The main objective of present study is to investigate the existence or absence of information content of accounting variables and degree of their association in evaluating equity risk and return, that can improve the investor’s knowledge of analysis, confirming or rejecting the previous decisions in an ideal manner.In this research, an attempt is made to make use of Residual Income Model, in order to come up with a new accounting method in assessment of equity risk on price and stock return, using the difference between market price and intrinsic value (price differential) in market of Iran. Moreover, we employed Fame and French (1992) three-factor-model (Market beta, Firm size and Market-to-Book ratio) as determining risk factors.Results indicate that market beta is not related to price differential. Instead, Market-to-book ratio is significantly positively related to price differential. Also price differential is significantly positively related to abnormal return. However provides only limited power in explaining abnormal return. ]t was found that relative information content of Price Differential and Systematic Risk are not similar. Finally, Price Differential, as Systematic Risk does not have incremental information content in explaining Return.

Value at risk and expected shortfall are the two most popular measures for calculating financial risk. To calculate these measures (Value at risk and expected shortfall) there are many approaches, which can be divided into two main categories; parametric and non-parametric. In parametric approach it is supposed that the distribution of asset return belongs to a specific class of distributions. For some distributions we can claculate easily the mentioned measures. In this paper the the relation of epected shortfall has been proved for four symetric distribution.

After the 2008 financial crisis, the importance of studying systemic risk became more apparent. In this regard, various metrics have been presented to measure systemic risk, but the main question is which metric has a better and more comprehensive function than other metrics. The main contribution of this paper is answer foresaid question. In this research, the most widely used indicators include systemic expected shortfall, marginal expected shortfall, delta conditional value at risk and component expected shortfall using data related to 38 companies (selected sample) listed in the Tehran Stock Exchange (TSE) from 2014 to 2023 (a ten-year period) has been examined and compared. The research method is based on the correlation and reliability matrix of the values of each measure and the ranking of the sample companies during the period under review. The findings show that the measure of delta Conditional Value at Risk (∆CoVaR) has the best performance in order to explain systemic risk.

In the present study, we examined the effect of the expected investment, expected profitability and the book to market ratio on the market expected returns. Fama-French model (2006) was used To calculate the Expected profitability and the expected investment. expected return, market value and book to market ratio was calculated by conventional methods. with the aim of increasing comparability, the two growth variable number of shares and equity book value growth also has been used separately. study was conducted in two stages regression so that the first stage regressions to estimate the expected investment and profitability expected and second stage regressions also is used to examine the relationships between variables. The results show that there is a significant positive relationship between the expected return and the expected profitability and book to market ratio. The relationship between the expected return and the expected investment is significant negative. The results confirmed all the hypotheses.

The concept of “ value” in a business is a potential product or service that a business promises to deliver to the customer and in general, it is the reason why a customer chooses a brand and prefers it over competing brands. In recent years, with the increasing presence of consumers in social networks, it has become possible to access data related to the interests and expected values of consumers. The purpose of this study is to identify the components of value, in order to create and present value to the customer, using the analysis of comments and content produced by consumers in social networks. For this purpose, 41, 904 customer comments, regarding the "cell phone" product were collected from the Digikala online store site and analyzed using "big data analytics" methods (Opinion Mining and Latent Dirichlet allocation). According to this study, five main groups of values were detected: 1. Functional values, 2. Economic values, 3. Qualitative values, 4. Emotional values, 5. Social values. Also, the components related each group was identified. The results show that by using big data analytics, a clearer picture of the customer's expected values can be obtained with more speed and less waste of resources, in order to provide products tailored to the needs and desires of the consumer.

Objective: Left-tailed risk illustrates the probability of unfavorable events that could occur in a range wider than three variances of the distribution function. Although such events have a very low occurrence probability, they would cause significant losses in case of occurrence. This research aims at examining the cross-sectional effects of left-tailed risk on expected excess returns. The present research also examines the probability of the persistence of left-tiled risk in the future. Methods: In this research two proxies of value at risk and expected shortfall are used to measure left-tailed risk. For this purpose, a sample of 120 companies listed in the Tehran stock market in the period of the years 2010-2017 have been selected. Research hypotheses were examined with the use of Fama and Macbeth regression. Transition matrix was used to determine the probability of left-tailed risk persistence in the future. Results: According to the findings of the research, left-tailed risk has a significant and negative effect on the expected excess returns. The findings also suggested that the negative returns of the left tail will have a persistence probability of over 50% in the future. Conclusion: The findings of the present research illustrate a new anomaly in the financial area, which is the negative effect of left-tail risk on the expected excess returns, and persists in the future.

Identifying the needs and expectations of different customers in different market segments is the first step in implementing a targeted marketing strategy for each company. The market segmentation can exactly analyze the group of potential customers, whose needs are not well provided by current products, allows them to identify opportunities to produce new products. The purpose of this research is segmenting customer of nuts and dried fruits market. The statistical population of this research is the buyers of nuts and dried fruits in Mashhad County. After interviewing with the five experts of nuts & dried fruits, identified 32 expected values, then questionnaire was developed based on demographic variables and customers’ expected values and was distributed among the supplier stores of nuts and dried fruits using accidental non-probability sampling method in Mashhad County. The validity of the questionnaires increased based on the experts’ opinion and its reliability calculated using SPSS software. The Cronbach’s alpha coefficients were 0.957 for the whole questionnaire, which indicated high reliability of research tool. DBSCAN algorithm and Rapid Miner software were used for data analysis and customer segmentation. Based on the findings of this research, three market segments of nuts and dried fruits customers; which are called Luxury Customers, Sensitive Customers and Ordinary Customers, were identified on the basis of demographic characteristics and their customers’ expected values. Finally, practical and managerial suggestions were provided according to customers’ expected values and demographic characteristics of each market segment.