رضا راعی

Investors are willing to invest in the capital market which would earn a proper profit and would make the possibility of an accurate forecast of future trends and prices. In this regard, deep learning (DL) networks have been able to help in predicting the capital market movement. Due to the high capacity of DL approaches in many fields due to their strong capacity, they have been widely used in financial issues such as stock market movement prediction, portfolio optimization, financial information processing, etc. Recently, generative adversarial networks (GANs) illustrated suitable results intending to analyze and predict time series data. Therefore, in this study, the GAN consisting of a convolutional neural network as a generator and long short-term memory in the adversarial network is proposed for stock price prediction. In addition, in order to increase the accuracy of the network, other DL approaches have been used in network training. The results of the daily data of the Tehran Stock Exchange between 1394 to 1398 demonstrate that the prediction accuracy of the GAN network using the most appropriate features is up to 10%. The experimental results of this model show that the GAN network with the mentioned architecture can have a promising performance in stock price prediction compared to other DL models.

According to the 22 selected features (which are checked during the research) with machine learning methods, this study predicts the cash holding of companies admitted to the Tehran Stock Exchange. 201 companies were investigated from 1396 to 1400. Multiple linear regression, K-nearest neighbor, support vector regression, decision tree, random forest, extreme gradient boosting algorithm and multilayer neural networks are used for prediction. The results show that the multiple linear regression methods provide the k-nearest neighbor of the root mean square error (RMSE) and the mean absolute error (MAE) of the high error. Meanwhile, more complex algorithms, especially support vector regression, achieve higher accuracy; The findings indicated that by reducing to 15 variables, machine learning methods, especially K-nearest neighbor, provided better results. Based on the paired sample t-test, support vector regression has a better performance than other supervised machine learning algorithms except decision tree. Also, the most important variables were company size and capital expenditures (CapEx). The World Uncertainty Index and inflation were also relatively important variables; Therefore, by using the support vector regression algorithm, we may predict the amount of cash to a significant extent.

This study investigates the existence and intensity of the Disposition Effect among individual investors. The Disposition Effect refers to investors’ tendency to sell assets with positive returns while holding onto those with negative returns. This behavioral bias can lead to asset mispricing and reduced market efficiency as decisions are influenced by cognitive errors, psychological factors, and emotional responses. The research focuses on whether the timing of market entry impacts the strength of the Disposition Effect, particularly as market entry is influenced by broader economic and psychological conditions. Understanding these effects can enhance financial decision-making and investment strategies, thereby contributing to improved market performance and stability across diverse conditions.
The statistical population includes all individual shareholders in the Iranian capital market whose trading data were recorded from early July to early December 2022. To achieve the research objectives, trading data were collected, including transaction dates, directions, prices, and volumes, alongside personal information such as age, gender, trading history, and account balances. These data were analyzed to assess the presence and intensity of the Disposition Effect. Investor market entry data were categorized into sub-samples based on monthly market returns, volatility, and economic instability indicators. Statistical methods, including survival analysis and the Kaplan-Meier model, were employed. Survival analysis assessed how long investors remain influenced by the Disposition Effect under varying market conditions, while the Kaplan-Meier model analyzed the distribution of the Disposition Effect’s duration and examined how both market entry timing and prevailing market conditions influence its intensity.
The findings indicate that the Disposition Effect is particularly pronounced among new investors, especially during unfavorable market conditions. These conditions include periods with lower overall market returns, higher market volatility, and heightened economic uncertainty. New investors exhibited a stronger tendency to hold onto poorly performing stocks and sell stocks with positive returns, reflecting the psychological and cognitive challenges they face during the initial stages of their market involvement. In contrast, investors with greater trading experience demonstrated a reduced tendency toward the Disposition Effect, highlighting the mitigating influence of experience over time. These results underscore the significant role of market conditions and entry timing in shaping investment behavior and behavioral biases across varying investor profiles.
The results emphasize the importance of understanding the Disposition Effect and its relationship with market entry timing. New investors, particularly those entering the market during adverse conditions, are more prone to exhibiting the Disposition Effect, often making suboptimal decisions. These insights can guide financial managers and investment advisors in developing tailored strategies, such as investor education and advisory services, to address behavioral biases and enhance decision-making processes. Additionally, the findings can assist policymakers in designing targeted interventions to mitigate the negative effects of behavioral biases, thereby fostering a more efficient and resilient market environment. Improved awareness and better management of the Disposition Effect can contribute to better investment practices, reduce risks, and enhance overall market stability.

The alpha of the mutual fund, which is a measure of its performance, is not only the result of the selection of securities, but also has a direct relationship with the weights assigned to each of the securities in the fund's portfolio. In this study, the alpha of the mutual fund, which is a symbol of the skill of its managers, is divided into two components: the alpha caused by stock selection and the alpha caused by stock weighting. In order to measure the aforementioned skills, 60 mutual funds in stocks were considered as a research sample and the skills of stock selection and stock weighting were examined during the period of April 2021 to September 2022. According to the results, the total alpha of 0.0025 and the alpha of stock selecting, 0.0149, are significant and positive at the five percent level. Therefore the weighting alpha can be considered as the difference between the total alpha and the selecting alpha, it can be claimed that the weighting alpha is negative and the fund managers of the country lack the skill of weighting stocks.

Bank competition and financial stability is one of recent topics which researchers have paid attention since global financial crisis. This study is investigating the effect of competition on systemic stability in Iran’s banking system.Statistical population is all Iranian banks under supervision of centeral bank and our sample consist of 20 banks in period of 2009 and 2021. Lerner index and Herfindahl–Hirschman index are proxies of market power and concentration respectively. ΔCoVaR is our systemic risk measure which can be decomposed into its components.Lerner index had a statistically significant positive relationship with ΔCoVaR while HHI didn’t. Market power haven’t shown any effect on idiosyncratic tail risk and the risk caused by macroeconomic and financial factors, as well as the combination of the two, and in other words, it doesn’t affect the overall systemic risk through this channel. However, the HHI has had a positive and significant effect. Also, market power has a positive and significant relationship with the interconnectedness component, while the concentration index has not shown a significant relationship.This study has proven that the more competitive system, the more stable. Meanwhile, market power affects systemic risk through interconnectedness, but idiosyncratic risk is the effective channel for concentration.

The purpose of this article is to investigate the impact of financial distress risk on the fall in the stock prices of companies listed on the Tehran Stock Exchange. In this research, statistical data of 195 companies admitted to the Tehran Stock Exchange during the years 2015-2019 have been used, and multiple regression using the panel data method has been used to analyze the data. To measure the variable of financial distress risk, Merton's distance to default index and to measure the fall of stock prices, four methods of risk period of stock price fall, negative skewness of stock returns, maximum sigma and low-to-high volatility have been used, as well as asset return ratio variables. leverage ratio, ratio of market value to book value and company size have been used as control variables. The results of the research show that there is a positive and significant relationship between financial distress risk and falling stock prices.

In this study, systemic risk index (β) was measured and decomposed by using the extreme value theory in the banks listed in Tehran Stock Exchange (TSE) during 2013-2021. This index was divided into two dimensions: total bank risk (tail risk) and link of the bank to the system during the financial crisis (system linkage). Using autoregressive distributed lag (ARDL) regression, the relationships between systemic risk and its dimensions with the characteristics of the banks and the relationships between their economic value-added (EVA) and various financial risks, including systemic risk, were calculated. According to the results, Post-Bank, Tejarat, and Saderat banks imposed the highest systemic risks and Karafarin and Eghtesad-e Novin banks imposed the lowest systemic risks, respectively. The results showed that with increasing the bank size, the systemic and tail risks that are specific to each bank decreased and the systemic linkage increased. There was a positive and significant relationship between the EVAs of the banks and their ratios of regulatory capital to credit risk-weighted assets. Also, there was a negative and significant relationship between the EVAs of the banks and their ratios of core capital to operational risk-weighted assets, net sustainable financial resources, and interest rate risk variables.

Given the importance of estimating stock returns, the purpose of this study was to identify the factors affecting stock returns by using the meta-analysis method. Meta-analysis is the main tool for combining results in social and behavioral research. It allows researchers to combine quantitative results of studies, explain the compatibility of results, and achieve a single result. Based on the research background, the impacts of different factors on stock returns were considered. A model of factors affecting stock return is designed in the analysis of empirical studies. This research used the meta-analysis method to comprehensively examine the factors affecting stock returns and assess the different factors that had been examined in various studies over the past years. In a similar research, the researcher had selected the factors affecting stock returns and only tested the selected factors via general categories. However, in this research, a more comprehensive study was conducted to separately examine all the factors whose effects on stock returns had been tested in different studies. Therefore, some of the factors investigated in this research had not been studied in the previous research. Accordingly, a model of factors affecting stock returns was developed. In this research, 422 studies were collected and 102 of them were analyzed. Totally, 153 factors were extracted from these studies and finally, 16 factors were tested. The type of effect size calculated in this study was r. Among the examined factors, earnings per share, operating cash flow, net operating assets, operating earnings, market return, earnings quality, and return on equity had an impact on stock returns. Since the majority of the factors affecting stock returns were related to the financial reports of companies, i.e., their financial status and performance, those factors could be considered to investigate investment opportunities.

In this paper, the effect of banks network topology on banks systemic risk in Tehran Stock Exchange have been investigated by using banks daily stock return from 2013 to 2021. First, systemic risk index have been measured and decomposed by using EVT approach and then this index divided into two dimensions, bank tail risk and systemic linkage. Then the network between banks listed in the banking industry of Tehran Stock Exchange have been created based on dynamic conditional correlations (DCC) by using the spanning minimum tree (MST) approach and the banks network topology have been measured. Finally, the relationship between banks systemic risk and its dimensions with banks network topology have been investigated by using regression of panel data. According to the results, Post-Bank, Tejarat and Saderat banks have the highest and Karafarin and Eghtesad-Novin banks have the lowest systemic risk, respectively. Also, the regression results showed that there is a positive and significant relationship between the variables of node strength, betweenness centrality and size with the banks systemic risk and a negative and significant relationship between the variables of node degree and liquidity with banks systemic risk.

If the central bank and the relevant bank risk committee do not monitor and control the amount of liquidity created by a bank, it risks bankruptcy and ultimately all the banks in the country. Therefore, this study examines the impact of liquidity creation on the profitability and financial stability of banks. In terms of purpose and method, this research is applied post-event. A multivariate regression model and data panel approach were used to analyze the information of 15 banks listed on the Tehran Stock Exchange over the period 2013-2020. Berger and Bauman's (2009) method was used to calculate the liquidity creation index and the profitability and financial stability of banks were measured by the return on assets ratio (ROA) and Z-Score, respectively, as well as by control variables at the bank level (size, deposits, facilities, and non-interest income) and industry level (concentration index of banks). According to the results, banks are more profitable and stable when liquidity is created.

In this study, we investigated the existence of the bank lending channel (BLC) as one of the monetary transmission mechanisms and the effect of banking characteristics on this channel in the Iranian economy.

The Factor-Augmented Vector Autoregressive (FAVAR) model, introduced by Bernanke, Boivin, and Eliasz (2005) was used. This research studied 61 macroeconomic variables from 2004Q2 to 2020Q1 and 24 banking variables from 2009Q1 to 2020Q1.

The investigation delivered two main results. First, by considering the growth of M2 as a proxy of monetary policy, the monetary policy proved to have a significant effect on bank lending and the BLC in the Iranian economy. When identifying monetary policy shock, the response of real lending growth was found positive and significant in the quarter when the shock was identified and also in the following three quarters. Substituting nominal lending growth for real lending growth, lending growth had a more significant response to the monetary policy shock. Also, by considering the monetary base as a proxy of monetary policy, lending growth had a less significant response to the monetary policy shock. Second, we found that bank characteristics don’t have a significant effect on the BLC. We investigated the effect of bank characteristics on the BLC at both aggregated and disaggregated lending. The results of the analysis of aggregated lending response showed that by including the bank factors, compared with the case where there are only economic factors in the model, the aggregated lending response doesn’t change significantly after considering the bank factors. Therefore, the bank characteristics do not significantly impact the response of the aggregated lending growth to the monetary policy shock. The results of the analysis of disaggregated lending response showed that except for Parsian and Pasargad banks, whose lending response to monetary policy shock is positive and insignificant, other banks give positive and significant responses to monetary policy shock. Overall, the bank characteristics have a more significant effect on the BLC in the disaggregated lending case.

According to the achieved results, the BLC can be considered an active channel in the Iranian economy, by which the real economy can be affected. Also, the bank characteristics don’t have a significant effect on the BLC. Therefore, considering the strength of the BLC in the Iranian economy, the very close relationship between the BLC and monetary policy variable (M2), and regarding the insignificant effect of bank characteristics on the BLC, monetary policy-maker should take into account the BLC when setting monetary policy.

In recent years, deep learning neural networks have been recognized as powerful tools for solving complex problems. Deep learning is a subfield of artificial intelligence in which complex problems with numerous parameters and inputs are modeled based on a set of algorithms. In this research, a new framework of deep learning is presented. Using wavelet transform, stacked auto-encoders, and the Long Short-Term Memory or LSTM, we predict the market direction in the future contracts of gold coins of Iran's Commodity Exchange market. The input data is first denoised using the wavelet transformer in the proposed method. Then, using the stacked auto-encoder, the indicators influencing the market direction are identified. Ultimately, these indicators are given as input to the LSTM architecture to predict the market direction. Proposing several new technical indicators to increase the accuracy of the proposed model, adjusting the parameters of the utilized algorithms, including LSTM, for this problem, and suggesting a trading strategy to achieve appropriate profitability are among the contributions of the present study. Investigations reveal that the proposed method outperforms other approaches and achieves higher accuracy and efficiency.

The purpose of this study was to apply the relative robust approach that minimizes the maximum regret to deal with the present uncertainty in the input data of the Markowitz mean-variance portfolio optimization model by reconstructing that model as a second-order conic program. Regret is defined as the difference between the obtained solution and the optimal solution under a specified input data set. This approach uses scenarios to consider the present uncertainty in the input data. Moreover, the robust portfolio optimization model introduced by Bertsimas and Sim, which considers uncertainty as an interval, was used to be compared with the relative robust approach and the Markowitz model.

In this research, the return of 50 more active stocks of the Tehran Stock Exchange (TSE) was used to obtain the optimal portfolio using the minimax regret method based on the Markowitz Model. Then, using the out-of-sample Sharpe criterion, the results of the minimax regret method were compared with the classic methods.

Based on the research findings, the relative robust approach in the out-of-sample test on most corresponding points of the efficient frontier showed better performance in comparison with the Markowitz model. Also, the Bertsimas and sim approach delivered better performance than the Markowitz model in the out-of-sample test. The results did not prove any significant difference for out-of-sample outputs between the relative robust and Bertsimas and sim approaches.

According to the obtained results, the relative robust approach can surpass the mean-variance approach for investors with almost all levels of risk-return preferences. The approach presented in this research can provide investors with a new risk criterion that can be considered in choosing the optimal portfolio. Furthermore, the results confirmed that investors act indifferently in choosing between the relative robust solution and the solution of Bertsimas and Sim's approach. This method can be applied as a portfolio optimization approach and also different markets can be considered under this technique to have a better understanding of its capability.

In this research, the effect of scale-time volatility of assets (currency, stocks and housing) on the efficiency of the banking network in the period 1399: 4-1388: 1 has been studied quarterly using the Markov switching model.

In this study, we first calculate the efficiency of the bank network using the data envelopment analysis model with bootstrap data. Then, the volatility of asset market (exchange rate, stock market index and housing price index) extracted using the wavelet conversion pattern and examines the impact of volatility of asset market on the efficiency of the country's banking network in the form of the Markov switching model and observing their effect on different levels of efficiency.

The average efficiency of the country's banking network in the study period has been about 56.1%, which indicates that efficiency has not been appropriate. The short-term volatility of the exchange rate in the state that the  efficiency of the bank network and the high regime has a negative and significant effect, but if the long-term exchange  volatility, regardless of the regime and the level of banking network  efficiency, has a negative and significant effect. The short-term volatility of the stock market index have had a positive and significant effect on the level of low banking network efficiency. But if volatility are continued in the stock market, regardless of the level and regime, the efficiency of the banking network has a negative and significant effect. The short-term  volatility in the housing market have had a positive and significant effect on the level of bank network efficiency but in the opposite side of the long-term volatility in this market and in a high level of bank network  efficiency, it can lead to significant reductions. Therefore, by stabilizing the economy (lack of large exchange rate, stock index and housing), it can be expected to improve the efficiency of the country's banking network due to its level and regime.

One of the issues that can be important in policy making perspective is to consider the impact of volatility of assets market in different time periods on different levels of banking network efficiency. Because they may have a different impact on different levels of bank network efficiency as well as different periods of volatility of assets market.

Enhanced index Tracker portfolio, as one of the investment management strategies, is trying to combine the benefits of both active and passive approaches. This research is going to provide a two-stage model that can first reproduce the index performance with a smaller number of index-forming shares and, secondly, calculate the Enhanced index tracker portfolio weights.

In the first step, using a binary mathematical programming model to create clustering of time series, an index tracker portfolio was created. Coppola-based correlation coefficients and mutual information were used as time series similarity measures at this stage. In the second stage, the weight of investment in the selected shares was determined in a way that the return on the portfolio surplus was maximized relative to the index created in the first stage. The uncertainty resulting from the estimation of the excess stock returns in the second phase was considered by using a robust optimization approach.

The results obtained by applying the out of sample test on the 50 more active companies in the Tehran Stock Exchange from the spring of the Iranian calendar year of 1394 to spring of 139, using the tracking error and market ratio, indicate that in addition to the success of the similarity criteria in time series clustering and index tracking, at a confidence level of 99%; Enhanced index Tracker portfolios based on normal, T and Clayton Copula correlation coefficients have a positive significant difference with the index.

According to this study, to develop an enhanced index tracker portfolio, it is practical to apply copula-based correlation coefficients and try a robust optimization approach to take into account the uncertainty of the parameters.

Real estate investment are characterized by low liquidity and irreversible cost. Real Estate industry is one of the most important industry in occupation. Moreover, its production has the biggest weight in family portfolio.These are important characteristics of real estate markets. Real estate industry has cyclical trend. In recession, investor delay their investment. Traditional capital Budgeting models such as Net Present Value are base on fix assumption and condition. They ignore management flexibility. In this paper residential Projects are evaluated with real options (option to delay) by Black-Scholes and Binomial Lattice Model.These model values managerial flexibility. Experimental results show that project Evaluation with real options outperforms the traditional models such as NPV. This paper studies the optimal timing of investment in an irreversible project where the benefits from the project and the investment cost follow continuous- time stochastic processes. According to optimal investment timing proposed by MC Donlad and Siegel, time to start this Project is 9/5 years from now.

Modeling the joint distribution of liquidity and return to determine the dependency structure of a 15-stock portfolio using intraday data in 2019 provides a suitable model for the commonalities. Based on the complexities of higher dimensions multivariate modeling (combined distribution of liquidity and return), after univariate modeling of the stocks’ liquidity based on Autoregressive Conditional Poisson model (ACP) and the returns with the Generalized Autoregressive Conditional Heteroscedastic (GARCH), the marginal distributions were incorporated into a Copula Vine to model the dependency. The findings of this study, based on high-dimensional high-frequency data, indicate that there is an extreme nonlinear correlation between the liquidity of stocks and also between liquidity and returns across the portfolio, which is necessary to take into account in risk assessments to prevent inaccurate assessment of risk indicators such as value at risk. In addition, the results have shown that modeling the joint distribution of liquidity and stock returns in high dimensions relying on the Copula Vine model due to the flexibility performs well.

One of the most important methods for market risk measurement is Value-at-risk (VaR) that financial institutions such as banks, insurers and investment funds use them extensively. VaR as a risk measure is heavily criticized for not being sub-additive, thus the researchers focuses on the assessment of the Tail value-at-risk (TVaR), and this measure is using on the Basel Committee on Banking and Solvency II of Europe and Swiss Solvency Test (SST). this paper focuses on TVaR to measure the risk of the stock market. Considering that the time horizon of the risks of an insurer unlike banks is annually. thus, to calculate the TVaR, we use of the two methods of the variance-covariance approach with the EGARCH-Extreme learning Machine model to volatility forecasting and use of square-root-of-time rule; and Filtered Historical simulation model. The results of using the daily returns of the Tehran Stock Exchange Index for 1388 to 1396 confirm that the EGARCH-Extreme learning Machine model with use of square-root-of-time rule performs better in TVaR calculation in terms of efficiency and accuracy.

Since Markowitz's (1952) pioneering work on a single-period investment model, mean-variance portfolio optimization problem has become a cornerstone of investment management in both academic and industrial fields. Despite the presence of various theories and methods, the  model continues to be considered in the portfolio selection, because it doesn`t need to estimate other parameters in optimization and computes simply. The objective of this study was to investigate the performance of Model  in the portfolio.

In this paper, various models and methods have been used to select the optimal portfolios and to evaluate the performance of the portfolio. At the end of the paper, the ELECTRE multi-criteria decision-making method has been used to rank the portfolio selection models. Portfolio selection models in this paper include  model, mean-variance model, minimum-variance model and composition of the minimum-variance model and  model. In this paper, various criteria such as Sharpe ratio, Trainer ratio, Modigliani and Modigliani ratio, Sortino ratio, Information ratio have been used to measure portfolio performance.

Relatively, the performance of the  model was better in terms of Sharpe ratio and Modigliani and Modigliani ratio, the performance of the mean-variance model in terms of Trainer ratio, and the performance of the composition of the minimum-variance model and  model in terms of Sortino ratio and Information ratio.

Finally, the ELECTRE multi-criteria decision-making method has been used to rank the portfolio selection models. The results indicate that  model and minimum-variance model is superior to other models.

The interbank market is a part of the money market in which banks and credit institutions enter into transactions to finance and consume surplus resources in the short term with the aim of balancing their liquidity situation. In a corridor framework of monetary policy, the central bank sets interbank money market (IBMM) interest rate and also implements open market operation (OMO) to direct market interest rate towards the policy rate. Hence estimation of IBMM equilibrium interest rate is the most important challenge in monetary policy making. In order to model the interbank market in Iran, this research provides a 4-periods search model, including shocks, searching for counterparty, bargaining and settlement with central bank, and it simulates Iranian banks’ behavior to derive Iran’s interbank rate using 2017-2019 period data.The results show that the model-driven lending rate of large banks is higher than their borrowing rate which is consistent with rational behavior of the banks, while the actual rates are the same in both cases that implies distortion in interest rate policy making by Iran central bank.

Risk management is one of the most important areas of study in finance, and its vital role in the field has attracted the attention of managers and investors in in various sectors of the industry. Especially in recent years, with the onset of financial crises, the importance and necessity of accurate studies in this area has doubled. The main purpose of this study is to provide a model for a more accurate measurement of equity portfolio risk.

To conduct this research, adjusted closing prices of a sample of thirty listed companies have been used. CVaR is the main model and four other models are formulated using different methods of variance modeling. The first method calculates conditional value at risk using constant variance and in the other three methods, variance is modeled on GARCH, E-GARCH and T-GARCH approaches.

Ultimately, the results have been evaluated using appropriate statistical tests, namely paired t test and Wilcoxon signed rank test. The results obtained from both tests suggest that the method used to model variance has a significant effect on attaining a better optimal portfolio.

Considering results of the research, which approve the tested hypothesis, one can conclude, taking into account the heteroscedasticity in Iranian capital market, would result in a better optimized portfolio. Moreover, the results illustrated that the use of CVaR model, for risk measurement, rather than previously used traditional models, can be effective in improving the performance, in optimizing stock portfolios, significantly.

Stroke is the third most common cause of death in developed countries and the most common disabling neurological disease. There are several risk factors for stroke. Regarding this, the present study was conducted to investigate the effect of Helicobacter pylori antibody on the incidence of stroke.

