Mathematical programming methods dominate in the portfolio optimization problems, but they cannot be used if we introduce a constraint limiting the number of different assets included in the portfolio. To solve this model some of the heuristics methods (such as genetic algorithm, neural networks and particle swarm optimization algorithm) must be used. In this paper we utilize binary particle sw...

This paper studies consumption and portfolio choice under incomplete markets and parameter uncertainty. I establish the necessary conditions under which the investor’s optimization problem under incomplete markets can be transformed into a complete markets problem. This paper extends the literature on asset allocation by obtaining closed form solutions to the consumption and portfolio problem o...

Portfolio methods provide an effective, principled way of combining a collection of acquisition functions in the context of Bayesian optimization. We introduce a novel approach to this problem motivated by an information theoretic consideration. Our construction additionally provides an extension of Thompson sampling to continuous domains with GP priors. We show that our method outperforms a ra...

In this working paper we study some properties of a particular mapping in Rn related to an optimization problem with one equality constraint. We motivate the definition of the relevant mapping starting from a portfolio selection problem, in which we minimize the risk of an investment (the variance of its return) with one equality constraint given by a fixed level of the return itself. The vecto...

Portfolio selection (optimization) problem is a very important and widely researched problem in the areas of finance and economy. Literature review shows that many methods and heuristics were applied to this hard optimization problem, however, there are only few implementations of swarm intelligence metaheuristics. This paper presents artificial bee colony (ABC) algorithm applied to the cardina...

According to the theory proposed by Acerbi & Scandolo (2008), the value of a portfolio is defined in terms of public market data and idiosyncratic portfolio constraints imposed by an investor holding the portfolio. Depending on the constraints, one and the same portfolio could have different values for different investors. As it turns out, within the Acerbi-Scandolo theory, portfolio valuation ...

The paper presents a simulation-optimization approach using genetic algorithm to the supplier selection problem. The problem consists in selecting a portfolio of suppliers from a set of pre-selected candidates. The supplier selection is a multi-criteria problem that includes both qualitative and quantitative criteria. In order to select the best suppliers it is crucial to make a trade off betwe...

In this contribution we consider a dynamic portfolio optimization problem where the manager has to deal with the presence of minimum guarantee requirements on the performance of the portfolio. We briefly discuss different possibilities for the formulation of the problem and present a quite general formulation which includes transaction costs, cardinality constraints and buy-in thresholds. The p...

Environmental modellers recurrently work with decisions where a portfolio of actions has to be formed to effectively address the overall situation at hand. When creating the portfolio, one needs to consider multiple objectives and constraints, identify promising action candidates and examine interactions among them. The area of portfolio decision analysis deals with such tasks. This paper revie...

In this paper, a cardinality constrained mean-variance model is introduced for the portfolio optimization problems. This model is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. The use of heuristic algorithms in this case is necessary. Some studies have investigated the cardinality constrained mean-variance model using heuristic algorithm. But alm...