A sequential predictor retraining algorithm and its application to market prediction

Abstract

In many applications, there are a large number of predictors, designed manually or trained automatically, to predict the same outcome. Much research has been devoted to the design of algorithms that can effectively select/combine these predictors to generate a more accurate ensemble predictor. The collaborative training algorithms from attribute distributed learning provide batch-processing solutions for scenarios in which the individual predictors are heterogeneous, taking different inputs and employing different models. However, in some applications, for example financial market prediction, it is desirable to use an online approach. In this paper, an innovative online algorithm is proposed, stemming from the collaborative training algorithms developed for attribute distributed learning. It sequentially takes new observations, simultaneously adjusting the way that the individual predictors are combined, and provides feedback to the individual predictors for them to be retrained in order to achieve a better ensemble predictor in real time. The efficacy of this new algorithm is demonstrated by extensive simulations on both artificial and real data, and particularly for financial market data. A trading strategy constructed from the ensemble predictor shows strong performance when applied to financial market prediction.