Forecasting stock exchange rates is an important financial problem that is receiving
increasing attention. During the last few years, a number of neural network models and …

This paper reports the results of the NN3 competition, which is a replication of the M3
competition with an extension of the competition towards neural network (NN) and …

Inspired by the commonly held view that international stock market volatility is equivalent to
cross‐market information flow, we propose various ways of constructing two types of …

This paper analyzes volatility models and their forecasting abilities in the presence of jumps
in two crude-oil markets-Brent and West Texas Intermediate (WTI)-between January 6th …

Large one-off events cause large changes in prices, but may not affect the volatility and
correlation dynamics as much as smaller events. In such cases, standard volatility models …

Recently, support vector machine (SVM), a novel artificial neural network (ANN), has been
successfully used for financial forecasting. This paper deals with the application of SVM in …

Abstract The Time-delay Added Evolutionary Forecasting (TAEF) approach is a new method
for time series prediction that performs an evolutionary search for the minimum number of …

Purpose The present paper aims to conduct a comprehensive scientific mapping of
exchange rate forecasting, highlighting trends, developments, and methodological changes …

Purpose In initial public offerings (IPOs), the media plays a pivotal role by disseminating the
information to the investors who generally lack the expertise to understand the information …

Financial asset prices occasionally exhibit large changes. To deal with their occurrence,
observed return series are assumed to consist of a conditionally Gaussian ARMA–GARCH …