The aim of this paper is to explain the financial markets fluctuations by strategic pattern of investment behaviors, together affected by different situations. The key point is finding the strategic patterns of changes in exchange rate, interest rate and other macro-economic indicators envisage economic environment and Commanders financial markets by fluctuations. These variations will affect expectations and demands of entrepreneurs, including investors, especially two indicators, degree of expected return and risk. Changes in the expected return and the degree of risk, put investors in that position in order to achieve the expected return performance with attention to new market conditions and the degree of their acceptable risk, choose their strategies for make some changes in the combination of their portfolio and rebalanced it. portfolio Rebalancing is one of the reachable strategies could be used by investors to review their investments ploy. In this paper, with attention to portfolio Rebalancing as a proper strategy to make coincidence in investor’ s portfolios simultaneously, we regard the variations in expected rate of return and degree of acceptable risk, affected by market conditions inflections, thus we design and to develop a Fuzzy linear programming model. To achieve the research strategic model, the three key components: risk, expected return and liquidity degree on equities have been used to design model and represent it. Also, consider to the importance role of transaction costs to gain the expected return in return calculating process of portfolio, we spot it in our model. In order to test the designed model, data and information of traded listed stock of Tehran exchange market, during 1384-1387. Then we import the different subjective satisfaction level of investors to test the model efficiency. The results show that the designed model could be use for the subjective satisfaction level of investors about the expected return, risk and liquidity of its portfolio, as well as possible and reach to the different strategy of portfolio Rebalancing for each investor with different satisfaction level.

Choosing an appropriate strategy for portfolio Rebalancing is a crucial matter in today's financial markets with logarithmic and high frequency transactions. In log-optimal approach named also active strategy, portfolio Rebalancing is a continuous time process and the optimality of such strategy is assured only for very long-term investment horizons. But continuous-time Rebalancing is impractical and portfolios have finite horizon usually. In this article we will introduce another strategy with less Rebalancing frequency to attain log-optimal utility at least, because of costly and infeasibility of continuous Rebalancing for investors. Then we will implement these strategies on a portfolio consist of several Tehran exchange stocks. The results showed that «Hybrid Rebalancing Strategy» offers more utility for investors in comparison to other strategies.

While market conditions change over the investment period, portfolio optimization models identify an optimal portfolio and hold it during the investment horizon. Accordingly, this paper presents a strategy to divide the investment period into sub-periods and revise an existing portfolio at the beginning of each sub-period. This study develops a Fuzzy multi-objective nonlinear portfolio Rebalancing model to upgrade a portfolio considering investor profile, transaction costs and risk-free rate of return. Finally, the proposed method is validated for three different investor profiles by using the real data derived from Tehran Stock Exchange. The results show that the proposed strategy outperforms the market.

The ability to choose the most optimal change in the composition of the portfolio of assets, brings the investor to the highest level of investment in terms of efficiency and effectiveness in the dynamic and changing market. Rebalancing the portfolio occur through a change in the composition of assets weights, remove the assets, bought and sold assets and etc. Therefore, in this study solving the multiobjective portfolio Rebalancing model with Fuzzy parameters. The return, risk, liquidity and uncertainty as a key financial criteria are considered. Due to its importance as well as transaction costs, the net return of the portfolio are adjusted. the multiobjective portfolio Rebalancing model with Fuzzy parameters is solved by Fuzzy goal programming and a hybrid intelligent algorithm that combines Fuzzy simulation with a genetic algorithm. The results demonstrated the effectiveness of the solution approach and effciency of the model in practical applications of Rebalancing an existing portfolio.

One of the main concerns of investors in financial market is reduction of risk and achieving desired return. One of the ways that reduce risk, is creating portfolio; but due to market changes, the optimal portfolio will not be stable and it is necessary to be control and Rebalancing. Generally, investors in financial market are divided into two categories: real investors and legal investors. The volume of real investors' capital is little and is called small investors, too. portfolio selection and Rebalancing for small investors need attention to their criteria and limitations that are not considered in the classic financial models such as Markowitz’ s model. These criteria are transaction cost, dividend, systematic risk and transaction units. In this study, a multi-objective and comprehensive model are provided that addresses the goals and limitations of small investors. For this purpose, the lexicographic goal programming is used and a mixed integer programming model is provided. Finally, the model is solved with actual data and the results are analyzed.

The management of financial portfolios or funds constitutes a widely known problematic in financial markets which normally requires a rigorous analysis in order to select the most profitable assets. In this field designing profitable automated trading systems, which could trade dynamically and make appropriate decisions is significantly important. Technical analysis is a popular method to predict future price movement. One of the deficiencies of technical analysis is the lack of attention to risk of investing and portfolio management. This study has developed automated portfolio management systems using technical analysis indicators to find uptrend price movements and used GARCH and FIGHARCH models to consider the risk in the decisions. The developed model has assayed in one year time scope. The results illustrate that using FIGARCH models has made superior return to risk ratio. Also the ratio shows that the developed models are significantly better, comparing the other investing methods such as Markowitz model and buy and hold strategy.

Recently, the economic crisis has resulted in instability in stock exchange market and this has caused high volatilities in stock value of exchanged firms. Under these conditions, considering uncertainty for a favorite investment is more serious than before. Multi-objective portfolio selection (Return, Liquidity, Risk and Initial cost of Investment objectives) using MINMAX Fuzzy goal programming for a Fuzzy Allocated portfolio is considered in this research and all the main sectors of investment are assumed under uncertainty. A numerical example on stock exchange is presented to demonstrate the validity and strengths of the proposed approach.

Stock portfolio problems are one of the most relevant real-world problems. In this study, we discuss the portfolio's risk amount, rate of risk-return, and expected return rate under a Fermatean Fuzzy environment. A linear programming problem is used to formulate a Fermatean Fuzzy portfolio. The Fermatean Fuzzy portfolio is converted to a deterministic form using the score function. Lingo software is used to solve these deterministic portfolio problems. The main feature of this model is that investors can select a risk coefficient to enhance predicted returns and customize their strategies according to their circumstances. An example is offered that illustrates the effectiveness and dependability of the proposed approach.