Comparing fast- and slow-acting features for short-term price predictions

Sammanfattning: This thesis compares two groups of features for short-term price predictions of futures contracts; fast- and slow-acting features. The fast-acting group are based on limit order book derived features and technical indicators that reacts to changes in price quickly. The slow-acting features constitute of technical indicators that reacts to changes in price slowly. The comparison is done through two methods, group importance and a mean cost calculation. This is evaluated for different forecast horizons and contracts. Furthermore, two years of data was provided to do the analysis. Moreover, the comparison is modelled with an ensemble method called random forest. The response is constructed using rolling quantiles and a volume weighted price.  The finding implies that fast-acting features are superior at predicting price changes on smaller time scales, while long-acting features are better at predicting prices changes on larger time scales. Furthermore, the multivariate model results were similar to the univariate ones. However, the results are not clear-cut and more investigation ought to be done in order to confirm these results.