In recent years, machine learning algorithms havebecome increasingly popular in financial forecasting. Theirflexible, data-driven nature makes them ideal candidates fordealing with complex financial data. This paper investigates theeffectiveness of a number of machine learning algorithms, andcombinations of these algorithms, at generating one-step aheadforecasts of a number of financial time series. We find thathybrid models consisting of a linear statistical model and a nonlinearmachine learning algorithm are effective at forecastingfuture values of the series, particularly in terms of the futuredirection of the series.
|Title of host publication||Unknown Host Publication|
|Number of pages||8|
|Publication status||Published (in print/issue) - 27 Mar 2014|
|Event||IEEE Computational Intelligence for Financial Engineering and Economics - |
Duration: 27 Mar 2014 → …
|Conference||IEEE Computational Intelligence for Financial Engineering and Economics|
|Period||27/03/14 → …|