The stock market is a complex and dynamic system with noisy, non-stationary and chaotic data series. Prediction of a financial market is more challenging due to chaos and uncertainty of the system. Soft computing techniques are progressively gaining presence in the financial world. Compared to traditional techniques to predict the market direction, soft computing is gaining the advantage of acc...

The use of Artificial Neural Networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary ti...

We analyze the network structure of lagged correlations among daily financial news sentiments and returns of financial market indices of 40 countries from 2002 to 2012. Using a spectral method, we decompose the network into bipartite sub-structures, and show that these sub-structures are relevant to the performance of prediction models, bridging concepts from network theory and time series anal...

The goal of this paper is to identify key financial and accounting variables that can accurately classify Green IT leaders against Green IT followers. The detailed objectives are: (i) to build and compare single and meta classifiers to identify the relationship between environmental performance and financial performance; (ii) to select and interpret a final prediction model with a smaller set o...

The aims of the present study are developing a financial stability index (FSI) using banking indices to measure financial stability in Iran, and examining the relationship between financial stability and macroeconomic variables for policymaking. To these ends, we have employed principal-component analysis, out of sample forecasting, Autoregressive Integrated Moving Average (ARIMA) method, and V...