Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

The computational role of short-term plasticity and the balance of excitation and inhibition in neural microcircuits: experimental and theoretical analysis

The computations performed by the brain ultimately rely on the functional connectivity between neurons embedded in complex networks. It is well known that the neuronal connections, the synapses, are plastic, i.e. the contribution of each presynaptic neuron to the firing of a postsynaptic neuron can be independently adjusted. The modulation of effective synaptic strength can occur on time scales that range from tens or hundreds of milliseconds, to tens of minutes or hours, to days, and may involve pre- and/or post-synaptic modifications. The collection of these mechanisms is generally believed to underlie learning and memory and, hence, it is fundamental to understand their consequences in the behavior of neurons.(...)

Neural correlations during brain activation in arithmetical tasks – an approach using electroencephalographic data

Dissertação apresentada na Faculdade de Ciências e Tecnologiea da Universidade Nova de Lisboa, para obtenção do Grau de Mestre em Engenharia Biomédica

Sustainability of systems interoperability in dynamic business networks

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores