Financial market forecasting particularly stock market forecasting is a considerable debate that confront to forecast failure and model break down when structural breaks in trends occur.This paper discusses the modeling to predict stock return under structural breaks and investigate new approaches of forecasting in this condition. This study proposes a taxonomy for research area in forecasting under structural breaks to suggest further studies. We use literature survey as methodology of the research and categorizes the methods, models, and results of the recent researches in stock market forecasting. Consequently, it provides three categories of strategies to forecast stock return under structural breaks. First strategy, called economically motivated model restrictions, uses Financial theories as signs to adjust the parameters of models in outsample periods. Second strategy, known as regime shift, uses a Markov chain transition matrix to model structural breaks in time series. Third strategy applies mix of quantitative models and qualitative surveys to predict future of Financial markets. The proposed strategies are applicable in Tehran stock exchange under uncertainty conditions.

The purpose of this paper is to predict stock prices using Hybrid GA-SVM Algorithm. Predicting time series such as stock price forecasting is one of the most important issues in Financial field. In real life, identifying time series movements in stock price indices is very complex. Therefore, the use of a classical model alone cannot accurately predict stock price indices. Hence, by using combined methods, uncertainty in forecasting can be reduced. In stock price forecasting in Financial sector, more than 100 indicators have been created to understand stock market behavior, so, identifying the appropriate indicators is a challenging problem. One of the techniques that has recently been studied for serial forecasting is support regression Vector (SVR) or machine support vector (SVM). This study uses the GA-SVM hybrid algorithm to predict the stock price index. Experimental results show that Hybrid GA-SVM Algorithm provides a more appropriate and promising alternative to stock market forecasting.

By delivering service to establishing reasonable relationships with other industrial, manufactural, agricultural and service sectors through collecting small insurance premiums from insureds and indemnifying them timely. the insurance industry as a non-banking Financial institution can raise public and private capitals, and direct and invest on such Financial resources efficiently, secure production by entrepreneurs, business owners, and professionals, reduce imports from and dependence upon global markets, and hence lead to economic development. Therefore, identification of the public and private shares of this Financial market can have a substantial effect on saving Financial resources. Moreover, forecasting of this Financial market in respect to insurance premiums chargeable and payable, controlling, and directing such Financial resources can help different economic sectors make investments and implement their monetary and Financial policies in order to reach their long term economic goals. Therefore, this study has attempted to analyze in addition, forecast insurance market. With regard to the analysis and identification of the public and private market share of insurance, also the forecasting of this Financial market, policy maker can conduct the monetary and Financial policies in order to achieve their long-term economic goals. In fact the survey of the chargeable and payable premiums of insurance and then controlling and directing it Financial resources, can help different economics sectors. Introduction Insurance market, which is an important part of Financial markets in every country, plays an important role in policy making and applying macro-economic policies. Controlling availability of this Financial market and conducting liquidity of insurance market towards different parts of national economics, this market leads to the investigation and transmission of investment to economic institutions and thereby providing future perspectives of economic firms. Given the fact that during a short period of time, shock and the intensity of the movement of liquidity and capital of both the private sector and people of society in transferring from economic parts to non-economic ones are high, the recognition and introduction of information, structure and prediction of short-term market for determining appropriate policies is vital. Methodology In this work, having looked at monthly statistics data, the researchers tried to investigate factors affecting the market. It is done by using ARDL and ARMA models. In addition, their relationship will be described and estimated based on the ARDL method. On the other hand, since the prediction of Financial market is efficient and stable, we can provide a framework for achieving economic growth and development. Thus, this paper will examine the structural stability of insurance market and forecast its market based on auto regression moving average process. Finally, based on these results, applicable policies will be suggested. Results and Discussion The primary duty of this market is providing liquidity for the government, the private, and industry sectors in the form of collecting stagnant savings and the liquidity of private sector in order to finance the long-term investment projects. forecasting of this Financial market in respect to insurance premiums chargeable and payable and controlling and directing such Financial resources can help different economic sectors make investments and implement their monetary and Financial policies in order to reach their long-term economic goals. In other words, the analysis and identification of the public and private market share of insurance premiums chargeable and payable and forecasting them play a dual function in the structure of a free economy. On the one hand, it helps to increase the capital of the government and the private sector, and on the other hand, it helps the secondary market to meet the potential and actual investors. Investigation, controlling and conducting the market can lead to the Financial resources of the capital market and investment for development and growth and the policies are planned based on the goals of macroeconomic. Economists believe one of the reasons for the lack of the development of developing countries is the low level of investment. In this regard, the insurance market is the main and important center to attract these savings. Accordingly, we should identify its influential factors in the market, and thus use appropriate policies for the progression and growth of the market. Achieving the optimal growth and development of economy without mobilization of Financial resources in long term is impossible. In this regard, the position and the role of Financial market are of high importance. The insurance market, which is the market of demand and supply of Financial resources, can play a vital role when the process of supply and demand of its Financial sources is the optimal allocations. The main prerequisite for the optimal allocations of resources in the capital market is the efficiency in their performance. Conclusions and Suggestions Thus, policy makers should pay attention to the public and private market share of insurance premiums chargeable and payable, its flourishing growth and to government policies, and control the market, prevent the uncontrolled growth of liquidity, eliminate the cumbersome investment regulations and incentives tax, and finally draw a plan for implementation of economic policies. In this regard, it is essential that the country's economic managers identify macro-economic variables affecting the market, especially government-controlled variables and how their influence can lead to appropriate policies for the stimulation of the market and growth of economy.

‎forecasting price trends in Financial markets is of particular importance for traders because price trends are inherently dynamic and forecasting these trends is complicated‎. In this study‎, ‎we present a new hybrid method based on combination of the dynamic mode decomposition method and long short-term memory method for forecasting Financial markets‎. This new method is in this way that we first extract the dominant and coherent data using the dynamic mode decomposition method and then predict Financial market trends with the help of these data and the long short-term memory method‎.‎ To demonstrate the efficacy of this method‎, ‎we present three practical examples‎: ‎closing price of US Dollar to Iranian Rial‎, ‎closing prices of zob roy Isfahan stock‎, ‎and also closing prices of siman shargh stock‎. ‎These examples exhibit bullish‎, ‎bearish‎, ‎and neutral behaviors‎, ‎respectively‎.‎ It seems that the proposed new method works better in predicting the Financial market than the existing long-short-term memory method‎.

The goal of the present article is extending and developing Multiple Disciminant Analysis (MDA) method which is able to distinguish buble price in Tehran stock exchange. The principal goal of the present study is to offer model for approximating buble price and also the factors efficient to make the model work at Tehran stock exchange. In order to do so by applying separation method a sample consisting of 397 companies accepted at Tehran stock exchange were selected and information related to their price and volume of trades during years 2001 until 2009 were collected and then through performing runs test, skewness test and duration correlative test the selected companies were divided into 2 sets of with bubble price and non bubbled companies. In the next stage by investigating cumulative return process and volume of trades in bubbledted companies, the date of starting bubble price was specified and in this way the multiple discriminant analysis, and by using information related to size of company, clarity of information, ratio of P/E and liquidity of stock one year prior bubble price; a model for forecasting bubble price of stocks of companies present in Tehran stock exchange were designed. At the end the power of forecasting model was studied by using data of test set. Whereas the power of forecasting MDA model was 90.2%; the model has high power to anticipate bubble price.

The goal of the present article is extending and developing econometrics and network structure based methods which are able to distinguish price manipulation in Tehran stock exchange. The principal goal of the present study is to offer model for approximating price manipulation in Tehran stock exchange. In order to do so by applying separation method a sample consisting of 397 companies accepted at Tehran stock exchange were selected and information related to their price and volume of trades during years 2001 until 2009 were collected and then through performing runs test, skewness test and duration correlative test the selected companies were divided into 2 sets of manipulated and non manipulated companies. In the next stage by investigating cumulative return process and volume of trades in manipulated companies, the date of starting price manipulation was specified and in this way the logit model, artificial neural network, and by using information related to size of company, clarity of information, ratio of PIE and liquidity of stock one year prior price manipulation; a model for forecasting price manipulation of stocks of companies present in Tehran stock exchange were designed. At the end the power of forecasting models were studied by using data of test set. Whereas the power of forecasting logit model for test set was 92.1 %, and for artificial neural network was 94.1%; therefore both of the 2 aforesaid models has high power to anticipate price manipulation and there is no considerable difference between forecasting power of these 3 models.

The purpose of this study is to examine managers’ earnings forecasting behavior in pre and post-periods around Financial restatements. Following restatements, two conflicting incentives including “the reputation repair” and “risk avoidance by manipulating forecast bias after restatements” could be raised. In order to examine the behavior of managers in pre and post-periods around restatements, quasi-experimental research design and“Interrupted Time Series Design with Comparison Group” defining test and control groups consist of Tehran stock exchange companies (65 companies) between 2007- 2013 is used. Results show that there is no significant difference between earning forecast optimistic biases at post-restatement periods in test group versus control group.Rejecting the hypothesis suggested there is significant difference in managers’ earnings forecasting behavior between test group and control group following restatements, it is inferred that other factors beyond restatements could impact managers’ earnings forecasting behavior in test group companies.