Objective Investment, the choice of assets to maintain and earn more for future prosperity, is one of the most important issues in the economy of all countries, commanding attention from individuals and high-ranking officials alike. Enhancing STOCK RETURNs through forecasting is a critical concern in capital markets, necessitating attention from both individual and institutional investors. Accordingly, the present study aims to optimize the forecast of risk-based STOCK RETURNs in selected INDUSTRIES of the TEHRAN STOCK EXCHANGE.   Methods Data analysis was performed in two phases. In the first phase, the data were estimated using a combined data method and AR (1) autoregressive process, from 2010 to 2019. This model forecasts STOCK RETURNs for selected INDUSTRIES on the STOCK EXCHANGE. In the second stage, using Data Envelopment Analysis (DEA), STOCK RETURN forecast optimization by the previous stage was optimized for selected INDUSTRIES of the TEHRAN STOCK EXCHANGE from 2010 to 2019.   Results The results of optimization of STOCK RETURN forecast in three INDUSTRIES of oil and gas extraction except exploration, extraction of metal ores and petroleum products revealed that the oil and gas extraction industry, except exploration, exhibited a higher efficiency of 0.4214 compared to other INDUSTRIES. The metal ore mining industry with an efficiency of 0.3728 stood in second place and the petroleum products industry with an efficiency of 0.2516 ranked in third place in terms of efficiency.   Conclusion Therefore, it can be said that optimizing STOCK RETURN prediction in the oil and gas extraction industry, excluding exploration, is at a higher level compared to other INDUSTRIES examined in this study. Given the oil industry's higher efficiency and optimization within the country, it is feasible to integrate oil into the production cycle using effective methods and introduce technological advancements to enhance oil supply within the country. This is particularly viable as the production output of other INDUSTRIES is significantly lower compared to these three INDUSTRIES. basic measures should be taken to increase the production of industrial and non-oil goods to reduce imports and increase exports of final goods to other countries to increase economic growth. Hence, it is recommended to utilize the oil and gas extraction industry, excluding exploration, for the production of final goods, followed by leveraging other INDUSTRIES for further production of final goods. Also, since the oil products industry is influenced by more variables in predicting STOCK RETURNs, it is recommended that investors in the oil products industry be aware of the variables studied in this research, especially the industrial production growth rate, and consider their STOCK RETURNs and investment levels accordingly. Future research should explore and assess the optimization of STOCK RETURN forecasting in other significant INDUSTRIES like pharmaceuticals, electricity, steam, hot water supply, etc. to compare and evaluate their outcomes alongside the findings of this study.

EXTENDED ABSTRACTINTRODUCTION Since increasing stress in financial markets is important for analysis and forecasting of economic activities and can be reflected in many variables of the financial market, recognizing the main sources of financial stress and its effects on various economic activities and sectors is one of the most important areas in the financial discussions. Considering the importance of this issue, in the present research, the financial stress index in Iran will be calculated, and then the short-term and long-term relationship between financial stress and STOCK RETURNs of top INDUSTRIES in the TEHRAN STOCK EXCHANGE by using the panel method in the form of a multivariate model will be evaluated during the period of 1384-1398 using daily data. The analysis of this relationship in the long and short term will be investigated using Co integration and Panel Error Correction Model (PECM) and dynamic ordinary least squares (DOLS) methods will be used to investigate the long-term dynamic relationship between model variables. It should be noted that in order to more accurately analyze, in addition to investigation the effects of total financial stress index FSI (which is a combined index of financial stress in the capital, currency and money markets) on the STOCK RETURNs of the studied INDUSTRIES, the financial stress index of each markets (money, capital and EXCHANGE market) has been entered into the model separately and we have investigated the effect of financial stress index in each of  studied financial markets on the STOCK RETURNs.  METHODOLOGY In this research, in order to investigate the effect of financial stress, oil price and other independent variables on the STOCK RETURNs of the studied INDUSTRIES, in the form of a multivariate panel model and the analysis of long-term and short-term coefficients, using Pedroni panel data method (Pedroni 1999&2004) and The panel error correction model (PECM) is used, and for this purpose the following is considered:                                                                                        (1) SR: STOCK RETURNs of the top INDUSTRIES in the TEHRAN STOCK EXCHANGE. This variable is calculated using the following: (Maditinos., & Theriou, 2011)                                                                           (2)                                                                              Ti,t  is the STOCK price index of industry i in period t.FSI: Financial Stress IndexINF: inflation rateINT: Interest rateRER: real EXCHANGE rate                                                                           (3)       ER: Nominal EXCHANGE rate       CPI*: Foreign CPI       CPI: Domestic CPIOIL: oil price FINDINGS The results show that in all four estimated models, the effect of the financial stress index on the STOCK RETURNs of INDUSTRIES is negative and statistically significant. The estimated coefficients for the oil price variable are positive in all four models, which are statistically significant in all models.The estimated coefficients for the inflation rate variable in all models have a negative sign and are statistically significant.The estimation results indicate the positive effect of the interest rate on the STOCK RETURNs of the studied INDUSTRIES.Based on the obtained results, the EXCHANGE rate in all models will have a positive effect on the STOCK RETURNs.  CONCLUSION The results indicate that in all four estimated models, the effect of financial stress index on industry STOCK RETURNs is negative and statistically significant. In other words, financial stress in the studied markets, including the capital market, money market and foreign EXCHANGE market has a negative impact on the STOCK RETURNs of INDUSTRIES and decrease STOCK RETURNs of these INDUSTRIES. Also, the research findings show that in all estimated models, oil prices, EXCHANGE rates and interest rates have a positive effect on STOCK RETURNs of the studied INDUSTRIES in Iran. In addition, the findings show that the estimated coefficients for the inflation rate variable are negative in all models and are statistically significant.

Financial markets are sensitive to EXCHANGE rate fluctuations of the Iran’ s economy. Changes in the foreign EXCHANGE market affect household, businesses, and government spendings. EXCHANGE rate management policy helps STOCK market to be protected from the effects of EXCHANGE rates. As for investment strategies, investors can invest without considering the EXCHANGE rate in the short run investments, but exposure to asymmetric EXCHANGE rate is very important in long run. This study explores the asymmetric EXCHANGE rate exposure of STOCK RETURNs building upon the capital asset pricing model (CAPM) framework, using monthly RETURNs of Iranian industry indices. In accordance with the existing literature, industry RETURNs are subject to lagged exposure effects, but the asymmetries vary across INDUSTRIES, which could be due to the discrepancies of trade balance and ownership of certain INDUSTRIES. Furthermore, the dynamic multipliers depict that industry RETURNs quickly respond to changes in the EXCHANGE rate and correct the disequilibrium within a short time, making the long run exposure to be symmetric or very small (Cuestas & Tang, 2015). Methodology The main aim of this study is, hence, to investigate the asymmetric EXCHANGE rate exposure of STOCK RETURNs in the Iranian STOCK market at the industry level. Specifically, we introduce the conventional CAPM for measuring EXCHANGE rate exposure. We construct the dynamic nonlinear model to investigate both the long run and short run asymmetric exposure effects, which is carried out by means of estimating a nonlinear autoregressive distributed lag (NARDL) model introduced by Shin and Greenwood-Nimmo (2014). Building upon the CAPM structure, this paper contributes to a growing literature on the analysis of EXCHANGE rate exposure of Iran's STOCK market on the following grounds. First, compared to linear regression models, the NARDL model demonstrates its competence and efficiency in estimating the EXCHANGE rate exposure. The disparities in the exposure effect depend on the ownership of these companies and the expansion of their global operations. Second, industry RETURNs strongly and quickly respond to EXCHANGE rate changes in the very short run, while most of the long run exposures are symmetric or very small. In fact, this paper studies the effects of positive and negative shocks of EXCHANGE rate on the RETURN of various INDUSTRIES in STOCK market based on CAPM model and NARDL approach to estimate parameters during the period of April 2012 to March 2015. To evaluate the efficiency of asymmetric effects of EXCHANGE rate on active INDUSTRIES in TEHRAN STOCK market, first EXCHANGE rates decompose to positive and negative shocks and then its asymmetric effects on STOCK market is analyzed using NARDL model. To do it, we use the Wald test for the symmetry or asymmetry effects of positive and negative EXCHANGE rate on RETURN of active INDUSTRIES in the short run and long run. Results and Discussion The results indicate that most INDUSTRIES of STOCK market are under the influence of positive and negative shocks of EXCHANGE rate and these effects are different for INDUSTRIES. Hence, the effects of positive and negative EXCHANGE rate shocks for the INDUSTRIES such as agriculture, textile, rubber, engineering, leather, communication, steel products, radio, chemical materials, and multi-disciplinary INDUSTRIES are symmetric while the effect of EXCHANGE rate shocks on RETURN of INDUSTRIES like bank, automobile, basic metals, publishing and printing, electrical devices, computer, tool medical, cement, finance, non finance, investments, paper, non-metallic minerals, and machinery INDUSTRIES are asymmetric in short run, and for INDUSTRIES of ceramic tiles, they are asymmetric in the long run. Additionally, in INDUSTRIES like mass production, oil, transport, coal, drugs, wood, sugar, food ingredients except sugar, they are asymmetric in the short run and long run. Thus, the results of this study can be useful for investors and shareholders in predicting the short and long term effects of EXCHANGE rate shocks on the STOCK prices. Therefore, it can be argued that sudden shocks EXCHANGE rate can affect about 70 percent of RETURNs of active INDUSTRIES in TEHRAN STOCK market. Therefore, avoiding sudden shocks and maintaining relative stability in EXCHANGE market are the main suggestions for policymakers. Also, given that the EXCHANGE rate shocks are exogenous variables for firm managers, investors should further evaluate the performance of companies and their profitability, and consider long run vision in analysis and making decisions.

In this research, STOCKs' RETURN momentum (as one of the most challenging issues of finance in the past 2 decades) is studied on TEHRAN STOCK EXCHANGE. The methodology of examining momentum includes forming of 6438 portfolios and testing the mean of these portfolio RETURNs statistically during a 10 year period from 2002 to 2011.The evidence shows in a sample consisting of 94 listed companies which constitutes majority of market capitalization of TEHRAN STOCK EXCHANGE, trading strategies based on RETURN momentum are profitable in midterm. Fama- French (1993) three factor risk model cannot explain momentum in medium term and momentum excess RETURN after adjusting for risk is a challenge to efficient market hypothesis. Therefore, midterm RETURN momentum can be explained by behavioral models and market under reaction can result in momentum. In long term, RETURN momentum disappears and a RETURN of strategies formed based on RETURN momentum is close to zero and insignificant.

Investor sentiment about the capital market can play an important role in STOCK price trends, market transactions, and especially on the STOCK RETURN synchronicity. The entry of many Individual investors who do not have enough information about investment, the study of this issue has made more important. To this end, in this research we investigate the relationship between investor sentiment and STOCK RETURN synchronicity in TEHRAN STOCK EXCHANGE, by using the financial data of 167 firms listed on TEHRAN STOCK EXCHANGE from 15 various INDUSTRIES during ten years from March 2010 to March 2020. We use the Baker-Wurgler (2006) sentiment index as our primary measure of investor sentiment that Measured by principal component analysis. Also, three different methods have been used to measure the STOCK RETURN synchronicity: the Mork model, the Carhart four-factor model and the Fama and French five-factor model. The results show that the Investors sentiment significantly affect on increasing STOCK RETURN synchronicity. The findings also showed that the coefficients of positive and negative sentiment are not significantly different and as a result, positive and negative sentiment are symmetrically affecting the increase in STOCK RETURN synchronicity.

Financial Strategies are Part of Corporate Strategy that are in Financial Management span and include decisions about Investment, Financing, Working Capital and Dividend. For evaluating and selection of these strategies, different inside and outside factors should be mentioned. This article investigates the role of Strategic Reference Points Theory in making clear financial theories and strategies and analyzes the ways of using this theory for making strategic decisions in the field of financial affaires in companies. The next point is that, coordination between these Strategies and Financial Strategy of a company (vertical alignment) and coordination between these strategies themselves (horizontal coordination) improve the performance. The findings approve the point.

In this paper the factors influencing abnormal RETURN on common STOCK will be addressed. Regarding capital increase in TEHRAN Security STOCK EXCHANGE the four independent factors potential efficacy abnormal RETURN. The factors are as following capital to increase the market value of outstanding shares, volatility of daily RETURNs, firm's size (in terms of market value of outstanding shares) and market cumulative RETURN (MCR).This paper addresses factors influencing abnormal RETURNs of common STOCKs, in regard to “capital increase” in TEHRAN Security EXCHANGE. We identify and examine four factors potentially influencing abnormal RETURN. These factors are: capital increase to market value of outstanding shares, volatility of daily RETURNs, firm's size (in terms of market value of outstanding shares) and market cumulative RETURN (MCR).Our sample consists of 96 capital increase in firms listed in TEHRAN Security EXCHANGE between 1383 and 1386. To examine the effects of capital increase on abnormal RETURN, we use an event study.Cumulative abnormal RETURNs are calculated for two thirty-day windows before and after the event. We use a multi-variable regression to compare the results in these two windows.We find that capital increase cause “negative” abnormal RETURNs. Among four independent variables, only volatility of daily RETURNs influences abnormal RETURNs.

According to volatility feedback theory there are relationships between STOCK RETURN and the risk of STOCK. However, the results of empirical research, in several countries and markets, are different. This study investigates the effect of anticipated STOCK RETURN volatility on STOCK RETURN in Automobile industry using GARCH in Mean (GARCH-M) models, and ARDL modeling and Bounds test approach to level relationship. We also investigate the effect of unanticipated STOCK RETURN volatility on STOCK RETURN using ARDL model and Bounds test approach in the period of 06/04/1998 - 06/07/2010, applying daily and weekly Automobile industry index in TEHRAN STOCK EXCHANGE. Estimation of the GARCH-M model results by applying FIML method of estimation show that anticipated STOCK RETURN volatility affects the STOCK RETURN positively. Moreover, Bounds test approach results from both models confirm existence of long-run relationship among variables under investigation at 1% significance level. The ARDL estimation results show that anticipated (unanticipated) volatility of Automobile industry STOCK RETURN increases (decreases) the RETURN in long-run. Results from Granger causality test confirms one-way long-run causation from anticipated volatility of Automobile industry STOCK RETURN to the RETURN.