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

The purpose of this research is to investigate the periodic relationship between risk and return using the two-variable model approach. The current research is of a descriptive type and in terms of purpose, it is in the correlation-regression research category. EViews9 and Excel software are used to analyze data and extract research results. The information related to the companies admitted to the stock exchange during a 5-year period have been analyzed to examine the relationship between the variables to test the research hypotheses. The research results showed that there is no significant relationship between the expected return based on BARR and conditional variance based on BARR. The significance level for all explanatory models is greater than 0.05. Therefore, it can be claimed that there is no significant relationship between the achieved return and the conditional variance based on the single-variable MAR model. Also, the durbin watson test confirmed the autocorrelation error of the model. Also, there is no significant relationship between the achieved return and the conditional variance based on the BMAR model. The significance level for all explanatory models is greater than 0.05. Therefore, it can be claimed that there is no significant relationship between the achieved return and the conditional variance based on the single-variable MAR model. Also, the durbin watson test confirmed the autocorrelation error of the model.

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.

Stock portfolio optimization through artificial intelligence algorithm: comparison of stock portfolio in terms of risk-taking and investor cautionAbstractThe process of selecting stock portfolios for investment is one of the issues that has been considered by many researchers. In decision making for investment, two factors are very important and are the basis of investment. These two factors are risk and return, and in this regard, investors are studied to select the best investment portfolio according to the amount of risk and return. The purpose of this research is to create an intelligent model for selecting the optimal stock portfolio using research algorithms. The proposed model examines the different types of investments that an investor can and is willing to consider in order to form his or her investment portfolio. For this purpose, the expected risk and return of companies listed on the Tehran Stock Exchange have been examined on a monthly basis. The statistical sample of the research includes the financial data of 237 Iranian stock exchange companies during the years 1390 to 1398. The results show that the LISFLA algorithm is able to select a portfolio using the Marquis model for risk-averse and risk-averse investors.Keywords: stock portfolio, risk, return, frog algorithm