Non-parametric prediction of the mid-price dynamics in a limit order book

We propose a novel non-parametric approach to short-term forecasting of the mid-price change in a limit order book (i.e., the change in the average of the best offer and the best bid prices). We construct a state vector describing the state of the order book at each time, and compute a feature vector for each value of the state vector. The features get updated during the course of a trading day, as new order flow information arrives. Our prediction at every time instant during the trading day is based on the feature vector computed at that time. The distinction of our approach from the previous ones is that it does not impose a restrictive parametric model. Implicit assumptions of our method are very mild. Initial experiments with real order book data from NYSE suggest that our algorithms show promise.