Any decision on investment is considered one of the most important decisions in terms of financial management. Since, the future benefits could not be certainly determined, investment necessarily bears risk. Therefore, it has to be evaluated through its expected value and level of risk. As a result, deciding on investment indicates total ASSETs of a company together with the composition of such ASSETs and business risk in the eyes of investors.One of evident characteristics of accounting is that it helps investors in the course of predicting the RETURN of investment. However, accounting should provide investors with necessary information so that they can make the best selection of shares together with their level of risk and proper RETURN among the various shares of stocks in the market. In turn, the information is reflected through financial statements including balance sheet.This paper tends to study the relationship between the net operating ASSET and the share RETURNs in the companies subscribed in Tehran stoke exchange during 2001 and 2008. This study can contribute to investors and users in the best Selection of shares group out of various shares of stock.

Any decision on investment is considered as one of the most important decisions in terms of financial management. Since, the future benefits could not be certainly determined, investment necessarily bears risk. Therefore, it has to be evaluated through its expected value and the level of risk. As a result, deciding on investment indicates total ASSETs of a company together with the composition of such ASSETs and business risk in the eyes of the investors.This paper tends to study the relationship between the net operating ASSET and the share RETURNs in the companies subscribed in Tehran stoke exchange during 2001 and 2008. This study can contribute to the investors and users in the best selection of shares group out of various shares of stock.

Choosing a stock portfolio in investment discussions is a difficult and difficult task. Deciding which stock is in a better position compared to other stocks and deserves to be selected and placed in one's investment portfolio, and how to allocate capital between these stocks, is a complex issue. Considering the importance of the issue,The main purpose of this research was to investigate the selection of the portfolio of stock ASSETs based on the method, risk and RETURN of the stock market in the Tehran stock market. This study is post-event in terms of practical and methodological purpose, the data collected annually from 1391 to 1402, which includes twenty active companies in the Iranian stock market. which was selected using the risk minimization model based on the Markotiz model of the optimal portfolio. The results and analysis show that the selection of the efficient portfolio is based on the efficiency frontier diagram of the latest stock price of the companies based on yield and risk, for example,Amir Kabir Kashan steel has less risk and more yield than all the items. Pars Industrial Carbon Black is less risky than Fan Avran Petrochemical, Chemical Daro Pars. However, it is less efficient compared to Techno-Avaran petrochemicals, but it is more efficient compared to Dorofos chemical. Khuzestan steel has the highest risk and zero RETURN of all items and should not be chosen. Chador Melo, Khark Petrochemical, Mobarakeh Steel have more risk and very very little RETURN,And should not be chosen.

Explanation relation between risk and RETURN and capital ASSET pricing are concepts which is appointed as dominator and major paradigms in capital markets. So far as after offering CAPM by Sharp & Lintner, this model has been revised and criticized frequently.In this paper another version of CAPM has been tested versus traditional CAPM in Tehran Stock Exchange. This version of CAPM measures sensitivity of stock RETURN to consumption percapita and consider it as measure of risk. This model is identified as Consumption based Capital ASSET Pricing Model or in abbreviation is C-CAPM.We examine 184 stocks in Tehran Stock Exchange which have not had long deadlock from beginning of 1380 to end of 1385. Despite of the fact that Consumption beta should be a better measure of systematic risk theoretically, but our results show empirical performance of traditional CAPM is more encouraging than C-CAPM, and CAPM had relative success over all tests.

In this paper we use robust optimization to solve ASSET allocation problem under uncertainty for RETURN and covariance-variance parameters. Not taking uncertainty into account about input parameters in optimization problems can take optimal solutions away from optimum region or make them infeasible. For designing and defining the set of uncertainty for RETURN and covariance-variance, we use the concept of estimated distance and bootstrap for predictions, respectively. Calculations of the sets of uncertainty are based on predications of ARMA and GARCH methods. In order to ensure that robust approach’ s results outperform the nonrobust approch’ results we use robust sharpe index with the help of pair comparison test during eight consecutive quarter periods. Finally, some numerical examples are given to illustrate the effectiveness of the proposed approach.

The main objective of this research is the introduction of conditional downside capital ASSET pricing model which is the consolidation of conditional ASSET pricing model and downside capital ASSET pricing model.this model suggests that for the explanation of the relationship between the risk and the expected investors RETURN its necessary to pay attention to the mere risk, and in all aspects this model indicate the relationship among risk, RETURN and market risk elimination. it is supposed that an investor receive RETURN in accordance with the risk it tolerate in the polar market, but at the unsymmetrical market, this assumption is not complete, however obtaining RETURN is the identical assumption in both markets.thus several model are available for the calculation of the expected RETURN ratio, that the presented model here has much more power in comparison with other current models with the help of pierson and spierman correlation tests, the relationship between the following variable is examined: traditional β, downside β, capital ASSET pricing model, downside capital ASSET pricing mode, conditional downside capital ASSET pricing model.

One of the main concerns of investors is the evaluation of the RETURN on invest-ment, which is conducted using various models such as the CAPM (single-factor model), Fama-French three/five-factor models, and Roy and Shijin’ s six-factor model and other models known as multi-factor models. Despite the widespread use of these models, their major drawbacks include sensitivity to unexpected changes, sudden shocks, high turbulence of price bubble, and so on. To eliminate such negatives, the multi-factor model using the penalty function method is used, in which, instead of averaging, the optimization and avoidance of the effects of abnormal changes and other factors affecting the capital market are considered. In order to evaluate stock RETURNs, it is possible to select effective factors, to simulate and develop a model appropriate to the conditions governing the capital market in Iran. In the present study, by forming portfolios of investments and identifying and refining effective factors, the classification and estimation of the hybrid model of penalty and multi-factor (P and PCA) functions were performed based on the functional data during 2007-2017. The results of this study indicated that the extensive use of the simulation algorithm for the penalty function in the form of P and PCA estimation method improves the efficiency of multi-factor methods in stock RETURN evaluation, and that the use of the hybrid algorithm of penalty and multi-factor functions, compared to the exclusive use of multi-factor models, brings a higher accuracy in estimating stock RETURNs.

In This study we have tried to find relationship between variables such as market value and earning price ratios (E/P) and leverage ratio with expected rate of RETURN using both CAPM and D-CAPM in Tehran stock exchange during 2001-2006 The results indicate that in a systematic Downside Risk Capital ASSET Pricing Model, there is a positive and significant relationship between systematic downside risk, Betta coefficient, size of companies and proportion of earning price ratio with RETURN on shares of companies in Tehran Stock Exchange. However, the relationship between the ratio of book to market value of companies and leverage ratio to dividend yield were insignificant. We have also found that comparision between CAPM and D-CAPM provide a better result for testing the relationship between risk and RETURN and it turned out to be even more efficient.

This research examines the relationship between ASSET growth and future stock RETURNs over the period 2007 to 2011. Considering the diversity of ASSET growth measures, we use a new measure derived from factors analysis in addition to the most important ones. We use the Fama and MacBeth model for testing the relationship between ASSET growth and future stock RETURNs in this research. The research is based on correlation approach and we use multiple regression panel data for analyzing data. This research concludes that there is negative relationship between ASSET growth and future stock RETURNs. Also, our findings show that Lyandres et.al measure of ASSET growth has the greatest predictability power among others. Furthermore, our results show that factor analysis measure does not have greater predictability power than other measures.

Evaluation of stocks based on RETURN and risk related to capital ASSETs is one of the important issues of this field. The majority of multifactor models are defined based on the assessment of one of the RETURN and risk criteria. Nevertheless, the model presented in this study evaluated RETURN and risk simultaneously. The multifactor patterns are static and do not express dynamic changes during time intervals affected by latent factors. In this research, unpredicted fluctuations in stock RETURN were defined as latent factors in the penalty function. A more accurate estimate was provided by using the simulation of Fama– MacBeth regression in the estimation of effective parameters and separation of the effects of latent and manifest factors affecting stock RETURN and risk. According to the analysis of the field of knowledge and content analysis, factors affecting the stock RETURN were recognized, and the most effective factors including market measures were refined as manifest factors based on the tolerances. Finally, the model proposed(P-PCA) was exploited in risk prediction (at risk value). According to the results of the study, the mentioned model more efficiently showed the effects of latent and manifest factors on stock RETURN over a long period. In addition, it was able to predict the risk of investment with proper accuracy and similar to patterns of conditional variance, such as ARCH and GARCH.