publication . Bachelor thesis . 2016

Valutakursprediktion med hjälp av övervakade och oövervakade statistiska inlärningsmetoder

Vasiljeva, Polina;
Open Access English
  • Published: 01 Jan 2016
  • Publisher: KTH, Matematisk statistik
  • Country: Sweden
Abstract
In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. We use a data set with over 200 predictors comprised of different financial and macro-economic time series and their transformations. We perform forecasting for one week ahead with different parameterizations and find a hit rate of on average 53%, with some of the parameterizations yielding hit rates as high as 60%. However, there is no clear indicator ...
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[14] Allan D Gordon. A review of hierarchical classification. Journal of the Royal Statistical Society. Series A (General), pages 119-137, 1987.

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