A0420
Title: Functional motif discovery in stock market prices
Authors: Marzia Cremona - Universite Laval (Canada)
Lyubov Doroshenko - Université Laval (Canada)
Federico Severino - Universite Laval (Canada) [presenting]
Abstract: Financial asset prices display recurrent patterns over time. However, such time series are usually noisy and volatile, making the identification of repetitive patterns challenging. 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 are embedded in a functional data analysis framework by extending probabilistic K-means with local alignment to discover functional motifs in stock price time series. The discovered motifs are then exploited for price forecasting by developing a novel motif-based (MB) algorithm. After illustrating the technique on simulations of mixed causal-noncausal autoregressive processes, it is applied to the prices of S\&P 500 top components. Finally, the superior performance of motif-based forecasting is demonstrated in several other forecasting methods.
