

In this paper, we derive a portfolio optimization model by minimizing upper and lower bounds of loss probability. These bounds are obtained under a nonparametric assumption of underlying return distribution by modifying the so-called generalization error bounds for the support vector machine, which has been developed in the field of statistical learning. Based on the bounds, two fractional prog...

Financial problems have been the subject of much research. A widely used approach in recent work on these problems is the use of fuzzy set theory, where fuzzy terms are used to model the uncertain environments. The purpose of this work is to combine the fuzzy analytic hierarchy process (AHP) with the portfolio selection problem. More specifically, the decision-making problem is to decide which ...

In this paper we demonstrate that coordinate-wise descent algorithms can be used to solve portfolio selection problems in which asset weights are constrained by l q norms for 1 ≤ q ≤ 2. A special case of the such problems is when q = 1. The l 1 norm constraint promotes zero values for the weight vector, leading to an automatic asset selection for the portfolio. We first consider the case of min...

Finding the best way to optimize the portfolio after Markowitz's 1952 article has always been and will continue to be one of the concerns of activists in the investment management industry. Researchers have come up with different solutions to overcome this problem. The introduction of mathematical models and meta-heuristic models is one of the activities that has influenced portfolio optimizati...