درخت حوزه‌های تخصصی

This paper aims to investigate the portfolio optimization under various market risk conditions using copula dependence and extreme value approaches. According to the modern portfolio theory, diversifying investments in assets that are less correlated with one another allows investors to assume less risk. In many models, asset returns are assumed to follow a normal distribution. Consequently, the linear correlation coefficient explains the dependence between financial assets, and the Markowitz mean-variance optimization model is used to calculate efficient asset portfolios. In this regard, monthly data-driven information on the top 30 companies from 2011 to 2021 was the subject to consideration. In addition, extreme value theory was utilized to model the asset return distribution. Using Gumbel’s copula model, the dependence structure of returns has been analyzed. Distribution tails were modeled utilizing extreme value theory. If the weights of the investment portfolio are allocated according to Gumbel’s copula model, a risk of 2.8% should be considered to obtain a return of 3.2%, according to the obtained results.

It is well documented that using linear models to forecast plenty of financial observations due to their nonlinearity is not satisfactory. Therefore, in this paper, the technical analysis indicators are forecasted using Neural Network Auto-Regressive model with eXogenous inputs (NNARX). Then the effect of different factors (economic, systematic risk, company's properties and corporate governance) on their forecasting error (eRSI, eMA1, eMA2 and eMACD) was investigated. For this purpose, required data were collected using the removal sampling method for 323 companies listed on the Tehran Stock Exchange from 2014-2020. In addition, the mean absolute percentage error (MAPE) was applied to measure the error of forecasting technical analysis indicators. NNARX and dynamic panel data models (GMM) were used to study the effective factors on the error of forecasting technical analysis indicators. Results indicated that the error of forecasting technical analysis indicators is less than 0.1 and has sound accuracy. Also, the company's size and corporate governance indicators didn't significantly affect the error of forecasting technical analysis indicators. In addition, financial leverage doesn't significantly affect eRSI and eMACD but has a significant inverse effect on eMA1 and eMA2. On the other hand, return on assets has a significant inverse effect on eRSI, eMA1, eMA2 and eMACD. Also, economic recession and prosperity, inflation fluctuations, exchange rate fluctuations and systemic risk have a significant positive effect on eRSI, eMA1, eMA2 and eMACD.

The most important policymaker entity in the bank-centered economy of Iran is central bank of Islamic Republic of Iran which has always been trying to manage and improve macroeconomic indicators by applying monetary policies. However, investigation of Iran's economy after four decades shows that this country has always suffered from double-digit inflation rates and 15000-fold liquidity throughout this period, while GDP of Iran has grown only by two times during this period. This paper tries to evaluate the effect of monetary policies of central bank on macroeconomic indicators by analytical-descriptive and library method via combining system dynamics and fuzzy multi-criteria decision-making using Vensim and Super Decision software. Monetary policy instruments in this re-search include foreign exchange rate, deposits interest rate, facilities interest rate, required reserve ratio, and open market operations. Further, the macroeconomic indicators include inflation, liquidity, national foreign exchange value, and eco-nomic growth. The results indicated that the most important macroeconomic indi-cators in the country according to economic experts are "national foreign ex-change value" and "inflation". The most important tool for monetary policies of central bank is "foreign exchange rate". Indeed, in order to improve the economic misery index, this bank should take measures to improve the national foreign exchange value, then manage inflation and liquidity, and eventually adjust the banking interest rate

This paper primarily aims to introduce a model to enhance the performance of the stock market portfolio of the Iran Social Security Organization. Performance indices were measured using documentary-based research and expert interviews based on theoretical saturation to evaluate the stock portfolio performance of the Iran Social Security Organization relative to companies listed on the Tehran Stock Exchange (T.S.E.) during 2016-2020. The Delphi technique was employed to ensure the validity of the indices. Drawing on the analytical hierarchy process (A.H.P.), the indices were prioritized and ranked based on the weight vector. The Iran Social Security Organization was found to have poor stock portfolio performance based on risk, return, liquidity, Sharpe ratio, and T.O.P.S.I.S. Hence, several solutions were identified based on expert interviews through thematic analysis to improve the stock portfolio performance of the Iran Social Security Organization. The solutions were validated through the Delphi technique, prioritizing and quantifying the performance improvement indices using the A.H.P. Based on expert views and T.O.P.S.I.S., a stock portfolio performance improvement model was proposed for the Iran Social Security Organization. Handling the non-profitable, low-return, and out-of-strategy companies is the optimal solution for the portfolio performance improvement of the I.S.S.O., with portfolio modification based on liquidity, effective stock market-making, return-based stock risk management, synergy between the portfolio and value chain completion, and value-added creation approach to stock management and exchange having the second-fifth ranks, respectively.