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The optimal portfolio selection is vital for investment. The risk of portfolio Selection and return is the most critical concern of investment companies and private investors. According to modern portfolio theory, diversification should cover the risk. This theory is based on the normality of assets return. Experimental findings indicate that the assets return non-normality. Higher moments are sed to upgrade traditional models with the primary presumption of a normal distribution in recent years. This study uses a higher moment and the entropy for diversification and selects a portfolio given a non-normality assumption. It is essential to use up-to-date information to increase the model's efficiency, and accordingly, we used the rolling window for new price information. For the financial information method, we use the total index return in the last five working days and weigh the shares of the banking, insurance, and leasing industries on the next working day and evaluate this for three years. Python, math, and NumPy libraries were used to analyze the data. The results show that a much higher moment model can provide better portfolio selection results in most cases.

Objective: To identify the components affecting the behavior of investors and to develop a measurement model using a confirmatory factor analysis approach.Method: This is a correlational paper to identifythe dimensions and structures affecting investor behavior first using TOPSIS technique and then with first-order confirmatory factor analysis and second-order factor analysis. The statistical population includes people who have been active in the Tehran Stock Exchange for at least two years with asample of 327. The sampling method is convenience nonprobability sampling. The data was collected through a researcher-made questionnaire. The expert approval and Cronbach's alpha coefficient were used to assess the content validity. Findings: the current paperidentified seven factors as effective factors on investor behavior according to the theoretical literature and research background using TOPSIS technique. In the next stage, the research findings using the confirmatory factor analysis approach indicate that the two factors of investor financial literacy and investor personality traits have the most effective role in investor behavior. Also, the factors of higher expected returns, rules and regulations, security, profitability, position and location of investment are the next effective priorities on the behavior of investors.

One of the main demands of investors (depositors and shareholders) of banks is transparency. However, in addition to the requirements for meeting this demand, measuring how to meet it has become challenging. So far, researchers have proposed different qualitative criteria for transparency. In this study, while introducing the correlation coefficient between book and market value at risk (VaRs) as a criterion of transparency, we seek to examine the stability of this criterion in different economic conditions. For this purpose, first, by using the e-garch model, the value at risk was estimated based on the balance sheet (book) information and also the market information of the banks' shares, then by calculating the correlation coefficients between book and market VaR’s under normal conditions, we predict book and market VaR’s using vector auto-regressive (VAR) models, along with defining three stress scenarios (Mild - Severe - hyper stress). We examined the significance of the difference between the calculated correlation coefficients in the three stress test modes. We thus tested the stability of the correlation coefficient of the defined scenarios. The findings showed that except for the correlation caused by the unemployment rate factor in mild and hyper-stress scenarios, in other cases, no evidence of H0 rejection was found, indicating the stability of the correlation coefficient between book and market VaRs as a measure of transparency.

Cash flow forecasting has significantly increased since 2000 due to more attention paid by investors and financial analysts than before. If cash flows can be predicted appropriately, a significant part of the informational needs associated with cash flows will be provided. In this regard, this study aims to examine the impact of firm characteristics on predictable future cash flows from operating activities by employing present operating cash flow and profitability. Eight hypotheses were developed, and the information was analyzed for 127 firms listed on the Tehran Stock Exchange between 2011 and 2020. The regression model was tested with a fixed effect model using panel data. The study's findings showed that firm characteristics like size, level of competition, and level of supervision positively impact the predicting power of present operating cash flow and profitability in anticipating future operating cash flow. By contrast, the outcomes disclose that characteristics such as the company's life will not significantly affect the predicted strength of present operating cash flow and present profitability to forecast future cash flow from operating activities.

The purpose of this research is to design a profit maximization model for companies using the theory of constraints. To collect information, library and field studies were used, according to the method of data collection, documents, interviews and questionnaires were used, so the current research method is mixed (qualitative and quantitative). The statistical population of the research in the qualitative part included experts, professors and specialists in the field of management accounting, 10 of whom wereselected by snowball sampling until theoretical saturation was reached, and primary data were collected through interviews and a questionnaire was designed. A questionnaire was included in the quantitative part as well. All managers and assistants of the finance and accounting departments of manufacturing companies admitted to the Tehran Stock Exchange were an unknown number. According to Cochran's second formula, 385 people were selected by available sampling. A questionnaire with 78 questions was used tocheck the research variables. Cronbach's alpha coefficient confirmed the reliability of the questionnaires, 10 interviewees confirmed the content validity of the questionnaires, and their construct validity was confirmed by confirmatory factor analysis. Then, the data obtained from the questionnaire was analyzed in two sections: descriptive statistics and inferential statistics. In the section on inferential statistics, the Kolmogorov-Smirnov test was used to check the type of data distribution in terms ofnormality or not, and then confirmatory factor analysis by LISREL 8.80 software. In order to check the construct validity of the questionnaire and to test the structural equation model with the help of Lisrel SmartPLS.03 software, it has been used to answer the research questions. The most important findings of the research showed that the components of identifying the limits of profit maximization, exploiting the limits of profit maximization, spreading the effect of profit maximization decisions, removing system limitations, and the process of continuous improvement of profit maximization, respectively, with a path coefficient of 0.41, 39 0.0, 0.43, 0.37, 0.40 have a positive effect on the theory of limitations

The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model .

The present study aimed to identify and prioritize the factors affecting the marketing of knowledge-based products of basic chemicals. This research is applied in terms of objectives and a data-based cross-sectional survey concerning its method. Initially, the factors affecting the marketing of products in this sector were identified based on library and field studies and by purposive sampling of 15 senior managers of knowledge-based chemical companies (research experts) utilizing a semi-structured interview and in the form of a questionnaire. Cochran’s formula was then employed, and 400 experts and staff working in this department were selected as the study’s statistical population through simple random sampling. Subsequently, confirmatory factor analysis was used to analyze the data and check the final model’s fitting. In the end, each factor’s relative importance was determined by the fuzzy DNAP method. The results led to the identification of 16 sub-criteria under seven main criteria, including technical and managerial, information and knowledge, economic, infrastructural, legal, and support issues, besides supply capabilities. The results revealed that the information and knowledge criterion and the innovation sub-criterion had the highest score, while the supply capability criterion and the knowledge management sub-criterion were ranked last.