جستجوی مقالات مرتبط با کلیدواژه "ریسک و بازده" در نشریات گروه "مالی"

Optimization of banks' foreign exchange portfolios aiming to determine an optimized combination of foreign exchange assets is in such a manner that it leads to a minimum risk and maximum yield. The approach used in this article, which is actually a priority over other methods is to use a combination of inexact mathematical models (fuzzy) and optimization. Thus, in this research, linear programming with fuzzy target coefficients has been used. The target coefficients are the same as the exchange rate. In other words, the daily rates of all currencies due to fluctuations are considered as fuzzy numbers. Therefore, in this investigation, by developing a multi-purpose mathematical model and by applying the fuzzy data relating to transaction rate of six foreign exchanges in year ended on March 20, 2020, including US dollar, Emirates Dirham, Japanese Yen, Turkish Lira, Korean Won and Euro, case by case risks related to the fluctuations of noted foreign exchanges and also their yield in the foreign exchange portfolio of Bank Mellat were measured and estimated for the future time horizon. All banks may benefit from the developed model. By determination of the optimal weight of each foreign exchange, upon describing and analyzing the existing status it specifies the optimal status, enabling banks to fulfill their foreign exchange obligations on due dates by properly and optimally making investment in foreign exchange assets meanwhile gaining a competitive advantage. "Gams Software" was used for solving the model.

Effectiveness and efficiency of investment is related to the use of technical analysis tools to maximize returns and minimize trading risks as the two challenges researchers commonly face. In the present study, the daily stock price information of 135 companies listed on Tehran Stock Exchange (TSE) during the period of 2010-2020 was used to investigate their investment strategies based on technical indicators in both short-term and medium-term periods. Investor behavioral reactions were indicated by the utilized technical indicators. The relationship between the values of the technical indicators and stock returns showed that the signals represented by the three indicators of Moving Average (MA), Exponential Moving Average (EMA), and Relative Strength Index (RSI) over a weekly to 6-month period to buy or sell the stocks (as a strategy) performed better than the other indicators. Therefore, investors can use a combination of these three indicators to determine their strategies of buying and selling so as to obtain more returns.IntroductionBehavioral finance is one of the new topics that has been raised and considered by some financial researchers over the past few decades. The behavioral financial perspective states that most investors’ behaviors depend on judgment, emotional tendency, and informal sources of information. A significant number of studies has shown that investors’ emotional behaviors affect the stock market. According to the traditional view of stock returns, changes in the stock price are related to systematic changes in the firm’s core values. However, recent studies have shown that investor’s emotional tendency plays an important role in setting prices and determining time-series returns. The effectiveness and efficiency of technical analysis tools can be determined by their applications to maximize returns and minimize transaction risks as the challenges researchers frequently face. There are several strategies for stock trading in a financial markets. Most of these strategies are based on some technical indicators. This study examined the performance of 11 technical indicators to find which ones better represent the investors’ behavioral reactions and finally provide an optimal investment strategy by combining them.   Method and DataTo investigate the relationship between the values of the technical indicators and stock returns, we extracted the daily stock price data of 135 companies listed in Tehran Stock Exchange (TSE) during the period of 2010-2020 and accordingly used the regression method and panel data. FindingsThe values of investments in the weekly to 6-month modes showed that the 6-month information led to higher returns compared to the weekly information, indicating significantly increased returns represented by the 11 studied indicators with the increasing time of the period. The indicators of Moving Average (MA), Exponential Moving Average (EMA), and Relative Strength Index (RSI) had the highest return values ​​in both the weekly and monthly information modes. In the mode of 6-month data, the true strength index (TSI) had a lower coefficient of variation, but better performance than the other indicators. Among the 11 indicators studied, the indicators of MA and EMA had a higher coefficient with stock returns in the short- and medium-term investments. The higher coefficient meant that the returns were enhanced by increasing these indicators of buying and selling. Therefore, they had more power or strength than the other indicators and could better represent the stock returns. The relationship between the values of the technical indicators and returns in the short run revealed that the indicator of MA better represented the stock returns compared to the other indicators. In the medium term, the mentioned indicator was a better indicator for representing the stock returns. When comparing performance of the strategies by the use of the technical indicators, moving from a weekly to 6-month period demonstrated more returns related to the mentioned indicators that were significantly different from the returns represented by the other indicators. Conclusion and discussion In all the investment periods, the indicator of MA had more power to influence on the returns and better represented the stock returns as compared to the other indicators. The signals that were given by the three indicators of MA, EMA, and RSI in the different short and medium-term periods to buy or sell the stocks (as a strategy) better showed the investors’ behavioral reactions and could be thus utilized with higher confidence in comparison to the other indicators since leading to higher returns and profitability. As the trend of the returns represented by the mentioned indicators moved from weekly to the 6-month mode, a larger difference was achieved in terms of significance, indicating that the relationship between the values of the technical indicators and stock returns could provide better buying-selling signals in a longer period.

The Impact of Observing the Principles and Rules of Correct Communication on Project Management (Case Study: Karaj City) In recent years, many efforts have been made to guide investors in proper investment and numerous models have been offered. The concepts of portfolio optimization and diversification have become tools for developing and understanding financial markets and financial decisions. In most optimization methods, the optimal answer and its accuracy are highly dependent on inputs to the extent that a more appropriate and accurate selection of input variables will be very important in stock portfolio optimization. In this research, through a regular and logical process based on the judgment method in a survey of 14 experts in the field of capital market investment and a quantitative and multivariate model of fuzzy network analysis, to assess the level of importance, ranking and refining the effective factors. Portfolio optimization was undertaken. Based on the analysis, the variables of profit volatility, return on capital, company value, market risk, stock profitability, financial structure, liquidity and survival index can be introduced as the most important factors affecting the optimization of the stock portfolio.

In this study, the investment management problem in the Tehran Stock Exchange and OTC companies in Iran as a stock portfolio optimization problem is investigated. This model has two objective functions including risk minimization and return maximization. The constraints are including the assigning all budgets to the companies considering budget limitation. In order to deal with the uncertainty conditions in the model parameters, the chance constraints approach is used and the objective functions are considered as a single problem using the goal-programming method. To solve the problem in the two-purpose mode, the augmented epsilon constraint method is used. According to the numerical results, it can be seen that problem solving in two-objective mode is able to produce Pareto solutions that do not dominate each other in a feasible space. Also, in case of uncertainty, the use of goal-programming leads to numerical solutions with the appropriate level of performance and costs are consistent with reality. In fact, the problem results in both multi-objective and single-objective situations can be implemented in real-world conditions. Finally, it can be said that the use of computational results of this study can be used as an operational tool.

The present study through a Dynamic Stochastic General Equilibrium (DSGE) Model and considering some of the observed facts in Iran's economy attempts to simulate the effects of oil and currency shocks on systematic risk and price returns and then the linear-logarithmic form of the equations was obtained after optimization and obtaining first-order brokers' conditions with using the Uhlig method. Examined samples collected from seasonal data from 2002 to 2016. In the end, the immediate reaction functions of the financial variables against oil revenue and currency shocks were investigated. The results of the research show that oil and currency shock initially have a negative effect on the systematic risk of stock and price returns, and then in the subsequent periods of this trend, they continue to fluctuate in a stable and stable state, so that the systematic risk fluctuations are greater than price return. Also, the Iranian economy shows the relative success of the model in the realities of the Iranian economy which determine by comparing the moments of the variables present in the model and the moments of the actual data.

In financial studies, portfolio can be defined as a set of investments that are selected and accepted by an individual or institution. Portfolio selection is one of the main concerns of investors in financial markets. The average-variance model with bound restrictions is considered as one of the main models in solving the portfolio optimization problem. In terms of complexity, this model is a polynomials NP-hard non-linear problem that cannot be accurately solved. In this study, an Antlion optimizer- Genetic algorithm (ALOGA) and a population based incremental learning and differential evolution algorithm (PBILDE), which are modern meta-heuristic models for solving optimization problem, are used to optimize the investment portfolio through increase the return and reduce the risk. Among 591 companies listed on Tehran stock exchange from April 2012 through March 2015, 150 companies were selected as the final sample using screening method. The data of these companies were analyzed using the applied algorithms in this research and their efficiency was compared together. The results indicate that ALOGA and PBILDE algorithms both are suitable for solving the portfolio optimization problem. In addition, using the ALOGA algorithm, it is possible to create an optimal portfolio with high accuracy and efficiency.

In fact optimization portfolio and diversity are the bases to develop and improve the financial markets and also the financial decisions. In the present research the function of the classic optimization portfolio (Markowitz Mean-Variance Model) has been studied as the targeted function in the presence of the Sharp Ratio. This study has presented a stable developing model of portfolio in which the results of the optimization portfolio have been shown by the relevant corresponding stable formulizing procedure based on the Factor Model by using the stock market indexes as the input data and the test of the stability of the input parameters .To do this the input data were chosen in a given confidence interval based on the worst predetermined scenario in order to maximize the Sharp Ratio. In the present survey there has been an attempt to have a survey on 164 monthly portfolio of all the accepted firms in Tehran Stock Exchange Market during a span of 15 years and the risk and return potentiality of each portfolio has been calculated based on both models of the Classic and Optimization Sharp Models. In the next level, a detailed survey has been conducted to investigate the probability of the existence of a significant difference between the risk and the real return potentiality of these two models by using the mean-difference test. The results indicate that the real return and real risk in the Sharp Model Respectively has no significant differences and has significant differences in the classic model.

Maximization of the share holders` wealth is the main objective in assets and liabilities management which depends on two key factors: risk and return. To attain this, the optimal management of the balance sheet could assist to maximize the profit and minimize the risk. This study investigates primarily the functionality of risk and return, and subsequently inspects their effects on the maximization of stockholders wealth by the means of network analysis method. To realize the proposed balance sheet of Tejarat Bank, and then a Multi Objective model of goal programming model has been applied. In this work, the criterion of risk and return have been estimated as ideal parameters in order to determine the model considering the existing structural limitations. The result shows that all defined objectives except market risk has been accomplished while deviations of all parameters except market risk has been taken into account as zero.

