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Title: Financial space : pattern recognition for foreign exchange forecasting
Author: Rosowsky, Y. I.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
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We investigate the use of rejection applied to supervised learning for predicting the price direction of five foreign exchange currencies. We present two novel models which specifically take into account the random walk hypothesis when learning and predicting financial datasets. Both models project and then search a feature space for patterns and neighbourhoods unlikely to have arisen from a random process. The models invoke the human reply to an unfamiliar question of ‘I don’t know’ by rejecting (ignoring) training and/or test samples which do not satisfy checks for spurious relationships. The novel algorithms within this thesis are shown to significantly improve on both forecasting accuracy and economic viability when compared to several supervised learning reject and non-reject algorithms - the k-nearest neighbour and support vector machine algorithms are the main source of comparison. Reject-based models in general are shown to improve on the non-reject methods. Furthermore, several other contributions are noted within this thesis, namely: i) introducing intra-day data for forecasting daily price changes improves accuracy, ii) reducing the size of the time steps from one day to five minutes increased accuracy across all models; iii) forecasting accuracy was nearly always shown to reduce, across all models, after the events of the credit crisis (the years 2007 and 2009 are compared).
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available