This case-control study was conducted on 50 patients (i.e., case group) with stroke admitted to Sina Teaching Hospital of Hamadan, Iran, using convenience sampling method. This group was compared with 50 healthy controls in terms of the serum level of Helicobacter pylori antibody. The data were analyzed using SPSS software (version 21) using proper statistical tests at 95% confidence interval.

The mean serum levels of Helicobacter pylori antibody in the case and control groups were 25.93±44.03 and 35.36±41.25 Uarb/ml, respectively (P=0.108). In addition, the serum levels of this antibody were obtained as 20.78 and 35.84 (P=0.055) in the females and 31.51 and 34.31 (P=0.501) in the males of the control and case groups, respectively. The Spearman correlation coefficient revealed a positive but insignificant relationship between age and serum antibody level of Helicobacter pylori in both groups (r=0.150; P=0.13).

As the findings indicated, the stroke patients had a lower serum level of Helicobacter pylori antibody, compared to the healthy non-stroke patients; however, this difference was not statistically significant.

Investment management in life insurance is very important because of its specific features. Unlike other insurance policies, life insurance is long-term and focused mainly on return on investment. Also, due to the 85% participation of insurers in the surplus of interest, the surplus in a period has little income for the insurance company,but the insurance company has a guaranteed return on the deficit. In addition, high return is one of the main factors of insurance companies competition in marketing. Also, insurance companies need to make sure they have enough liquidity to fulfill their obligations. Despite the fundamental differences between life insurance and property and liability insurance, No 60 Central Insurance Regulations do not differentiate between the investment of the above insurance reserves. In this study, we designed a portfolio optimization model for life insurance investments. Accordingly, the Markov Switching Vector Auto Regressive based on ideal optimization with contrasting goals set with the actual performance of the company. Optimization has been made in the portfolio of life insurance based on Moalem Insurance Company from 2012 to 2016. Investment weights have been implemented by simulating the Markov Switching VAR model with optimization objectives including maximizing insurance returns, minimizing shortfall, maximizing policyholder returns and minimizing liquidity shortages. Based on the results, the ideal planning optimization model produces more returns than the actual returns of the insurance and performs better in other objectives. As a result, this approach can be used to optimize life insurance investment portfolios in insurance companies.

The occurrence of financial crises in recent decades has caused a lot of damage to the economy as well as economic enterprises in many countries. The Extreme Value Approach is a new approach to the phenomenon of financial crisis, which has been able to analyze the events that are less likely to occur but the damage caused by them is significant. In this study, we use the Extreme Value theory and Stochastic differential equations to find a new method for estimating the more precisely the value at risk. For this purpose, after estimating the parameters of the Stochastic differential equations, which includes the geometric Brownian motion, the geometric Brownian motion with the jump, the nonlinear GARCH model, and the Heston model, simulate the Monte Carlo simulations of future paths and then use peak over threshold approach, to estimate the value We at risk. The results of the simultaneous use of Stochastic differential equations and Extreme value theory ​​are compared with historical simulations and variance-covariance approaches for value at risk. The results of Back-test techniques on value at risk indicate the superiority of the Heston model in estimation of value at risk.

Active management is one of the issues that is important in terms of violating effectiveness of financial markets financial markets. Because inefficient market, there is a potential to generate abnormal returns through active portfolio management. In many studies in this regard the reason for the surplus return compared to the baseline portfolio by minimizing the tracking error variance (TEV) in this regard, the risk of the entire portfolio is not taken into account. In this study, by using the differential evolution algorithm (DE) to optimize the active portfolio, with the goal of maximizing portfolio surplus returns compared to the standard portfolio, considering the risk of the entire portfolio from the calculated conditional risk value criterion (CVaR) based on the GARCH approach is used. The results of the portfolio consist of 14 stocks with a positive average yield from the beginning of 2011 to the end of June of 2017 from the top 50 stock exchanges on a monthly shows that subject to risk portfolio based on CVaR, causes better performance in the active optimization of the portfolio, based on backtesting method.