In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods...

The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neo-classical economic theory assumes that individuals form expectations rationally, thus enforcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that indi...

In this paper, we present the advantages of Maximum overlapping Discrete Wavelet Transform (MODWT) in improving the forecasting accuracy financial time series data. Amman stock market (ASE) in Jordan was selected as a tool to show the ability of MODWT in forecasting financial time series, using Banking sector. Experimentally, this article suggests a novel technique for forecasting the banking d...

The purpose of resent research is to analysis and compares performance evaluation models of selected investment companies in Tehran Stock Exchange Market in the field of their portfolio management. The duration of research was between years 2009-2014. Statistical society the research is consisting of all active investment companies in in Tehran Stock Exchange Market which were 30 companies. Vol...

Artificial stock markets are models of financial markets used to study and understand market dynamics. Agent Based Artificial Stock Markets can be seen as any market model in which prices are formed endogenously as a result of participants’ interaction and in which the representation of participants varies from simple equations of forecast functions to intricate software components endowed with...

Under the growth of the stock market sector and the widespread of stock market applications, the stock market prediction has become one of the most important and challenging tasks in the stock market. Many data mining techniques are exploited to predict the stock prices in order to help investors in making investing decisions. One of the most common and widely used techniques is Artificial Neur...

The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine ...

The stock market, which has been investigated by various researchers, is a very complicated environment. So far, most of the research only concerned the quantitative factors, like index in open or volume, instead of qualitative factors, say political effect. However, the latter always plays a very important role in the stock market environment. Therefore, this research proposes illl intelligent...

The stock market is the most popular investing places for users. Because of its expected high profit. Recently forecasting stock market returns gaining more attention. The prediction of stock markets is regarded as a challenging task. Data analysis is the way of predicting future value. if future stocks prices will increase or decrease. The main objective of this paper is to predict future stoc...

Stock markets are affected by many uncertainties and interrelated economic and political factors at both local and global levels. The key to successful stock market forecasting is achieving best results with minimum required input data. To determine the set of relevant factors for making accurate predictions is a complicated task and so regular stock market analysis is very essential. More spec...