Forecasting the short-term trend of a stock market has long been a big challenging task. Parameters of stock markets, including open/close prices, daily-high/low prices and trading volumes, were frequently used in previous studies to forecast the stock market. Basing on the fact that the moving direction of these parameters have certain inertia within short-term period, we here explored the pot...

The application of Internet-based virtual stock markets (VSMs) is an additional approach that can be used to predict shortand medium-term market developments. The basic concept involves bringing a group of participants together via the Internet and allowing them to trade shares of virtual stocks. These stocks represent a bet on the outcome of future market situations, and their value depends on...

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...

T his study examines how oil price shocks interact with the stock market index within a nonlinear autoregressive distributed lag model in Iran. Based on quarterly data for the period from 1991 to 2017, the findings revealed statistically significant evidence of short-run and long-run asymmetric behavior of stock market index in response to the positive a...

In this papeT we study the performance of the GARCH model and two of its non-linear modifications to forecast weekly stock market volatility. The models are the Quadratic GARCH (Engle and Ng. 1993) and the Glosten. Jagannathan and Runkle (1992) models which have been proposed to describe, for example, the often observed negative skewness in stock market indices. We find that the QGARCH model is...

Original scientific paper As the stock market volatility is highly nonlinear, coupling and time varying, it is difficult to predict by the traditional forecasting methods. For explaining the existing problems of the current volatility forecasting method, we use the model based on the weighted least squares support vector regression (WLSSVR) method to predict the stock index volatility in this p...

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...

Abstract: It is very important to minimize the risk in portfolio selection. For minimizing risk of portfolio at a given expected returns, it is efficient to compose portfolio with stocks which have low cross-correlation among them. In this regard, forecasting the cross-correlations among stock prices has attracted much interest among investors and financial market researchers. Most of studies i...