The technical analysis and machine learning have been integrated in stock trading signal forecasting. And it has been proved that there are some weaknesses in technical analysis because of the complex environment in the stock market. In our prediction system, web news media sentiment analysis is regarded as a supplementary way to cover the shortage of technical analysis. It is considered to bri...

One of the challenging research problems in the domain of time series analysis and forecasting is making efficient and robust prediction of stock market prices. With rapid development and evolution of sophisticated algorithms and with the availability of extremely fast computing platforms, it has now become possible to effectively extract, store, process and analyze high volume stock market tim...

Traditional document representation for classification follows Bag of Words (BoW) approach to represent the term weights. The conventional method uses the Vector Space Model (VSM) to exploit the statistical information of terms in the documents and they fail to address the semantic information as well as order of the terms present in the documents. Although, the phrase based approach follows th...

Investment strategies usually aim at achieving maximum profitability what, according to current management theory (Refenes, Burgess, & Bentz, 1997), can be obtained by the construction of well balanced investment portfolios that seek to maximum return and minimum risks. In order to provide users with information to plan a good investment portfolio, we present an e-commerce Web site solution tha...

Analysis and prediction of stock market time series data has attracted considerable interest from the research community over the last decade. Rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, and availability of high-performance hardware has made it possible to process and analyze high volume stock market time series data effectively, in ...

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility. Employing German DAX-index retu...

Study of volatility has been considered by the academics and decision makers dur-ing two last decades. First since the volatility has been a risk criterion it has been used by many decision makers and activists in capital market. Over the years it has been of more importance because of the effect of volatility on economy and capital markets stability for stocks, bonds, and foreign exchange mark...

As the capital markets evolve and expand, more and more data is being created daily. This explosion of data has made the flow of information much more efficient. As market participants act on this information flow, it drives market prices to more efficient values, . One of the driving forces in this march to efficiency, is the application of various algorithmic learning techniques on both marke...

Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single method, traders need to use various forecasting techniques to gain multiple signals and more information about the futur...