جستجوی مقالات مرتبط با کلیدواژه « بازده » در نشریات گروه « مالی »

The present study deals with redesigning the design of the financial stability model in order to respond to the return of Islamic banking to the shock of the value of the national currency for the years 1400-1373. For this purpose, using the structural vector autoregression model (SVAR), which are known as impulse models; The response of Islamic banking returns to the shocks of national currency value, financial stability, inflation and inflation were investigated. According to the results, the response coefficient of the yield impulses of cooperative banking contracts to oil revenues, weakening of the value of the national currency and inflation is negative and equal to 0.01, 0.25 and 0.06. Also, the response of financial development momentum coefficient to the yield of cooperative banking contracts is positive and equal to 0.32. The increase in oil and foreign exchange revenues of the central bank causes an increase in the monetary base of the country, and the volume of liquidity also increases, and therefore inflation will also increase. The expectation of inflation in the future period and uncertainty about the inflation rate are also effective in fueling the intensity of inflation, and as uncertainty increases, the amount of investment in the production sector decreases and the production situation and the yield of contracts also worsen. On the other hand, the artificial pricing of the exchange rate in the years before thein order to reduce the pressure of the foreign exchange market, it is suggested that the annual off

Investors, in their pursuit to maximize expected returns, minimize risks in their stock portfolios, and achieve the desired benefits, require suitable methods and criteria to select stocks for their portfolios and allocate capital. One of the most important things in stock portfolio optimization is the use of a suitable optimization algorithm. The function of the multi-objective portfolio optimization model is quadratic. Quadratic functions are a special class of nonlinear programming problems in which the objective function is quadratic and the constraints are linear. Common algorithms for quadratic programming require certain parameters with fixed values. Such algorithms are extensively employed for solving real-world problems, particularly in financial contexts. The major objective of this research is to apply the inner point mathematical algorithm to optimize the stock portfolio and to use this algorithm to address the multi-objective portfolio optimization problem. With the GlueVaR risk measurement criterion, the problem of portfolio optimization takes into account the two objectives of maximizing returns during the research period and reducing investment risk, reassuring investors to make better and more accurate decisions about the final object of this research.

The necessary information for this study was provided by 50 active companies listed on the Tehran Stock Exchange. However, due to the availability of their daily prices during the study period, the final number of companies considered was reduced to 33. The inner point mathematical approach was utilized to optimize the model with the dual objectives of increasing efficiency and reducing risk. To demonstrate the effectiveness and capability of the algorithm in solving the problem of two-objective optimization, its output was compared with other risk measurement criteria such as variance, and value at risk (VaR). The investment risk in the stock portfolio was also calculated using the GlueVaR criterion. Comparing conditional (CVaR) was also done. The GlueVaR criterion has the advantage over the other criteria since it takes the investor's attitude toward risk into account. This advantage formed the basis of the calculation method in this research according to the mentioned algorithm.

According to the research, value at risk (VaR) and conditional value at risk (CVaR) perform better than other variance risk measures in the portfolio optimization model with the GlueVaR risk measure and the internal point optimization method for determining the most effective border. Additionally, when applied to optimization problems, the internal point method discovers the optimal point with fewer iterations, providing strong evidence of the algorithm's effectiveness.

Based on the current findings, it is evident that the internal point algorithm is effective in resolving stock portfolio optimization issues. Additionally, the GlueVaR risk measurement criterion outperforms VaR, variance, and CVaR for most investors with diverse risk and return preferences

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.

The existence of bubbles in the market,especially the capital market,can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country.On the other hand,due to the goal of investors in achieving a high return portfolio with the least amount of risk,it is necessary to pay more attention to these markets In this study,in order to maximize returns and minimize investment risk,an attempt was made to create an optimal portfolio in conditions where the capital market has a price bubble.According to the purpose,the research is of applied type,and in terms of data,quantitative and post-event,and in terms of analysis,is descriptive-correlation.In order to identify bubble months in the period from2015to2019in Tehran Stock Exchange,sequence tests and skewness and kurtosis tests were used and after identifying bubble periods,artificial bee colony algorithm was used to optimize the portfolio.The results indicate the identification of 10 periods with a price bubble in the study period.Also,in portfolio optimization, selected stock portfolios are formed with maximum returns and minimum risk.This research will be a guide for investors in identifying bubble courses and how to form an optimal portfolio in these conditions.