In this study, a formula is de rived for the period specific beta (market risk) for a portfolio of financial assets that has been formed on the basis of directional forecasts. This is an important contribution to the literature since measuring the risk of an actively managed portfolio is problematic due to the fact that managers may change fund risk conditional on market expectations. The period- specific nature of the measure is a significant advantage since historical fund re turns are not required and the beta is not influenced by prior fund returns’ deviations from the bench mark. The methodology employed allows for the development of a time series of fund betas that permits investigation into a number of important Empirical issues. This study is also of practical interest from the perspective of risk management and for both portfolio performance and attribution. Finally, there are many active strategies based on directional forecasts and the approach used here en com passes a significant proportion of these. The author of this article used of consultation and guidance of Rahnama Roodposhti Fereidoun, Professor and A member of the science team Islamic Azad University, Science and Research Branch ,Tehran, and thankses a lot of him.

Portfolio is combination of assets by an investor for investment. Process of selecting optimal stock basket is one of issue, which is paid attention by scholars. Various criteria of this process have changed by the time and this condition is necessary as optimal for making decision. Several criteria are included in selecting stock basket and it is necessary to use optimal tools for making better decision. Aim of this research is creating intelligent model in order to select optimal stock basket throughout adjusted differential evolution algorithm. Thus, we investigated risk and returns of companies listed at Tehran stock exchange annually. Sample study of research includes 102 companies during 2009 and 2013. Results of research showed that selected model by considering interaction between risk and expected return leads to selected optimal stock basket.

Official statistics show that in the recent decades, on average, only about 12% of the country's real GDP has been allocated to investment in the manufacturing sector, including the production of goods and services and a considerable portion of it has been absorbed into unproductive speculative activities.Accordingly, this study with purpose to examining and comparing security of investing in different markets has evaluated the risk of investing in four market including stocks, gold, currency and Iran's housing using the Value at Risk (VaR) Criteria. Also in this study to providing a more accurate analysis of the security investment based on investor's attitudes TOPSIS method has been used.All information required for the study was collected on a monthly basis for during 2002 - 2013.The result of this study showed based on VaR Criterion, the security of investment in the stock market is much lower than other markets, so investors in this market face higher risk of investments relative to other markets.Also, the result of this study showed based on TOPSIS method (according to risk and return critrias) risk averse and risk neutral Investors have the Similar behavior, So that the two groups prefer The investment in the housing market and then investment in the gold market on the parallel markets including exchange and stock. However, unlike the aforementioned groups, risk-taking investors prefered investing in the stock market and then investing in the housing market on the investment in the gold and currency markets.

Risk is not separable from the life of human in the whole history. So noticing to the risk was always existing. Evaluating risk were existing in different ways. In today’s world which is so complicated understanding and calculating of the risk has become so difficult. This calculation has become so difficult in financial market more. So there are many ways for this calculation from the simplest to the easiest. With regard to development in each country without investment there would be no develop. So ever investor for investment will need to item one is return and the other one is risk. . calculating return is not so much difficult, but the calculation of risk is so difficult and is a qualitative variable. So for answering the need of investors many ways emerged to solve and explain the risk. Between existing models two kind of models nonparametric and Nero net are investigated in this research in order to predict the value at risk of 21 investment companies in capital market of Iran. Then the best model is presented.

In today’s complicated world, we are witness many elements that affect on the profit level of companies. Each of these changes can promote group of companies to the top while destroy other group. So investment decision for actual and legal companies are severely depended on these changes. For decreasing the risk of these sudden changes a desirable portfolio should be determined to avoid least damages from changes. Dealers uses different models to determine the risk value of their portfolio in the capital market. Each of these models uses specific assumptions to determine value under the risk of portfolio. this paper intend to survey risk value of 21 investment companies by using theeconometrist models. Then to compare the prediction power of each models, and introduce the superior model.

Investors search for profitable investment opportunities with respect to the risk in financial markets. Since investors are willing to sell stocks whenever they want; they invest in liquid stocks with low liquidity risk. The free float is an important criterion that can show liquidity of a single stock. The higher the stock free float، the lower liquidity risk. There is a common sense that the higher the stock free float، the higher the transaction volume and so the higher the risk and also the higher the expected return. In this paper، the relasionship between risk، return and liquidity is tested with free float in Tehran Stock Exchanges (TSE) listed companies. Free float is calculated quarterly in TSE. So، data gathered from 2004 to 2008 quarterly for 111 TSE listed companies. With Eviews 6 and in form of panel data، analysis is done. Using OLS and GLS، no significant relationship is confirmed between risk and free float and no significant relationship is confirmed between return and free float. But significant relationship is confirmed between liquidity and free float. By using inverse regression in form of GLS، although it is not justifiable in practice، a significant relationship is confirmed between risk and free float.