B0577
Title: Functional motif discovery in stock market prices
Authors: Marzia Cremona - Universite Laval (Canada) [presenting]
Lyubov Doroshenko - Université Laval (Canada)
Federico Severino - Universite Laval (Canada)
Abstract: Financial asset prices display recurrent patterns over time. However, such time series are usually noisy and volatile, making the identification of repetitive patterns particularly difficult. These motifs are rarely exploited for price prediction, even though some of them, such as the surge of a financial bubble, occur periodically and feature similar shapes. Asset prices in a functional data analysis framework are embedded, by extending and using probabilistic K-means with local alignment to discover functional motifs in stock prices time series. The information on the discovered motifs is then exploited to perform the price forecasts with a novel motif-based algorithm introduced. After illustrating the technique on simulations of the mixed causal-noncausal autoregressive process, it is applied to the prices of S\&P500 top components and performs motif-based forecasting. Finally, its performance is compared to some traditional forecasting models.
