The basic problem in finance theory is the selection of an appropriate mix of assets in a portfolio in order to maximize portfolio expected return and subsequently to minimize portfolio risk. Another approach takes into account portfolio performance expressed by various measurement techniques e.g. Sharpe ratio, Treynor ratio, Jensen's alpha, Information ratio, Sortino ratio, Omega function and Sharpe Omega ratio that are focused on determine the allocation of the available resources in the selected group of assets. This paper presents the alternative approach computing the weights of assets in portfolio assets based on the nonlinear measure techniques: Sortino ratio and Omega function. The proposed alternative includes principle of differential evolution from the group of evolutionary techniques. The experiments are set up on assets included in Dow Jones Industrial Average. Presented original approach enables using also other evolutionary algorithms in the area of portfolio selection based on different measurement techniques.
The task of the traveling salesman, which is to find the shortest or least costly circular route, is one of the most common optimization problems that need to be solved in various fields of practice. The article analyzes and demonstrates various methods for solving this problem using a specific example: heuristic (the nearest neighbor method, the most profitable neighbor method), metaheuristic (evolutionary algorithm), methods of mathematical programming. In addition to classic exact methods (which are difficult to use for large-scale tasks based on existing software) and heuristic methods, the article suggests using the innovative features of the commercially available MS Excel software using a meta-heuristic base. To find the optimal solution using exact methods, the Excel (Solver) software package was used, as well as the specialized GAMS software package. Comparison of different approaches to solving the traveling salesman problem using a practical example showed that the use of traditional heuristic approaches (the nearest neighbor method or the most profitable neighbor method) is not difficult from a computational point of view, but does not provide solutions that would be acceptable in modern conditions. The use of MS Excel for solving the problem using the methods of mathematical programming and metaheuristics enabled us to obtain an optimal solution, which led to the conclusion that modern tools are an appropriate addition to solving the traveling salesman problem while maintaining the quality of the solution.
The main aim of this paper was to examine overconfidence in the domain of bullshit detection and the contributing factors that explain why some people have the blind spot about their own incompetence. To verify whether people's lack of metacognitive awareness of their bullshit detection abilities is the result of self-enhancement motivation, we exposed one group of participants to a self-esteem threat scenario (by providing them with negative feedback on their cognitive abilities, i.e. intelligence) and compared it with two control groups (no feedback and positive feedback). The sample consisted of 596 adult Slovaks (47.1 % of women) aged 18 to 70 (M = 43.92, SD = 14.11). However, our manipulation had an effect only on overplacement. Bullshit detection predicted both overestimation and overplacement. Overconfidence was slightly associated with worse cognitive abilities (the relationship was mediated by bullshit detection ability) and higher self-esteem, but no relationships with dark traits were found. This finding is yet another evidence of the double curse of the Dunning-Kruger effect, i.e. the lack of comprehension of people´s own cognitive limitations.
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