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Predicting equity market price impact with performance weighted ensembles of random forests

For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The system's performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks.