جستجوی مقالات مرتبط با کلیدواژه "portfolio selection" در نشریات گروه "صنایع"

In the ever-evolving realm of finance, investors have a myriad of strategies at their disposal to effectively and cleverly allocate their wealth in the expansive financial market. Among these strategies, portfolio optimization emerges as a prominent approach used by individuals seeking to mitigate the inherent risks that accompany investments. Portfolio optimization entails the selection of the optimal combination of securities and their proportions to achieve lower risk and higher return. To delve deeper into the decision-making process of investors and assess the impact of psychology on their choices, behavioral finance biases can be introduced into the portfolio optimization model. One such bias is regret, which refers to the feeling of remorse that can induce hesitation in making significant decisions and avoiding actions that may lead to unfavorable investment outcomes. It is not uncommon for investors to hold onto losing investments for extended periods, reluctant to acknowledge mistakes and accept losses due to this behavioral tendency. Interestingly, in their quest to sidestep regret, investors may inadvertently overlook potential opportunities. This research article aims to undertake an in-depth examination of 41 publications from the past two decades, providing a comprehensive review of the models and applications proposed for the regret approach in portfolio optimization. The study categorizes these methods into accurate and approximate models, scrutinizing their respective timeframes and exploring additional constraints that are considered. Utilizing this article will provide investors with insights into the latest research advancements in the realm of regret, familiarize them with influential authors in the field, and offer a glimpse into the future direction of this area of study.  The extensive review findings indicate a growth in the adoption of the regret approach in the past few years and its advancements in portfolio optimization.

Portfolio selection is of great importance among financiers, who seek to invest in a financial market by selecting a portfolio to minimize the risk of investment and maximize their profit. Since there is a covariant among portfolios, there are situations in which all portfolios go high or down simultaneously, known as systemic risks. In this study, we proposed three improved meta-heuristic algorithms namely, genetic, dragonfly, and imperialist competitive algorithms to study the portfolio selection problem in the presence of systemic risks. Results reveal that our Imperialist Competitive Algorithm are superior to Genetic algorithm method. After that, we implement our method on the Iran Stock Exchange market and show that considering systemic risks leads to more robust portfolio selection. . Results reveal that our Imperialist Competitive Algorithm are superior to Genetic algorithm method. After that, we implement our method on the Iran Stock Exchange market and show that considering systemic risks leads to more robust portfolio selection.

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.

Moving averages are one of the most popular and easy-to-use tools available to a technical analyst, and they also form the building blocks for many other technical indicators and overlays. Building a moving average (MA) model needs determining four factors of (1) approach of issuing signals, (2) technique of calculating MA, (3) length of MA, and (4) band. After a literature review of technical analysis (TA) from the perspective of MA and some discussions about MA as a TA, this paper is structured to highlight the effects that each of the first three factors has on performance of MA as a TA. The results that based on some experiments with real data support the fact that deciding about the first and second factors is not much critical, and more attention should be paid to other factors.

In the portfolio selection problem, the manager considers several objectives simultaneously such as the rate of return, the liquidity and the risk of portfolios. These objectives are conflicting and incommensurable. Moreover, the objectives can be imprecise. Generally, the portfolio manager seeks the best combination of the stocks that meets his investment objectives. The imprecise Goal Programming model will be utilized to build the most satisfactory portfolio. The concept of satisfaction functions will be utilized to integrate explicitly the preferences of the portfolio’s manager. The developed model has been applied to portfolio selection within the Tunisian stock exchange market.