Economic indicators

Governments always affect the economic environment as legislators in the field of business. The economic conditions governing the market and business require different conditions and contexts for decision making and corporate execution practices. Companies and managers administering them under environmental conditions, achieve their goals by employing various earnings management methods. Hence, the present study examines the effect of governments’ economic performance on the earnings management methods used in listed companies of Tehran Stock Exchange (2004-2016) for a sample of 16 industries and 271 companies. To test the hypotheses, multivariate regression model was used. The results showed that during the research period, companies managed earnings, and while more than 70% of companies used the accrual earnings management method, there was a relationship between annual economic indicators and real earnings management, and the change in general level of prices and the political connections of states have affected the relationship. Also, the accruals-based earnings management method occurred independent of annual economic indicators and there is a significant relationship between governments changes and earnings management methods.

Housing can be considered as one of the most important needs of each family and the provision of suitable housing for people has always been considered as one of the indicators of progress in any country. Because of the specific attitude to the issue of housing in Iran, in other words, turning it into an investment, the major part of the households' income has been accounted for housing. Given the importance of the matter, housing in Iran is affected by factors that often are considered as markers to assess the recession or boom in this sector. Among these factors, sociocultural, economic, and demographic indicators can be noted that play a very important role in the formation of housing patterns and are considered a basis to assess mutual impressions of each of the above factors in this sector. This paper is aimed to exامینe how each of the economic, cultural, social, and demographic indicators has effect on the current housing situation in Iran. This paper attempts to analyze independent and interactive effects of each of the indicators on the housing sector by the method of content analysis and library and then continues to find ways to improve what is seen in the housing typology. The results suggest that changes in cultural indicators have the most profound impact on housing typology in long-term period, while changes in economic factors affecting the housing pattern is very transient, superficial, and short-term.

In the decade of 1979-1988, there was no development plan in Iran. During 1989-2022, six development plans implemented in Iran’s economy. The assessment of development plans conducted based on the performance and the results of economic indicators over the period. Economic Growth over the course of development plans was volatile. There has been a double-digit inflation rate over the course of the development plans and Productivity and the distribution of income indices were volatile. The challenges of planning in Iran include lack of common understanding on basic concepts of development plans, challenges of comprehensive plan, essential change due to external shocks, time span, parallel initiatives, coordination failure and lack of independent evaluation entity.

Objective: In today's interconnected global economy, changes in one market can have ripple effects across related markets, making it essential for economic and financial policymakers and experts to accurately predict these mutual impacts. Various methods have been developed to forecast the impact and mutual impressions of financial markets. In this study, a generic framework is proposed for forecasting economic and financial market indicators using the interrelationship of shocks among global financial markets and a system dynamic approach. Methods: To demonstrate the stages of the proposed generic framework and system dynamics modeling, as an example, the study forecasts the Iranian economic and the Tehran Stock Exchange indicators using their interactions with eleven major global financial markets, including London, Tokyo, Shanghai, Frankfurt, Paris, Milan, SIX Swiss, Istanbul, Korea, Bombay Stock Exchanges, and Dubai Financial Market. The New York Stock Exchange index return is used as a stimulant or driver for the other stock exchanges in the model.Results: The results indicate that the proposed forecasting model successfully predicted the Iranian economic and the Tehran Stock Exchange indicators. Furthermore, the study finds that while Iranian exports are sensitive to global financial markets, the sensitivity of imports and production returns to global financial markets is low. Conclusions: The proposed generic framework and system dynamics modeling can provide valuable insights for predicting different economies using their interactions with the global economy and finances.