The real return of investors in stocks is determined not only by the return of stocks, but also by the time and capital inflow and outflow from it. Therefore, in this study, we have differentiated between the two concepts of investors' RETURNS from stocks and stock RETURNS, and unlike previous researches, we have used money-weighted RETURNS instead of the percentage of price changes as a measure of INVESTMENT return. In order to compare the volatilities of investors' RETURNS and stock RETURNS, we have examined their volatility behavior on the TSE Index. Then, we have examined the safe heaven and hedge capability and the risk spillover effect of the dollar on the RETURNS of investors and compared the results with the RETURNS of the index. The results indicate that there is a significant difference between the volatility of investors' RETURNS from stocks and stock RETURNS, and the volatility of investors' RETURNS is lower than the volatility of stock RETURNS. Because investors tend to fluctuate with the market flow and enter the market in periods of high volatility to earn short-term profits and exit the market in periods of low volatility. Examining conditional volatility models shows that stock RETURNS have stronger leverage effects and more stability and durability in their shocks. At the end of the research findings, it is confirmed that the gold coin has a safe heaven and hedge capability for investors, and there is no evidence that there is a risk spillover effect from the dollar_rate on investors' RETURNS.

The success of any asset pricing model should depend on the proportionality of the real options in the value of the companies whose RETURNS are tested in the model test. The purpose of this study is to investigate the INVESTMENT Option Model Indicators and its impact on stock RETURNS. The statistical population of this study consisted of 146 companies in the period 1390-1397. This research is applied in terms of and in terms of nature and content is correlational and the research hypotheses are estimated using multivariate regression approach and panel data method. The results of the hypotheses test showed that INVESTMENT options based on Grullon's four-factor model affect stock In addition, the results showed that there is a significant difference between the explanatory power of Fama and French's five-factor asset pricing model and the INVESTMENT options model based on Grullon's four-factor model in explaining stock RETURNS. And the explanatory power of the Grullon's four-factor model in explaining stock RETURNS is more than the Fama and French five-factor model.

Designing the flexible INVESTMENT strategies for maximizing RETURNS under spatial variation and considering uncertainty in beef cattle INVESTMENT decision-making are vital. Therefore, the objectives of the study were (i) To explore the real options and its values spatially for beef cattle INVESTMENTs in Turkey, and (ii) To evaluate the adequacy of government support for beef cattle INVESTMENT spatially. Research data were collected from randomly selected 385 beef cattle farms by using questionnaires. The valuation of real options was assessed by using Binomial Valuation, Black-Scholes Method and Monte Carlo simulation. Tornado diagram was used for exploring sensitivity of decision variables for beef cattle INVESTMENT. The results of the research showed that the classical Net Present Value (NPV) value was -200.82 thousand US $. The NPV values of the options of wait, expand, and input-output change for the beef cattle INVESTMENT were US $102.37 thousand, 43.87 thousand, and 24.50 thousand, respectively. The research findings also showed that the value of real options and adequacy of government subsidies varied spatially. Based on the resuts of the sensitivity analysis, the most important variables affecting the investor's decision are carcass meat price, yield rate, capacity utilization rate, and fattening feed price, respectively. The research suggests that policy makers should consider the spatial distribution of INVESTMENT subsidies and policies to the specific needs of different regions to increase efficiency of INVESTMENT support policy.

Hamedan is located in a dry region of an average annual precipitation of 313 mm. Water which is one of the most important factors in agricultural production is in most parts of this region thet scarcest resource. Therefore, its optimum utilization for increasing productivity requires the use of modern efficient systems of irrigation. To improve efficiency in agriculture, adoption of water conservation technologies is inevitable. During the past few years, the government has concentrated efforts to introduce pressurized irrigation technology to improve water utilization efficiency. In this study, an economic analysis of pressurized irrigation projects in Hamedan province was made. Data were collected from 154 modern as well as from traditional irrigation systems. Stratified random sampling and projection models were built by using project evaluation criteria. Data were then collected through interviews for the agricultural year 2001-2002. Analyses were made by project evaluations such as Inner Revenuer at, Benefit - Cost Ratio and Net Present Value. Sensitivity analysis on the basis of income, expense interest rate, and useful life of plan indicate that the systems' return rate is highly dependent upon the original design as well as on the way they are utilized.

The necessity of research on predicting INVESTMENT RETURNS and presenting effective models of its explaining and compare the most efficient ones, affects both the expansion of INVESTMENT in the financial market and the creation of more confident conditions for decision making and portfolio formation. Experimental testing of some models has shown that INVESTMENT options are measurable as a way to create more options as one of the most common types of real options based on INVESTMENT options and can be effective in explaining stock RETURNS. The purpose of study is to explain the factors affecting equity RETURNS using the INVESTMENT options approach. The information from this study is taken from the financial statements of 146 companies during the period 1390-1397. The research is applied in terms of purpose; in terms of nature and content it is a correlation type. Multiple regression based on panel data analysis was used to test the relationship between variables and the significance of the model. The results of the hypotheses test showed that INVESTMENT options based on Grullon's four-factor model affect stock RETURNS and firms with more aggregated INVESTMENT options based on Grullon's model have higher stock RETURNS. In addition, the results showed that there is a significant difference between the explanatory power of Fama and French's five-factor asset pricing model and the INVESTMENT options model based on Grullon's model in explaining stock RETURNS. And the explanatory power of the Grullon's model in explaining stock RETURNS is more than the Fama and French five-factor model.

Safe INVESTMENT can be experienced by incorporating human experience and modern predicting science. Artificial Intelligence (AI) plays a vital role in reducing errors in this winning layout. This study aims at performance analysis of Deep Learning (DL) and Machine Learning (ML) methods in modellingand predicting the stock RETURNS time series based on the return rate of previous periods and a set of exogenous variables. The data used includes the weekly data of the stock return index of 200 companies included in the Tehran Stock Exchange market from 2016 to 2021. Two Long Short-Term Memory (LSTM)and Deep Q-Network (DQN) models as DL processes and two Random Forest (RF) and Support Vector Machine (SVM) models as ML algorithms were selected. The results showed the superiority of DLalgorithms over ML, which can indicate the existence of strong dependence patterns in these time series, as well as relatively complex nonlinear relationships with uncertainty between the determinant variables. Meanwhile, LSTM with R-squared equals to 87 percent and the analysis of the results of five other evaluation models have shown the highest accuracy and the least error of prediction. On the other hand, the RF model results in the least prediction accuracy by including the highest amount of error.