Financial sanctions have economic consequences for the oil-dependent economies. We examined the impact of financial sanctions on exchange rate fluctuations and macroeconomic variables in Iran. To this end, we employed a new Keynesian DSGE model. The results indicated that with the shock in foreign exchange, production (Y) and imports initially decreased. Oil production has shown a positive reaction initially and a negative reaction in the medium term, and after 7 periods, the effect of the shock has disappeared. The capital stock (K) also decreased initially, and in two periods, it reacted positively. In the tenth period, its effect disappeared, and in the long term, it became partially negative, and its effect disappeared. The inflation rate has decreased initially, and its effect disappeared over time. Consumption decreased, and after five cycles, the reaction became positive and then disappeared. The interest rate increased initially and then decreased, and in the 10th period, the shock effect disappeared. The exchange rate initially decreased and then increased after one period.

Embedded options are virtually new instruments identical to options in many aspects except their non-tradable nature. Testing the efficiency of the Variance Gamma and Black-Scholes-Merton model on these instruments would provide a vision of transitioning from the classical model with its deficiency to more intricate models. Considering the complicated nature of the Variance Gamma stochastic process to price options, the Fast Fourier Transform (FFT) method is used in conjunction with the Nelder-Mead Simplex method to calibrate models. This research uses the Fast Fourier Transform (FFT) to price four embedded options with the ticker symbols Hefars912, Heghadir912, Heksho208, and Hetrol911 under the two models. The result approves that the Variance Gamma process is more efficient than the Black-Scholes-Merton model in pricing embedded options. Consequently, the variance gamma process would generate fewer errors in pricing those options that can be used in a practical sense.

Today, numerous studies have investigated the role and effects of intangible assets on companies' performance. The existence of extensive literature regarding the use of intangible assets as a competitive advantage, in addition to the importance of performance dimensions affected by these assets, is the focus of the attention of various researchers. Regarding examining the performance dimensions of banks, the CAMEL model can be a suitable basis for evaluating the effects of intellectual capital. Although there are many models for calculating intellectual capital, the added value of the intellectual coefficient is still one of the most common methods. Therefore, in the current research, the effects of intellectual capital (based on the calculation model of the coefficient of added value of intellectual capital) on the performance of banks (based on the CAMEL model) have been investigated. Also, the shape of the function (linear or nonlinear) has been investigated in this research. The results show that the value added of the intellectual capital coefficient in the ninth quantile has a significant relationship with the variable of capital adequacy. The form of the relationship is nonlinear and inverted U. This variable affects the quality of assets in the seventh to ninth quantiles, management in the eighth and ninth quantiles, income in the first and second and sixth to ninth quantiles, and liquidity in the first to fourth quantiles. The shape of the function is U, inverse U, and inverse U, respectively.

Neurotransmitters are the chemical messengers nerve cells use to transmit signals to target cells. Neurotransmitters affect the behavioral aspects of investors' performance. Therefore, this study investigated neurotransmitters' effect on individual investors' behavior by using structural equation modeling. To achieve this goal, exploratory and confirmatory factor analysis has been used. The study is applied research and utilizes a survey methodology. The required data were collected through the distribution of questionnaires among 315 individual investors. This study was done in the Tehran stock exchange's second and third seasons of 2023. According to the research, neurotransmitters through eight components of adrenaline or epinephrine, noradrenaline or norepinephrine, dopamine, serotonin, GABA (gamma-aminobutyric add), acetylcholine, glutamate, and endorphins affect the behavior of individual investors. Based on the second-order confirmatory factor analysis, the GABA component (0.39) has the highest impact, and adrenaline or epinephrine (0.25) has the least effect on the behavior of individual investors. The findings indicate the necessity of redefining rationality and considering its effects on investors' decisions and behavior.

The correct understanding of behavioral factors affecting individual investment decisions in the stock market is one of the main goals of this research. This accurate knowledge will increase the efficiency of the market, and the financial resources will be adequately equipped and allocated. Finally, it will save resources in this market. Therefore, the current research seeks to investigate and test the effect of risk aversion based on the past performance of stocks in the financial behavior of investors. In this research, a regression model was used to test the hypotheses. The statistical population of this research is all the firms accepted in the Tehran Stock Exchange over 7 years, from 2016 to 2022. Considering the research period, the total number of data points is 980 years—firm (observation). Also, in this research, the stock price was used to evaluate the variable of past stock performance, which has not been paid attention to in past behavioral financial research due to its importance for investors' decision-making. The analysis of the research hypotheses showed that risk aversion has a positive relationship with investors' decision-making. In addition, the study of research data indicates that the past performance of stocks has a positive moderating role in the relationship between risk aversion and investors' decision-making.