Applying tax minimization policies can increase uncertainty about future tax payments due to uncertainty in the tax situation and thus increase the company's risk. However, if a company maintains a consistently low effective tax rate, it is unlikely that this corporate tax policy will increase the company's risk; But if tax minimization leads to instability in the tax situation, it will increase the company's risk. Low tax rates can also increase uncertainty about future cash flows. In this study, the relationship between tax avoidance and corporate risk (future fluctuations in tax rates and future fluctuations in stock returns) is investigated. The research is applied in terms of purpose and using a post-event approach and is correlational based on the method. Two hypotheses have been developed based on theoretical foundations in the direction of research objectives and by selecting 148 companies in the years 2014 to 2020 (1036 firm-year) these hypotheses have been tested. The results of testing the hypotheses show that there is a direct relationship between tax avoidance and tax rate fluctuations and the relationship between tax avoidance and future fluctuations in stock returns is not significant. Based on these results, it can be argued that tax avoidance carries tax risk, but due to the lack of specialization of the Iranian capital market, it cannot be considered effective in stock returns fluctuations.

The purpose of this study was to investigate the impact of corporate social responsibilities on the relationship between capital structure and financial performance of companies listed in the Tehran Stock Exchange. This research period is from 2011 to 2014. The results showed that there is a significant negative relationship between short-term debt ratio, long-term debt ratio and total debt ratio with equity rate of return, asset rate of return and net profit margin. We also found that corporate social responsibility has a significat impact on the relationship between short-term debt ratios and financial performance. Corporate social responsibility has also influenced the relationship between long-term (total) debt ratios and financial performance.

The present research is the first research on Fama and French Factor model analysis using wavelet analysis approach in Tehran Stock Exchange. However, in the case of the three-factor model Rostami et al. (1396) in a research entitled "Multi-scale pricing model with wavelet analysis approach and three Fama-French factors and non-liquidity in Tehran Stock Exchange", a three-factor Fama and French model with a three-factor approach Wavelet decomposition. The relationship between stock returns with beta, book value to market value and firm size in the medium term are significant. The relationship between stock returns and size in the short run is significant. The regression analysis of the time scale of the Fama and French model of the five-factor model using the wavelet analysis in MATLAB software shows that in the short run (2-4 months), variables of size, risk and profitability have a positive and significant effect on returns. . In the medium term (4 to 8 quarterly periods), size, risk, value, and long-term (8 to 16 quarterly periods), risk and investment have a positive and significant effect on returns. Wavelet analysis and Famafrnch model analysis at different time scales show that investors in different time horizons should consider different factors in shaping their expectations of a portfolio. The proposed methodology suggests that investors choose investment opportunities with dynamic portfolio management strategies and take multi-dimensional risk and returns.

Investor sentiment about the capital market can play an important role in stock price trends, market transactions, and especially on the stock return synchronicity. The entry of many Individual investors who do not have enough information about investment, the study of this issue has made more important. To this end, in this research we investigate the relationship between investor sentiment and stock return synchronicity in Tehran Stock Exchange, by using the financial data of 167 firms listed on Tehran Stock Exchange from 15 various industries during ten years from March 2010 to March 2020. We use the Baker-Wurgler (2006) sentiment index as our primary measure of investor sentiment that Measured by principal component analysis. Also, three different methods have been used to measure the stock return synchronicity: the Mork model, the Carhart four-factor model and the Fama and French five-factor model. The results show that the Investors sentiment significantly affect on increasing stock return synchronicity. The findings also showed that the coefficients of positive and negative sentiment are not significantly different and as a result, positive and negative sentiment are symmetrically affecting the increase in stock return synchronicity.

This paper examines empirically the presentation adjust Three-Factor model explaining stock returns. According to the comparison of the tested models, the three factors of value, size and market-adjusted in each of the tested models are significant. The difference between the two models tested in the calculations of the market factor have been calculated. Based on the theoretical background considering the market leverage is considered in the model. Based on the results of the present study, the power of explaining the proposed model is improved in comparing to the original model.

In this study the effect of fundamental variables and size on stock return is considered. The aim of the study is to answer two questions about each variable. The first question is how the effects of variables on the return have changed through time. In order to answer this question, the effect of each variable is considered in three consequential time window: 2004-08, 2009-13, and 2014-18. The second question in on the effects of variables in 2018. The return of the market in this year significantly deviated from historical average and it could be marked as an anomaly. The study aims to investigate how this irregularity affects the association between return and variables. In order to answer to this question, the effect of each variable is considered in two time window: 2004-17, 2004-18. The data in this study consists of price and financial statements listed companies in Tehran Stock exchange from 2004 to 2018 which are analyzed by methods used in Fama and French (1993) and Assadi and Eslami (1393). The results suggest that the effect of some variables has changed through time. The results also have shown that market irregularity in 2018 significantly changed the effect of some variables on stock return.

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.

