Predicting stock markets in boundary conditions with local models

This paper adopts the idea of regularity in the boundaries of financial time series in order to fit forecasting models which are able to outperform random walk predictions. In particular we propose the adoption of a local learning technique, called lazy learning, in order to perform model estimation and prediction in extreme conditions. The lazy learning method is proposed to return predictions in extreme conditions of trends of the Italian stock market index. The experiments show that in boundary conditions the technique is able to outperform a random predictor and to return a significant rate of accuracy.