In this paper, we analyze momentum strategies that are based on reward–risk stock selection
criteria in contrast to ordinary momentum strategies based on a cumulative return criterion. …

A variety of new and powerful time series tools are available to test for predictive components
in data which previously have been regarded as weak form efficient. The key issue is …

… There are two neural network models investigated in this research: backpropagation networks
and counterpropagation networks. There are three major steps in the neural network…

The benchmark performance of neural network models in exchange rate forecasting has
been established in a number of recent papers. In this paper, we drop a number of customary …

In this study, the properties of hidden nodes in backpropagatibn networks with respect to
their contribution to the solution of the problem after initial training are examined. Using a …

The paper explores the use of neural networks in analyzing the effects of advertising and
promotion in the retail business. In retailing, one of the key problems is the optimal allocation of …

… The data sets in this study are divided into two parts: a training or estimation set using the
historically oldest data with a size of approximately two thirds of the observations with the …

It has been widely recognised that the randomness of a stock market may actually be an
indicator of an underlying strange attractor which has a fractal structure and supports chaotic …

Regressions are the probabilistic equivalent of functions in the deterministic domain. A
regression expresses a functional link between a random variable Y and one or more …

In this article, the authors construct portfolios by integrating quantitative factors in the portfolio
optimization problem using their normalized score values in a constraint. The resulting …