Although econometric models are appropriate for describing and evaluating the relationships between variables and statistical inference, but they have some limitations for financial analysis. Many efforts have been made to model nonlinear relationships in financial data using machine learning technologies. The purpose of this study is to apply machine learning models to form optimal stock portfolios and compare their performance. The statistical sample of the present study consists of 156 companies listed in Tehran Stock Exchange during the period 2009-2018. After data collection, the intended deep learning models in Anaconda software and Python programming language were tested, and then the ability of each model was determined by return evaluation, composite return, trenors and jensens criteria to form an optimal stock portfolio. According to the free-risk and market return rate, forming portfolio by investor to more profit than these two rates and portfolio valuation results of trenors and jensens indexes, it was concluded that the deep Convolutional Neural Network is able to for optimal portfolio. According to this reasoning, the long short-term memory model is not capable of optimal portfolio formation.

Noise traders have an undeniable role in determining market volatility, returns and stock price movements. Therefore, In this paper, the effect of noise traders on the stock returns of companies with the aim of presenting an appropriate picture of how they are affected in extreme situations.The statistical population of this study includes all companies listed in Tehran Stock Exchange during the period of 2009-2017. The sample of 13717 data from 150 companies listed on the stock exchange monthly. The main hypothesis of this study is to evaluate the Extreme Effects of noise trading on stock returns by quantile regression was used to analyze the data. The findings of the research show that the level of noise activity increases with the level of efficiency Moreover,the positive effect of the noise trading index on returns with a coefficient of 0.0001.Under extreme returns, this effect is greater than the intermediate values and reflects the intensification of noise trading activity in periods of decline and market growth.

We evaluate risk and return of bitcoin in comparison with other competitive markets such as currency (Dollar and Euro), stock exchange, and gold (futures contract and cash coin). We gather different data relevant to bitcoin prices, Tehran stock exchange index, Dollar and Euro currency rates, gold futures, and gold coins during 5 years (9/19/2013 until 9/18/2018). We apply Dickey-Fuller unit root test, GJR-GARCH(1,1), TGARCH, and Spearman correlation coefficient to examine the research hypotheses. Findings show that the risk and return of bitcoin is significantly more than other investment opportunities like currency, gold, and stock exchange in the country, however, there is no relation between its risk/return and other competitive markets. Unlike other assets, good news effect on bitcoin trading market is greater than bad news effect. Finally, Dyhrberg’s (2016) hypothesis is unacceptable which implies to bitcoin is a stuff between gold and currency.

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.

The purpose of this research is to investigate the relationship between investor sentiment, courage in predicting dividends and future performance of the company in Tehran Stock Exchange. The research is applied from the direction of the target, and, depending on the type of research project, relying on historical information, then the event. The method of research inference is inductive and correlation type and consists of 3 hypotheses. The research community is the accepted companies in Tehran Stock Exchange for a period of 5 years. To document the results of statistical analysis and provide final solutions, the researcher used a statistical method using Eviews software to analyze the questions and hypotheses. The research was conducted using regression Compound linear and F and t tests. The results of the test of research hypotheses showed that investor's feelings on courage affect the prediction of dividends and company growth, but not affect future returns.

The purpose of this study is to investigate the relationship between free float stock and return, liquidity and the value of listed companies in Tehran stock exchange. For analyses data collected from 134 listed companies over seven years of 2010 to 2016 has been used in the study. To analyze the data and testing the hypotheses, the study has employed multiple regression models with several variables using panel data structure. The results show that based on testing the first hypothesis, the relationship between free float and stock return is significantly positive. Moreover, based on the second hypothesis, whereas the effect of current ratio is insignificant but the significance of the total model is accepted and the relationship between free float and stock liquidity is significantly positive. In the third hypothesis the total regression model is also significant, so there is a positive and significant relationship between free float with companies value.

Capital markets’ return is influenced by multiple and various factors. These factors range from macro global factors to dependent variable historical behaviors. Each of many researchers has selected a segment or segments of this vast range of factors impacting capital market return in different countries and embarked on devising a model for projection of the respective capital market return. The present research has utilized self-explanatory and combined models for the purpose of modeling and projecting Tehran Exchange’s return using Autoregressive Moving Average (ARMA) and Autoregressive Moving Average with External Inputs (ARMAX) models. In this research, to utterly expound the model and to factor in maximum factors, following examination of return issues and the factors impacting return, the subject of projection and the common methodology thereof have been surveyed and different models of projecting capital market return have been examined in detail. Then, Classic Linear Regression Models, Autoregressive Moving Average (ARMA) and Autoregressive Moving Average with External Inputs (ARMAX) models were used to predict the return of Tehran Exchange. After estimating the above models, deploying 99-period data and confirming the power of expressing them via applying diagnostic tests, the return of Tehran Exchange for the next 4 periods was projected. These predictions by applying estimation models were compared with the real data and the optimum model was selected using Akaike Information Criterion (AIC), Schwartz-Bayesian Information Criterion, Hannan-Quinn and also MSE, MAE, MAPE criteria. The final result indicates superiority of ARMA over ARMAX

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.