Prediction of cyclic and trend frequencies in time series using the Hilbert-Huang transform

Abstract

Prediction of future security returns is possible by decomposing a securities price into weighted superpositions of underlying basis states, given stationary distributions of the basis states. The (ensemble) Hilbert-Huang transform (HHT) is an empirical two-step online methodology which carries out such a decomposition from a multi-component noisy time series. HHT allows estimation of each component's instantaneous phase, period and amplitude. A hypothesis is presented where markets exist in the binary states of trend or cycle. Switching between states is based on phase-shifting in a dyadic filter bank. A trading algorithm is presented which exploits this model by combining intra-day predictions for trend and cycle components along with a much lower frequency drift component. The algorithm is simulated on e-mini S&P 500 futures data from CME GLOBEX at one minute sampling frequency. Results are presented which show a combined strategy Sharpe ratio in excess of 3.Biographical notes: Hugh L. Christensen received undergraduate and masters degrees from Oxford University and the PhD from the Signal Processing Laboratory, Cambridge University. He has worked in quantitative research for the UK Government and subsequently for algorithmic trading companies, applying techniques from signal processing and machine learning to high-frequency trading. His research interests include online methodologies for time series prediction, signal combination and the application of machine learning techniques to limit order book data.Simon J. Godsill is the Professor of Statistical Signal Processing at Cambridge University. He is an Associate Editor for IEEE Trans. Signal Processing and the journal Bayesian Analysis, and is a member of IEEE Signal Processing Theory and Prediction using Hilbert-Huang transform 373 Methods Committee. He has research interests in Bayesian and statistical methods for signal processing, Monte Carlo algorithms for Bayesian problems, modeling and enhancement of audio and musical signals, source separation, tracking and genomic signal processing. He has authored over 250 peer-reviewed journal and conference articles.