The main goal of this research was to investigate the capability of past style Return in Predicting future Return in comparison with past Return. To achieve this goal, based on the book-to-market value and the company size, 150 companies data listed in Tehran Stock exchange in period from 1384 to 1393 were used in the study. In this relation style Return and stock Return were independent variable and (1, 3, 6, 12) future month stock Returns was dependent variable. After controlling size, book-to-market value by using two stage Fama and Mack Beth Regression (1973) the data were analyzed. The result of the study indicated that the past 6 month style Return can predict future Return (1, 3and 6) better than past 6 month Return. Furthermore there was a significant relationship between the past 6 and 12 month style Return and future month Return (1, 3, 6, and 12). On the other hand, the ability to predict the future Return by 12 past month style Return was better than 6 past month Return.

Long term and short term volatility in cash flow, can prepare relevant information in stock Return prediction. Always operational cash flow has been one of the most important items in financial statement. Results of different researches indicate that cash flows can influence on investors decision because of including information content. Researches which done by now, have focused on relative and incremental and have had lower intonation on short term and long term cash flow volatilities. This research wants to study information content of short term and long term cash flow volatilities.Cash flows annual volatilities percent relative to prior period considered as short term cash flow volatility (CFVOL1).Operational cash flow variances applied to calculate long term cash flow volatilities (CFVOL5). Statistical population in this research is firms listed in Tehran Stock Exchange (TSE), based on considered condition 50 firms selected over 2002-2009. Panel analyses are used for analyzing data.Findings show that volatilities of short term operational cash flows is a relevant information to predict stock Return, while volatilities of long term operational cash flows don’t have meaningful impact on stock Return. Other achievements confirm that among control variables like beta, size and BE/ME, just BE/ME have effect on stock Return.

Strength measurement of rock requires testing that must be carried out on test specimens with particular sizes in order to fulfill testing standards or suggested methods. Often, the coring process breaks up the weaker core pieces, and they are too small to be used in either index tests or conventional strength tests such as point load index (Is) and Brazilian tensile strength (BTS). One of the index tests to indirectly determine the rock strength is the block punch index (BPI) test, which requires flat disc specimens without special treatment. This study aimed to evaluate the applicability of the BPI test for Predicting the uniaxial compressive strength (UCS), BTS and IS of the sandstones by empirical equations. Also, we have compared the performance of the BPI and IS for Predicting the UCS and BTS. It was experimentally shown that BPI is a reliable method for Predicting the UCS, BTS and Is of the sandstones under study. Moreover, the results indicate that BPI could be utilized with same importance as Is for Predicting the UCS, while Predicting the BTS by Is appears to be more reliable than BPI.

The purpose of this research is to study studying the relationship between stock price synchronicity and tails of Return Distribution at Tehran Stock Exchange. The sample consists of 118 companies that have been chosen from among compaines listed in Tehran Stock Exchange during the period of 2010-2014, and hypothesis testing has been done with multiple regression based on panel data. The results of hypothesis testing show that firms with high stock price synchronicity have higher probability of generating positive tails than firms with low synchronicity, and also there is positive relation between stock price synchronicity and skewness. Investors of stocks with hig price synchronicity have lower reaction to bad news in respect to stocks with low price synchronicity. High stock price synchronicity show that market information reflected on stock Return is more, and investors suffer only systematic risk. Therefore, it is suggested that investors in Tehran Stock Exchange invest on stocks with higher stock price synchronicity and with higher information transparency.

Freezing is one of the weather phenomena that almost happen in parts of the east Azerbaijan province such as Maragheh causing significant losses in agriculture, industrial activities, and transportation. The end of this research is studying of Distribution and freezing Return periods in Maragheh synoptic station during 48 year period (1343-1391) based on the minimum daily temperature. The methods of this study is Gumbel Distribution method, three parameter log-logistic method and log-logistic method to analyzing probability of freezing parameters occurrence (starting date, ending date, length of the freezing season, length of the growing season).The Results of the computation of the spring and fall freezing Return periods show that the first freezing in the fall starts on 10 Aban and the last freezing end on 17 Farvardin. Also analyzing of the beginning and end freezing trend shows that fall's freezing have an ascending trend, in other words, the dates of the occurring fall freezing goes ahead and spring's freezing have a descending trend. It means that, the dates of spring freezing go back. Therefore, length of the cold season according to the global warming becomes shorter.

