Prediction of stock market is a long-time attractive topic to researchers from different fields. In particular, numerous studies have been conducted to predict the movement of stock market using machine learning algorithms such as support vector machine (SVM) and reinforcement learning. In this project, we propose a new prediction algorithm that exploits the temporal correlation among global st...

This paper presents performance analysis of hybrid model comprise of concordance and Genetic Programming (GP) to forecast financial market with some existing models. This scheme can be used for in depth analysis of stock market. Different measures of concordances such as Kendall’s Tau, Gini’s Mean Difference, Spearman’s Rho, and weak interpretation of concordance are used to search for the patt...

There is an old Wall Street adage goes, ‘‘It takes volume to make price move”. The contemporaneous relation between trading volume and stock returns has been studied since stock markets were first opened. Recent researchers such as Wang and Chin [Wang, C. Y., & Chin S. T. (2004). Profitability of return and volume-based investment strategies in China’s stock market. Pacific-Basin Finace Journal...

Forecasting the behavior of the financial market is a nontrivial task that relies on the discovery of strong empirical regularities in observations of the system. These regularities are often masked by noise and the financial time series often have nonlinear and non-stationary behavior. With the rise of artificial intelligence technology and the growing interrelated markets of the last two deca...

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...

Stock market plays a significant role and has greater influence on basic economic energies of a country. Rapid changes in the stock exchange market with high dimensional uncertain data make the investors to look for effective forecasting using prediction mining techniques. The high dimensional stock data are classified into profitability, stability, cash flow and growth rate but does not deal c...

The daily fluctuation trends of a stock market are illustrated by three statuses: up, equal, and down. These can be represented by a neutrosophic set which consists of three functions—truth-membership, indeterminacy-membership, and falsity-membership. In this paper, we propose a novel forecasting model based on neutrosophic set theory and the fuzzy logical relationships between the status of hi...

Nowadays, stock market is the one of the major sources of raising resources for India and is act as a key driver for economic growth of a country. The stock market forecasting is a very difficult and highly complicated task because it is affected by many factors such as economic conditions, political events and investor’s sentiment etc. The stock market series are generally dynamic, nonparametr...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

This paper presents a study of artificial neural nets for use in stock index forecasting. The data from a major emerging market, Kuala Lumpur Stock Exchange, are applied as a case study. Based on the rescaled range analysis, a backpropagation neural network is used to capture the relationship between the technical indicators and the levels of the index in the market under study over time. Using...