In this paper we examine feed-forward neural networks using genetic algorithms in the training process instead of error backpropagation algorithm. Additionally real encoding is preferred to binary encoding as it is more appropriate to find the optimum weights. We use learning and momentum rates...

Since stock markets are volatile, dynamic and complicated, forecasting stock market return is considered as a challenging task. Nevertheless, researchers have developed various linear and non linear methods for effective forecasting. Among these neural networks are most suitable for forecasting...

Recently, Donaldson and Kamstra (1997) proposed a class of NN-GARCH models which are extended to a class of NN-GARCH family by Bildirici and Ersin (2009). The study aims to analyze the nonlinear behavior and leptokurtic distribution in petrol prices by utilizing a newly developed family of...

This paper presents a computational approach for predicting the S&P CNX Nifty 50 Index. A neural network based model has been used in predicting the direction of the movement of the closing value for the next day of trading. The model presented in the paper also confirms that it can be used to...

Este documento proporciona un acercamiento teórico a los Sistemas de Redes Neuronales Artificiales, así como a los software en los que se pueden realizar estás implementaciones y mostrar la manera en que esta metodología puede constituirse como una método para predecir series de tiempo...

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from...

One of the main challenges for life actuaries is modeling and predicting the future mortality evolution. To this end, several stochastic mortality models have been proposed in literature, starting from the pivotal approach of the Lee-Carter model. These models essentially use the ARIMA processes...

In this work we use Recurrent Neural Networks and Multilayer Perceptrons, to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market...

In this work we show a briefly presentation of four approaches to opinion polls. The example we present here, is referred on exit polls which have been realized for the elections of Serres Municipal in Greece on October 22nd of 2006. The methodology can be applied in any opinion poll, not only...

We employ neural network models to forecast the direction and the level of change in Istanbul Stock Exchange (ISE) Composite Index and 10 sector indices. We use 7 domestic and 15 international economic variables and stock indices. Three types of forecast methods were employed for each sector...