Predicting Returns with the least error is one of the most important issues in financial markets that has been considered by many researchers in recent decades . Traditional linear and nonlinear models due to the inefficiency of linear models in market turbulence, the lack of correct extraction of the conditional Distribution form of data due to the failure to record the conditional Distribution dynamics in nonlinear models and the existence of limiting assumptions, it lacks the ability to predict Returns in different market conditions. In order to eliminate the disadvantages of traditional models, in the present study using a new time-variable method called generalized autoregressive score (GAS) in order to predict the Distribution of Return of the total index of the stock exchange during the period 2010 to 2020 and for Bitcoin during the period 2014 to 2020. The results of modeling for the two assets by the new GAS model are compared with the results of the GARCH and AR models and their performance is tested for inside and outside the sample. The results show that in order to predict the daily Return, the overall index of the new GAS model has a better performance and in order to predict the daily Return of bitcoin, the GARCH model has been preferred.

As a cultural-artistic product, cinema plays a significant role in creating culture and filling the free time of people in society. The main problem of the research is to identify the contact points of the 5 stages (awareness, attraction, question, action, and support) affecting the customers' trip to the cinema, which can be effective in Predicting the Return rate of moviegoers. In the first step, using the content analysis method, 27 points were extracted as the contact points of moviegoers and placed in the 5A framework, and a research questionnaire was designed, the reliability and validity of which were confirmed using Cronbach's alpha and confirmatory factor analysis, respectively. In the second step, the artificial neural network model was used to predict the Return rate of moviegoers. For this purpose, 450 questionnaires were randomly distributed among adult moviegoers in Gorgan city, of which 421 questionnaires were found to be usable. The application of neural networks showed that by using the identified contact points based on the 5A framework in the customer journey, the Return rate of moviegoers can be predicted with an accuracy of 0.921. The results of the sensitivity analysis showed that the five stages of awareness (virtual advertisements), attraction (film advertising banner), question (opinions of other participants), action (type of personnel treatment), and support (repurchase) as the best predictors of consumer behavior. The results showed that the journey map of moviegoers is complex, highly sensitive, spiral, and continuous, where different touch points are touched by customers, which can affect their Return rate.1. IntroductionAs a cultural-artistic product, cinema plays a significant role in creating culture and filling the free time of people in society. Examining the purchase behavior of cinema industry consumers is important for several reasons: 1- Cinema is one of the main options for spending leisure time of many people in the society, so knowing the purchasing behavior of cinema consumers will be important for its development and expansion. On the other hand, leisure can have a positive effect on people's living conditions and enjoyment of life. 2- One of the newest and at the same time popular branches of art for spending free time, which is also known as the seventh art, is cinema, which has gained a special and influential place among the cultural and artistic works among different sections of the society in our country. The issue increases the need to know more about this market, especially from the point of view of consumers.2. Literature Review Today, one of the key goals of organizations is to create and manage customer experience (Lemon & Verhoef, 2016). This map identifies the company's most important customer touch points in each target market, shows the different decision paths taken by these audiences, and enables the marketer to measure the overall value of each touch point (Berman, 2020). The mapping process identifies weaknesses in a company's marketing strategy by comparing actual versus desired sequencing across devices (PC and smartphone), departments (sales and customer service), and channels (web and store) (Berman, 2020). One of the fields whose customers and consumers have received less attention in past researches is the cultural field, especially cinema. In the present research, it was tried to identify the contact points that the moviegoers show during their shopping trip to the cinema, from the time they are aware of the screening of a movie to their behavior after watching the movie and then based on these contact points, she predicted the rate of their Return to the cinema. According to the mentioned cases, the current research seeks to answer the following questions in two successive and interconnected qualitative and quantitative phases: 1- What contact points do moviegoers touch during their shopping trip to the cinema? 2- Based on the identified contact points, how accurately can the Return rate of moviegoers be predicted? 3-Which one of the identified contact points has a greater effect on increasing the accuracy of the predictive model and the customer Return rate to the cinema?3. Methodology The current research is descriptive-analytical and in the form of cross-sectional studies. In order to fulfill the objectives and answer the questions of this study, a mixed research method was used. This research is of an exploratory type, which includes two related parts: in the first step of the research, through in-depth semi-structured interviews, the customer journey map when using the cinema was drawn based on the 5 A framework. In the second step of the research, in order to analyze the statistical population, artificial neural network predictive methods were used in data mining. The statistical population of the research in the qualitative part included adult viewers who had the experience of watching a movie in the cinema at least once in the last 6 months. Content analysis and theoretical sampling method were used to collect data in the qualitative stage, and a total of 15 interviews were conducted until theoretical saturation was reached. The statistical population of the research in the quantitative part was the adult audience in three cinemas in Gorgan city. Due to the unlimited population of the research, the formula of the unlimited population was used to determine the sample size, and the minimum sample size was 384. A random sample was distributed in the cinema halls of Gorgan city, 421 of which were found to be usable after the initial review, and the analysis was done on them. In order to collect data, a questionnaire was used, the first part of the questionnaire was related to demographic information of the respondents, gender, marital status, income, education and age. In the second part, customer journey stages were measured based on the 5A framework. In order to determine the reliability of the used tool, Cronbach's alpha was used, and the results indicate that the current questionnaire has adequate reliability, so that the categories of the 5A framework have reliability of 0.812, 0.770, 0.699, and 805, respectively. 0.821 and the reliability of the whole questionnaire is 0.861. In order to check the validity of the questionnaire, confirmatory factor analysis was used. According to the significant number related to the research questions, which have a number higher than 1.96, it can be concluded that all the questions have the necessary validity to measure the related variable. In order to identify the audience's points of contact with the cinema during their journey, based on the 5A framework, interview questions were asked to the audience in the form of a semi-structured interview. In order to collect data, basic individual general questions and open-ended questions were used in line with the purpose of the research, and the questions were asked in line with the 5 stages of the customer's journey to the cinema, and detailed explanations were asked from the interviewee in the voice of need. The most important questions asked in the qualitative section are as follows: 1- In what way do you find out about the showing of a movie in cinemas in the city? 2- What factor/factors attract and encourage you to watch a movie? 3- What factor/factors influence your decision to go to a cinema and see a movie? 4- What factor/factors are important to you in the cinema environment from entering to exiting? 5- What kind of behavior do you show after watching the movie and leaving the cinema? In order to analyze the data, after each interview, the audio file was carefully read the handwritten and the text of the interviews several times. Meaningful units were identified and summarization of meaningful units was done with a description close to the text. Then, a list of codes was prepared and by reviewing, reviewing the meaning, determining the similarities and differences in the meaning, similar codes were placed on a more abstract level by deductive and inductive methods, and by continuous comparison, acceptable subclasses and classes were obtained. Marketing professors were requested to review the desired category and codes. The results of extracting the codes in the 5 categories (awareness, attraction, questioning, action and support) led to the identification of 24, 18, 21, 23 and 15 unique codes, respectively, and each of these codes is placed in its own subcategory.4. Results The results of the sensitivity analysis showed that the five stages of awareness (virtual advertisements), attraction (film advertising banner), question (opinions of other participants), action (type of personnel treatment), and support (repurchase) as the best predictors of consumer behavior. The results showed that the journey map of moviegoers is complex, highly sensitive, spiral, and continuous, where different touch points are touched by customers, which can affect their Return rate. The journey map of moviegoers in the research showed that the customer journey map is a continuous spiral flow and its separation into the above five stages does not mean that these five stages are separate from each other and a customer from the awareness stage to becoming a supporting customer is a continuous flow and It goes through a stage. The application of artificial neural network about moviegoers showed that according to the 27 points identified in the journey of moviegoers, it is possible to predict the rate of their Return to the movie theater with an accuracy of 0.921.5. ConclusionAlthough in the journey maps designed in 4th generation marketing, customer-oriented instead of company-oriented are emphasized, but company-oriented points and points that are controlled and monitored by the company cannot be ignored, and part of the contact points touched by customers willingly or unwillingly. It is still available to service companies and institutions. Customers start their shopping trip to the cinema with the awareness stage, part of which can be the individual's personal awareness such as previous experience, and some contact points may be available to the service provider, so it is necessary to pay attention to this point. In the awareness stage, the results of the sensitivity analysis showed that the two contact points of virtual advertising and previous experience play a significant role in informing the audience about a cultural event such as cinema. In the attraction stage, the results of the sensitivity analysis showed that the two contact points of the film advertisement banner and the film advertisement teaser play a significant role in attracting the audience to the cinema compared to other contact points. In the question stage, the results of the sensitivity analysis showed that the two points of view of other participants and virtual space play a significant role in answering personal questions and doubts, and the moviegoer will refer to these sources if they have questions. In the action phase, the results of the sensitivity analysis showed that the two points of contact between the personnel and the waiting room and the movie theater will play a significant role in the action phase compared to other contact points. In the action phase, the results of the sensitivity analysis showed that the two points of repurchase and loyalty to other contact points will play a significant role in the support phase, and according to the existing conditions, moviegoers in the statistical population are far from becoming patrons.