مجله مدیریت دارایی و تامین مالی
سال ششم شماره 2 (پیاپی 21، تابستان 1397)

One of the essential actions for perfect investment in the stock market is to consider various criteria in stock selection. One of these criteria is economic value added. By combining risk and return measures with economic value added, dividends and kurtosis and skewness coefficients; and using goal programming, a model that can lead to the most optimal portfolio can designed. The analysis of hierarchical process (AHP) was used to calculate the importance of the model criteria. In this study, comparison of the results was done between the ideal of economic value in the absence aspirations of dividends and vice versa and presence of both the ideals and Markowitz model. The research findings showed that the proposed model of this study has a higher return to risk ratio, compared to other models tested in this study such as Markowitz model, and subsequently the proposed model leads to a greater return.

This study investigates the relationship between some criteria of information quality with stock liquidity risk and market risk. For this purpos, the effect of three criterion of information quality including accrual items quality, earnings forecast error and EPS announce timely on stock liquidity risk and market risk by collecting financial data of 148 firms listed in Tehran Stock Exchange for the years 2008-20113 were tested using regression analysis. The results showed that there is a significant relationship between all three criteria of information quality with stock liquidity risk. So that, accrual items higher quality, EPS announce more timely and earnings forecast less error decrease stock liquidity risk. Also, the results indicated that there is no significant relationship between none of the criteria of information quality with market risk.

Nowadays, the pharmaceutical industry in terms of its relationship with public health and also the special economic value is considered one of the most important industries in the capital market. Therefore, improvement of the health sector has always been the main objective of the authorities in various countries. Hence, this paper investigates the impact of exchange rate on the pharmaceutical industry stock return in Tehran Stock Exchange by using monthly data from 2005:4 to 2016:2 and applying Markov switching non-linear approach. For this purpose, among the various modes of Markov switching model, MSIH (3) -AR (2) has been selected. The results show that in an optimal model consists of three regimes, and the exchange rate has different impact on the return of the pharmaceutical industry in different regimes. As a result; coefficients exchange rate in the first regime has negative effect, but in regimes 2 and 3 positive effect on the pharmaceutical industry stock return in the Tehran Stock Exchange. Also, the inflation rate of the health sector in the regimes 1 and 2 have a positive effect, but in the regime 3 has a negative effect on the pharmaceutical industry stock return in Tehran Stock Exchange. In addition, the results show that the sustainability of the pharmaceutical industry return in the regime with the high returns (first regime) was more than that of the regime with the little low return (second regime) and that of the regime with the high low returns (third regime). But the volatility of the pharmaceutical industry return in the third regime was more than that of the first and second regimes.

The purpose of this study is to examine the effect of banking sector credit and government deficit’s shock on stock market size in selected developing countries. Analysis has been made on data from 15 developing countries over the period 2012-1993, using panel VAR model. Causality test results show that at 95% confidence level there is not causal relationship between bank credit and the private sector and stock market capitalization (market size). But stock market capitalization variable had a causal effect on the government deficit and gross fixed capital .The analysis of impulse response functions reflects banking sector credit and deficit shocks’ positive effect and negative effect of shocks gross fixed capital formation on stock market capitalization index. Analysis of variance showed that the effect of stock market capitalization variable shock explained high percentage of its prediction error variance; and then arrange the gross fixed capital formation, government deficit and bank credit to the private sector to explain the fluctuations of stock market capitalization index.

Corporate governance plays an important role in ensuring the quality of the financial reporting process. Many of recent corporate failures have been attributed to poor governance, which is manifested in fraudulent financial reporting and earnings mismanagement. Therefore, the purpose of this study is to investigate the impact of corporate governance mechanisms on financial statements fraud of the listed companies in Tehran Stock Exchange (TSE). To achieve this aim, Board Composition, Ownership Concentration and Institutional Ownership were used as corporate governance mechanisms. Furthermore, nineteen red flags of fraud extracted from auditing standard No. 240 along with Data Mining techniques such as K-Means clustering used to discriminate financial statements fraud cases. The data sample is restricted to 182 companies in the (TSE) during 1381 to 1393. In conducting this research, three main hypotheses were proposed to examine the impact of board composition, ownership concentration and institutional ownership on fraudulent financial reporting across the firms and during the designated period. Accordingly, the statistical technique known as the “Panel Data” was exerted to test the research hypothesis. The results of the hypotheses testing demonstrate that fraudulent financial reporting is negatively affected by proposed corporate governance mechanisms, after controlling for potential confounding effects.

In this paper, the effect of agency cost on the relationship between corporate governance and cost of equity capital is studied. The models for testing hypotheses are multivariate regression model from Chen et al (2011) study. For this purpose, the related data of the listed companies in Tehran Stock Exchange was utilized from the periods 2009 to 2015, including 140 companies. The results show an inverse relationship between corporate governance and cost of equity capital. This means the companies that have better governance system, will have lower financing costs and the investors demand lower yields from them. Indeed if agency cost becomes lower, real investor has more confidence, thus more financial capital is provided for companies. Also the amount of agency costs and the investment opportunities have effect on the relationship between corporate governance and cost of equity capital.

Inadequate and inefficient provision of information in the last three decades has resulted in some problems in global financial markets. As a result, XBRL international standard preparation was proposed as a solution for financial reporting to associante with international markets. Because of the cost concerns of implementing this standard, it is necessary to investigate its effects on different parts of countries` financial system to choose the best performance scenario via cost-benefit analysis. This paper investigates the impact of implementing extensible business reporting language (XBRL) on the financial analysts` behavior in Iran, by using AMOS software for Structural Equation Modeling. We analyzed 140 financial Analysts responses that were gathered via a questionnaire and found that XBRL has positive effects on financial analysts` behaviors in the decision-making process, as follows: 1) Analysis behavior (78%), 2) search behavior (75%), 3) validation behavior (74%) and 4. behavioral biases (46%). Also the results of ANOVA show that analysts’ age and experience affecnts their decision making behaviors.

Investors are always looking for information about their investment choices to have a favorable investment and an optimized allocation of their resources. Bankruptcy prediction of the firm is one of the most important subjects that can help investors in this way. Many studies have been done in the field of bankruptcy prediction. However, the majority of them provide a general model for all industries as a unit. The main objective of this study is presenting a bankruptcy prediction model, specific for each industry, for three industries including automobile and parts manufacturing, chemical products, and Food, except for sugar products, using decision trees model. To determine bankruptcy of the firm we used the criteria of Article 141 in Commercial Code. This research was performed from 2002 to 2014. The results show that the designed model has a prediction accuracy of 95.95, 96.83 and 97.83 percent for automobile and parts manufacturing industry, chemical products industry, and Food, except for sugar products industry, respectively. These findings reflect high accuracy of these three models, especially for Food, except for sugar products industry.

In an uncertain economic decision environment, the knowledge of experts about discounting cash flows is confronted with a lot of ambiguities. Inexact parameters like cash flows and interest rates are usually estimated based on statistical techniques, expected values and fuzzy theory. In this study, by combining the fuzzy theory and Monte Carlo simulation, a method is presented for feasibility analysis of the investment projects using net present value and internal rate of return techniques with fuzzy data. The methods provided in litreture have several gaps. Firstly, some of these methods are based on specific fuzzy numbers (triangular and trapezoidal); secondly, they consider life of project and duration of investment as deterministic values or discrete fuzzy numbers. However, in many real problems, cash flows are in the form of fuzzy numbers with different shape and also project life is considered as continuous fuzzy number. The Method is presented in this paper can be used for cash flows, interest rates and life of project without any restrictions on shape of fuzzy numbers as discrete or continuous fuzzy number. Computational efficiency of the proposed method is compared with other methods by solving several examples. The results show the accuracy and efficiency of the proposed method.

The result of prior research shows that if the volatility of earnings increases and their persistence decreases, past earnings aren’t good predictors of future earnings. Relative to aggregate earnings, earnings components will be more informative for firms whose earnings components have different persistence and volatility. We examine whether these earnings components have incremental information content relative to aggregate earnings, and then we construct a model to describe the effect of components’ volatility and their persistence on earnings predictability. In this study, the analyses are based on a sample of 33 firms listed in Tehran Stock Exchange. Data needed are derived from financial statements of companies listed in TSE. To test our hypotheses, we use data for years from 2002 to 2016. The results of study show that higher persistence and lower volatility of components will increase earnings predictability. Among the main components used in the research, sales, cost of goods sold, employee expenses, depreciation, other general, administrative and selling expenses, and interest expenses improve earnings predictability.