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Forecasting stock markets using machine learning : forecasting the PSI-20 index using a machine learning approach
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Orientador(es)
Resumo(s)
Predicting nancial markets is a task of extreme di culty. The factors that in
uence
stock prices are extremely complex to model. Machine Learning algorithms have
been widely used to predict nancial markets with some degree of success. This
Master's project aims to study the application of these algorithms to the Portuguese
stock market, the PSI-20, with special emphasis on genetic programming and the
introduction of the concept of semantics in the process of evolution. Three systems
based on genetic programming were studied: STGP, GSGP and GSGP-LS. The
construction of the predictive models is based on historical information of the index
extracted through a blooberg portal. In order to analyze the quality of the models
based on genetic programming, the nal results were compared with other Machine
Learning algorithms through the application of signi cance statistical tests. An
analysis of the quality of the results of the di erent algorithms is presented and
discussed.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management
Palavras-chave
Genetic programming Stock markets Machine learning Geometric semantic operators Forecasting