Stock market prediction (SMP) is challenging due to its uncertainty, nonlinearity, and volatility. Machine learning models such as recurrent neural networks (RNNs) have been widely used in SMP and have achieved high performance in terms of "minimum error". However, in the context of SMP, using...

The numerous literatures have recorded the closely connection across commodity and stock markets. This study empirically examines the role of 7 metal commodities (gold, silver, aluminum, plumbum, copper, zinc and nickel) in predicting G7 stock volatility. The results of individual factor...

Using Thailand stock market data, we find that prospect theory has strong predictive power for returns in the Thailand stock market. This predictive power is strengthened during crises and bear and bull markets. The loss aversion component is the main contributor to the increased predictive...