سید محمدرضا داوودی

After closing, the multi-period portfolio can be corrected and revised at regular intervals. The philosophy behind using multi-period portfolio models is that investors often have a multi-period view of future changes in assets, which can be the result of technical and fundamental analysis or statistical model analysis. In conventional multi-period portfolio models, it is assumed that the forecast and correction time horizons are the same for all assets. However, one asset may be forecasted over a one-month horizon while another may be forecasted over a two-month horizon, and both may be revised in the future. The purpose of this study is to present a multi-period portfolio model in which assets have different time horizons for corrections or an asset may not be traded for the first few periods and then enter the correction stage. In this model, fuzzy variables defined in a credibility space are used to describe the return, and the credibility measure controls the risk. The model's objective function is to maximize the portfolio's ultimate wealth, and a constraint is used to control portfolio risk, in which the validity of the portfolio's ultimate wealth below a certain threshold is controlled at a certain level of confidence. A combination of particle swarm optimization and simulation is used to find the best solution. Finally, using a numerical example, the model is implemented on a portfolio with 6 assets and 4 monthly time steps on the Tehran Stock Exchange.

Purpose : The purpose of this study is to design and explain the development model of future managers based on talent management and personality types with the grounded theory approach in the headquarters of the National Social Security Organization. Method : This research was conducted with a qualitative-inductive approach and Strauss-Corbin's grounded theory method. The research tool was a semi-structured interview. Using the grounded theory method, the data obtained from interviews with 12 elites and qualified specialists of the Social Security Organization were analyzed in three open, axial, and selective coding stages. Findings : 19 general categories in the form of a paradigm model in which these factors include causal conditions (individual factors, organizational factors, lack of proper selection, knowledge and skills of employees), central phenomenon (future managers based on talent management and personality types), contextual conditions (Organizational context, Talent Selection, Talent utilization), intervening conditions (psychological (individual) factors, Individual factors, Management factors), and strategies (Talent sourcing, Empowerment of managers and employees, Proportion of job and employment, Succession, Foresight and Cognitive Strategy) and Consequences (Organizational results, Public satisfaction, Futurization). Conclusion : Conclusion: Recent developments in the field of human resources have made this field doubly important. Organizations try to employ the most specialized people at management levels, and therefore talent management, as a key king, examines the types of situations involved in this field. Proper use of talents leads to success and puts the organization on the right track.

Traders in the capital market always seek methods to make full use of available information and combine them to find the best buying and selling strategy. The present study uses a linear hybrid system to combine 106 signals from moving averages oscillators and RSI signals in the technical analysis along with two buy and sell bonds. In addition, the system has correctional property and modifies its parameters over time and according to new information. The result of the research on the Tehran Exchange overall index in the period 1380 to 1397 indicates that the system after the optimal training on training data has an average of daily returns of 0/0025, 0/0048 risk, and a daily Sharp ratio of 0/52, which is better than the individual performance of each signal and market performance in daily average return and sharp ratio criterion.

Technical analysis is constituted as an approach in the market analysis which is based on the study of pricing behavior and shares size in the past and price determination and its procedure in the future. Algorithmic transactions are growing rapidly in order to automate business strategies, given the arrival of computer-based technologies and the rapid processing of bulky information. Trading systems combine input information and ultimately identify the time of purchase and sale by forming one signal. In this paper, the training system is a kind of fuzzy inference system that combines fuzzified RSI and SO signals from technical analysis. The system’s trade rules database (selling, buying, and holding) would be calculated based on an optimization process using PSO. This optimization process should be repeated at certain intervals to keep the system up to date. This process is called the corrective property of systems. The findings on the overall index in the period 2001/3/21-2019/3/20 indicate that the system having optimized training on training data has an average daily return of /0027, risk-taking of /0065 and the daily sharp ratio of /42. Concerning the index of return and sharp ratio, the findings reveal that the system outperforms the signals and the market performance.