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.

A technical investigation of an oil painting on copper support, including a study on consolidants for treatment

Paper submitted to e-conservation Journal: Maria Leonor Oliveira, Leslie Carlyle, Sara Fragoso, Isabel Pombo Cardoso and João Coroado, “Investigations into paint delamination and consolidation of an oil painting on copper support”.

Characterization of the clients retention in the tlecommunications companies

The ability of a company to be able to do a precisely churn prediction, so it can act on it, is paramount. For this reason, Deloitte addressed me the challenge of characterizing the client’s retention in the telecom companies. To do so, it was created a comprehensive tool that enables Deloitte to evaluate the churn management maturity level of a telecom operator and highlight its strengths and weaknesses. The development of this matrix was based on a depth churn research, a market research based on 40 interviews and 2 focus group and the valuable feedback from Deloitte consultants.