Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm i...

In this paper we investigate the effect of oil price shocks on stock market index in Iran, by using of a structural VAR (SVAR) approach. We used four variables in the model namely Kilian index, global oil supply, real oil price and real stock market index. The data are monthly and spanning the period 1997M10-2014M12. We identify the effect of four different shocks on stock market including oil ...

In many empirical studies, the dynamic relationship among energy sector variables (such as, oil, electricity, gasoline, coal, renewable energy, etc.) and economic variables (such as; financial markets, real economy and the overall economy) are studied. Oil price changes may affect the economic variables more of oil importer countries then oil exporter countries especially emerging markets. In a...

this study examined the relationship between market monitoring cost consist the business disclosure index, the index of perception of corruption, rule of law and regulations quality, and also pointed out the technology index and exercise price limit rules that are in stock. in this research the relationship between the above indicators to price limit rules applied during the period 2005-2010 in...

The jamor purpose of the present research is to predict the total stock market index of Tehran Stock Exchange, using a combined method of Wavelet transforms, Fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers.To do so, first the prediction was made by neural network, then a series of price index was decomposed by w...

Currently, stock price forecasting is carried out using either time series prediction methods or trend classifiers. The trend classifiers are designed to predict the behaviour of stock price’s movement. Recently, soft computing methods, like support vector machines, have shown promising results in the realization of this particular problem. In this paper, we apply several prototype generation c...