Climate change and infectious diseases have emerged as two of the most significant challenges facing the world and its economic systems today. Recent studies have increasingly focused on understanding how phenomena such as the COVID-19 pandemic and the climate change crisis—including rising greenhouse gas emissions and uncertainties in climate policy—affect macroeconomic variables and capital markets. Many experts assert that climate change poses a serious threat to human civilization. In light of these pressing concerns, this paper examines the impact of Climate Policy Uncertainty (CPU) and Infectious Disease Risk (IDR) on the Islamic Dow Jones Index (IDJ). To achieve this, the study utilizes monthly data spanning from January 2016 to March 2021 and employs a Structural Vector Autoregressive (SVAR) model. Additionally, Contour Plot graphs are applied to estimate the interaction effects on the IDJ. The findings reveal contrasting impacts of the CPU and IDR indices on the IDJ. Specifically, an increase in the CPU index leads to higher utilization of the Earth's resources for producing goods and services by companies included in the index, resulting in a positive effect. Conversely, a rise in the IDR index exacerbates economic recession, thereby causing a decline in the IDJ.

This study aims to develop a working capital management model for companies listed on the Tehran Stock Exchange. The proposed model determines the expected level of working capital for a company, enabling it to create the highest possible value. Additionally, this model can be used to assess the efficiency of working capital management. The discrepancy between the actual level of working capital and the expected level serves as an indicator of inefficiency in working capital management. Initially, based on theoretical foundations and expert opinions, 28 variables affecting working capital were selected. Then, using the operational working capital index, the research models were estimated using multiple regression and genetic algorithm techniques for data from 156 companies over the period from 2011 to 2022. Influential variables were identified and filtered. Finally, suitable working capital management models were identified based on two criteria: (1) the strong correlation between the errors of the fitted models and the working capital efficiency of the company, and (2) the model’s accuracy in identifying companies prone to excess or shortage of working capital. In total, after estimating 119 different models using regression and genetic algorithm methods, four suitable working capital management models were determined. The regression method resulted in models with an average accuracy of 77.27% and 79.54% for the dependent variable of working capital and the cash conversion cycle, respectively. The genetic algorithm method resulted in models with an average accuracy of 89.03% and 82.08%. The final model, with the cash conversion cycle as the dependent variable, was identified as the best model. It includes the variables of the previous year's cash conversion cycle, company-specific risk, gross profit margin, trade credit, growth opportunities, operating cycle, economic policy uncertainty, and exchange rate changes.

This study explores the considerable significance of candlestick chart patterns as a foundational asset within the realm of stock market analysis and prediction. As a graphical representation of historical price movements and patterns, Candlestick charts offer a distinct and valuable perspective for understanding how the financial market operates. This perspective assists us in accurately pinpointing the most advantageous times for making decisions to buy or sell financial securities, such as stocks or bonds. These charts provide insights into market trends and potential trading opportunities. We adopt an innovative approach by harnessing image processing techniques to extract and analyze patterns from Candlestick charts systematically. Our findings underscore the pivotal role of visual data in financial analysis, particularly in times of market volatility and uncertainty. Investors often resort to technical analysis strategies when confronted with erratic market trends, often relying on insights derived from chart-based analysis to guide their decision-making processes. By meticulously extracting essential insights from candlestick charts, our study aims to provide investors with more efficient and less error-prone tools. Ultimately, this endeavor contributes to the enhancement of decision-making precision and the mitigation of risks inherent in participating in the dynamic stock market landscape.

This study investigates the impact of the board's social capital on the investment efficiency of listed companies in the Tehran Stock Exchange. Based on the theoretical foundations, the board social capital as a social-behavioral factor can affect the problem of over or under-investment (both of which are examples of the inefficiency of investment decisions). Therefore, when the board's social capital is at a high and favorable level, company managers show less opportunistic behavior and do their best to increase cooperation and interaction within the company, which leads to the strengthening of investment efficiency. In terms of purpose, the current research is the applied-developmental type and takes a descriptive-correlational manner. We measured board social capital using the Co-Working Experience index. Investment efficiency is also measured through under- and over-investment using the Richardson (2006) model. The control variables also include the size of the board of directors, the independence of the board of directors, the size of the company, the ratio of net profit to sales, the rate of return on assets, and the level of financial leverage. The statistical population of the research includes 183 companies admitted to the stock exchange from 2016 to 2022. In order to test the research hypothesis, a multivariate regression model has been fitted using the panel data method with the fixed effects approach. The results of the research indicate that the hypotheses of the research are confirmed, and there is a positive and significant relationship between the social capital of the board of directors and investment efficiency.

Portfolio optimization has always been the main concern of investors. What differentiates different optimization models from each other is the risk measure. The main contribution of this paper is to provide a portfolio optimization model that considers systemic risk so that it can help investors make optimal investment decisions as a general model. For this purpose, two models are presented. In the first model, systemic and systematic risk were considered simultaneously, and in the second model, only systemic risk was considered. In the two mentioned models, delta conditional value at risk (∆CoVaR) and the Markowitz model are used respectively to measure systemic risk and a benchmark model. Also, the criteria used to compare the performance of the reviewed models include the ratio of reward-to-risk, along with the Sortino ratio and the Omega ratio. The problem of optimization and examination of the results was carried out on a selected sample, 38 companies listed in the Tehran Stock Exchange (TSE) from 2013 to 2023. The results of empirical analysis of out-of-sample data (during a period of 1198 days) show that based on all three mentioned criteria, the first proposed model shows the best performance among the three models. In addition, the performance of the second model is ranked second. In short, it can be said that considering systemic risk in portfolio optimization leads to better performance than the Markowitz model.