Investment decision making is one of the key issues in financial management. Investor might know about different asset types when facing with various options and the ways in which investors can incorporate them in devising a strategy is significant. Selecting the approp More
Investment decision making is one of the key issues in financial management. Investor might know about different asset types when facing with various options and the ways in which investors can incorporate them in devising a strategy is significant. Selecting the appropriate tools and techniques that can make optimum portfolio is one of the main objectives of the investment world. In this study it is tried to optimize the decision making in stock selection or optimization of portfolio by means of artificial colony of honeybee algorithm. And to determine the effectiveness of the algorithm, Sharp criteria algorithm, the trainer criteria and its downside risk were calculated and compared with the portfolio made up of genetic and ant colony algorithms .The sample consisted of active firms listed in the Tehran Stock Exchange from 2005 to 2015. The sample was selected by the systematic removal method. The findings show that Sharp criteria algorithm formed by the artificial bee colony algorithm functions better than the genetic and ant colony algorithms in terms of portfolio formation .However, the trainer's criteria and downside risk of the stock portfolio formed through the artificial bee colony algorithm shows the optimum function, this difference is not statistically significant.
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Today, most energy consumption in industry is related to induction motors. Evaluation of induction motor’s efficiency is an important issue for life estimation, extend the life and energy saving managements. Using the estimated efficiency of the induction motor, i More
Today, most energy consumption in industry is related to induction motors. Evaluation of induction motor’s efficiency is an important issue for life estimation, extend the life and energy saving managements. Using the estimated efficiency of the induction motor, its performance can be judged and replacing the existing low efficiency motor by a high efficiency motor could be decided. In this paper, a novel and efficient method based on Modified Artificial Bee Colony (MABC) Algorithm is presented for efficiency estimation in the induction motors. The main advantage of the proposed method is efficiency evaluation of induction motor without any intrusive test. In order to demonstrate the capabilities of the proposed method, a comparison with other intelligent optimization algorithms is performed. Then, one of the important applications of efficiency estimation, which replaces the high efficiency induction motors instead of conventional motors, is discussed. The results of the calculation of energy savings show that if a standard motor is replaced with a high efficiency motor, energy savings will be significant.
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Multi-asset portfolio management and optimization have always been of interest to investors. Due to the inflation in Iran market, different performance of the asset classes in different market conditions and the ability to earn more profit along with less risk by divers More
Multi-asset portfolio management and optimization have always been of interest to investors. Due to the inflation in Iran market, different performance of the asset classes in different market conditions and the ability to earn more profit along with less risk by diversifying the types of assets, it seems necessary to select a portfolio consisting of stocks, foreign currency and commodities. In this paper, assets of the above categories, including Emami coins, American dollar, and 11 sector indices, are considered in the portfolio composition. Due to the importance of the risk measure in multi-asset portfolio optimization, a model with conditional value at risk, the historical simulation approach has been extended and its efficiency has been compared with the mean-variance model. The models have been solved using the artificial bee colony and imperialist competitive algorithms. The daily asset prices in the period 2013 to 2020 have been used to evaluate the models in Iran market. Results show that the mean-conditional value at risk model performs better than the mean-variance in the training and testing periods. Furthermore, optimized portfolios with the artificial bee colony algorithm could outperform the imperialist competitive algorithm based on the Sharpe ratio, conditional Sharpe ratio, and return on risk.
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