Stock data analysis for price forecasting and trend prediction has been a challenging problem that attracts researchers from different fields. Some use statistical methods, while others use neural network based approaches. This paper reports on a preliminary study on stock market data analysis using a hyperspace data mining approach that is built upon a projective geometrical method. Discussion...

For common people, stock investing is one popular way to manage their property. As Information Technology (IT) has risen in recent years, every security company has analyzed computer systems for their customers by developing their own investing. Taiwan is an island nation, and the economy relies on international trade deeply. The fluctuations of international stock markets will impact Taiwan st...

An accurate safety stock forecasting model has both academic and practical significance to inventory management. Reliable safety stock forecasting can not only help in making right decision but also in decreasing the cost and thereby increasing the profit significantly. Therefore in this paper, Artificial Neural Network (ANN) along with Clustering techniques, have been applied to predict the sa...

Stock market forecasting research offers many challenges and opportunities, with the forecasting of individual stocks or indexes focusing on forecasting either the level (value) of future market prices, or the direction of market price movement. A three-stage stock market prediction system is introduced in this article. In the first phase, Multiple Regression Analysis is applied to define the e...

---------------------------------------------------------------------***--------------------------------------------------------------------ABSTRACTStock Market has high profit and high risk features which tells why its prediction must be close to accurate. The main issue about such data sets is that these are very complex nonlinear functions and can only be learnt by a data mining methods to r...

This paper studies the role of detrended wealth in predicting stock returns. We call a transitory movement in wealth one that produces a deviation from its shared trend with consumption and labor income. Using U.S. quarterly stock market data we find that these trend deviations in wealth are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find ...

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN)...

Nowadays, forecasting and techniques used to obtain the forecasts are very important. The term of forecast means to make an inference (predict) about the future on the basis of existing information. Especially, forecasting of stock market data are frequently used in time series analysis literature. Moreover, fuzzy time series forecasting methods have been widely used in the analysis of stock ma...

Forecasting financial markets is an important issue in finance area and research studies. On one hand, the importance of prediction, and on the other hand, its complexity, have led to huge number of researches which have proposed many forecasting methods in this area. In this study, we propose a hybrid model including Wavelet Transform, ARMA-GARCH and Artificial Neural Network (ANN) for single-...

Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the machine learning technique used, the forecasting timeframe, the input variables used, and the evaluation techniques emplo...