Predicting trends in stock market prices has been an area of interest for researchers for many years due to its complex and dynamic nature. Intrinsic volatility in stock market across the globe makes the task of prediction challenging. Forecasting and diffusion modeling, although effective can’t be the panacea to the diverse range of problems encountered in prediction, short-term or otherwise. ...

We seek to forecast sector stock returns using established predictor variables. Existing empirical evidence focuses on market level data, and thus, data provide fertile ground for research. In addition in-sample predictive regressions, we consider recursive rolling forecasts whether such can be used successfully in a rotation portfolio. The results ten sectors eleven variables highlight that tw...

We have introduced an early warning system for volatility regimes regarding Tehran Stock Exchange using Markov Switching GARCH approach. We have examined whether Tehran Stock Market has calmed down or more specifically, whether the surge in volatility during 2007-2010 global financial crises still affects stock return volatility in Iran.  Doing so, we have used a regime switching GARCH model.  ...

Accurate stock trend prediction is a difficult job because various intricate and complex factors affect changes in price, trading volume and trends of a stock market. On a macro scale, the factors could be the overall global economic environment, industry trends, individual economic environment (business operation and competitors’ development), the amount of floating capital in the market, etc....

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...

The experts considered in this paper are neural networks whose forecasts are combined by another neural network, a gate. For regression problems such an architecture was shown to partly remedy the two main problems in forecasting real world time series: nonstationarity and overfitting. The goal of this paper is to compare the forecasting ability of gated experts (GE) with a that of a single neu...

Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies (in U.S. dollars) are related to the stock price changes of these companies. The authors’ findings indicate, in the financial services context, nega...

Stock market prediction is an important area of financial forecasting, which is of great interest to stock investors, stock traders and applied researchers. To determine the buy and sell time is one of the most important issues for investors in stock market. In this paper, a fuzzy approach using the famous candlestick method to stock market timing is investigated. Drawing candlesticks are very ...

Stock market represents an essential part of the economy in the Middle East, it is significant for shareholders and investors to estimate the stock price and select the best trading opportunity accurately in advance. This paper utilizes artificial neural network in the modeling of stock market exchange prices. The network was trained using supervised learning. Simulation was conducted for seven...

Numerous academic studies examine equity risk premium predictability based on various macroeconomic variables and price and volume based variables from stock market. In this article, we extend the frontier of the set of predictors from macroeconomic variables and stock market variables to foreign exchange market variables due to various reasons. Firstly, foreign exchange market reflects various...