when the past observations are correlated with future observations and their correlation is significant, the time series has long memory. in this paper the contagion effect of volatilities, with consideration of long-run effect, is investigated. the basic model is bekk (1, 1) and fbekk (1,d,1), model extended long-run memory parameter (d) is considered and estimated. furthermore in this paper p...

We propose a deep learning method for eventdriven stock market prediction. First, events are extracted from news text, and represented as dense vectors, trained using a novel neural tensor network. Second, a deep convolutional neural network is used to model both short-term and long-term influences of events on stock price movements. Experimental results show that our model can achieve nearly 6...

Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict  the  stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using  Meta Heuristic Alg...

The purpose of the present study is to examine the dynamic long run and the short run relationship between stock price and a set of macroeconomic variables for Indian economy using monthly data from April 2004 to July 2014. The long run relationship is examined by implementing the ARDL bounds testing approach to co-integration. VECM method is used to test the short and long run causality and Va...

This study applied a prediction-based portfolio optimization model to explore the results of portfolio predicament in the Tehran Stock Exchange. To this aim, first, the data mining approach was used to predict the petroleum products and chemical industry using clustering stock market data. Then, some effective factors, such as crude oil price, exchange rate, global interest rate, gold price, an...