Stock market analysis is one of the most important and hard problems in finance analysis field. Recently, the usage of intelligent systems for stock market prediction has been widely established. In this paper, a PSO based selective neural network ensemble (PSOSEN) algorithm is proposed, which is used for the Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index analysis. In...

A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to ...

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...

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...

We utilized asymmetric multifractal detrended fluctuation analysis in this study to examine the asymmetric multifractal scaling behavior of Chinese stock markets with uptrends or downtrends. Results show that the multifractality degree of Chinese stock markets with uptrends is stronger than that of Chinese stock markets with downtrends. Correlation asymmetries are more evident in large fluctuat...

A new framework for analysing time series data called Time Series Data Mining (TSDM) is introduced. This framework adapts and innovate data mining concepts to analysing time series data. It creates a set of methods with the growing deployment of a large Number of sensors, telemetry devices and that reveals hidden temporal patterns that are characteristic and predictive of time series events. Th...

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 ...

in this study, we focused on tehran stock exchange market analysis based on applying moving average rules. the tehran stock exchange in the middle east has evolved into an exciting and growing marketplace where individual and institutional investor trade securities of over 420 companies. in an attempt to examine the ability to earn excess return by exploiting moving average rules, the average a...