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Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict  the  stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using  Meta Heuristic Algorithms, has led to a lot of research in the field of finance and accounting. Accordingly, the purpose of this research is to model the Stock price crash risk of listed companies in Tehran Stock Exchange using firefly algorithm and compare the results with multivariate regression as a traditional method. Of the companies listed on the stock exchange, 101 companies have been selected as samples. Initially, 19 independent variables were introduced into the model as input property of the particle accumulation algorithm, which was considered as a feature selection method. Finally, in each of the different criteria for calculating the risk Stock price crash risk, some optimal variables were selected, then using firefly algorithm and multivariate regression, the stock price crash risk was  predicted  and results were compared. To quantify the Stock price crash risk, three criteria for negative skewness, high fluctuations and maximum sigma have been used. Two methods of MSE and MAE have been used to compare the methods. The results show that the ability of meta-meta-heuristic methods to predict the risk Stock price crash risk is not  generally higher than the traditional method of multivariate regression, And the research hypothesis was not approved.

One of the basic assumptions of management accounting illustrate that costschanges has a significance Relationship with increasing and decreasing in the levelof activity, recently after being raised of sticky costs issue by Anderson and hiscolleagues this assumption was discussed. It means Increases in costs by increasingthe more activity level of reduction in costs is exchange for the reduction in thelevel of activity. Anderson et al (2003) changed the expression of Cost behavior tosticky costs. The Subject profit forecast error is one of the issues that can affectinvestment decisions to hold or transfer of shares, because purchase of shares ofthe new company's stock is riskier process than other companies; Because of itslack of trading Background, historical information related to their is low. Accordingly,in prediction two fundamental goals follow: The first one obviously is theproper planning and second one is Familiarity and deployment of predictive techniquesusing predictive techniques for decision making and problem solving process.The aim of this study was to investigate the relationship between forecasterror of earnings and sticky cost in Tehran Stock Exchange. To achieve this aim, amain hypothesis and sub-hypothesis has been proposed, and to test hypothesesdata 108 firms listed companies in Tehran Stock Exchange using systematic samplingpurposefully selected, and data of the period between 2007 to 2013 was usedfor statistical analysis. Finally, the results indicate that There is an inverse relationshipbetween sticky costs and forecast error of earnings, and also betweensticky cost of goods sold and sticky costs of sales.

Accelerating digital transformation and the basic needs of businesses to adapt to environmental transformations and complexities have doubled the necessity for extensive stakeholder interactions. Gamification is a powerful tool that facilitates stakeholder interaction and a common understanding of the vision and contributes to the sustainability of businesses. Businesses' sustainability in the digital transformation age depends on the continuous interaction of stakeholders and a comprehensive understanding of all the business pillars, which will be possible through the flow of ideas inside and outside the workplace and by providing innovative processes. In this regard, this study explores the effect of gamification on corporate sustainability by explaining the role of digital transformation, open innovation, and value co-creation. The statistical population of this study comprises top managers and experts in e-businesses who use gamification in their processes. The statistical sample included 117 managers and experts active in this field, and they were selected through convenience sampling. The data collection tool was a questionnaire reliability of which was 0.763 using Cronbach's alpha. Using Smart PLS software, the gathered data were analyzed by structural equation modeling (SEM). Gamification, with a factor of 74%, has a positive effect on open innovation, while a factor of 85% has a positive effect on digital transformation. Gamification, both directly and indirectly, creates value co-creation in businesses, and finally, value co-creation at 78% affects corporate sustainability.