In this paper we propose and examine new approaches in smoothing transition autoregressive (STAR) models. Firstly, a new STAR function is proposed, which is the hyperbolic tangent sigmoid function. Secondly, we propose Feed-Forward Neural Networks Smoothing Transition Autoregressive (FFNN-STAR)...

We investigate whether long-term co-movements among the prices of precious metals commodity futures contracts can be observed. The past literature on agricultural commodity futures prices obtains the mixed results. We find that there is no long-term interdependence among the prices of the four...

This paper analyses the validity of the weak-form market efficiency, using the random-walk hypothesis for the six industrial base metals - copper, aluminium, zinc, nickel, tin and lead - traded at the London Metal Exchange. I analyse the behaviour of daily and weekly prices of the daily rolling...

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...

The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing...