Price forecasting is an integral part of economic decision making. Forecasts may be used in numerous ways; specifically, individuals may use forecasts to try to earn income from speculative activities, to determine optimal government policies or to make business decisions. The importance of this topic is caused by instability in the world economy and stock markets; there is a growing interest i...

The increased popularity of financial time series forecasting in recent times lies to its great importance in predicting the best stock market timing. In this paper, we develop the concept of a pattern modeling and recognition system for predicting future behavior of time series using local approximation. In order to improve the performance of this system, we propose a systematic and automatic ...

In this study, we explored data from StockTwits, a microblogging platform exclusively dedicated to the stock market. We produced several indicators and analyzed their value when predicting three market variables: returns, volatility and trading volume. For six major stocks, we measured posting volume and sentiment indicators. We advance on the previous studies on this subject by considering a l...

this research applies and compares the market leverage lally method, ibbotson and sinquefield method and siegel method, to present alternative measures for market risk premium (mrp) estimation and test forecasting power of these methods in calculating expected rate of return. the higher level of leverage implies greater risk of investment in a specified stock, so higher return is expected by in...

This study examines several dynamic heteroskedastic factor model specifications to test for the confidence set of model parametrizations that best incorporate economy-wide information for forecasting stock market volatility. To this end, diffusion indices (i.e. factors) are distilled from two large sets of US excess stock returns and macroeconomic variables. Using 40 years of data, the main emp...

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second ha...

This paper develops and estimates a heteroskedastic variant of Campbell s [Campbell, J., 1993. Intertemporal asset pricing without consumption data. American Economic Review 83, 487–512] ICAPM, in which risk factors include a stock market return and variables forecasting stock market returns or variance. Our main innovation is the use of a new set of predictive variables, which not only have su...

The paper briefly analysis the methods used in forecasting of the stock market quotations, from the classic methods, used by the fundamentalists and chartist annalists, to the newest methods. The practical situations, where the analyzed methods are suitable, are also indicated.

Stock market prediction is essential and of great interest because successful prediction of stock prices may promise smart benefits. These tasks are highly complicated and very difficult. Many researchers have made valiant attempts in data mining to devise an efficient system for stock market movement analysis. In this paper, we have developed an efficient approach to stock market prediction by...

The goal of this paper is to study different techniques to predict stock price movement using the sentiment analysis from social media, data mining. In this paper we will find efficient method which can predict stock movement more accurately. Social media offers a powerful outlet for people’s thoughts and feelings it is an enormous ever-growing source of texts ranging from everyday observations...