Prediction of cyclic and trend frequencies in time series using the Hilbert-Huang transform Online publication date: Sun, 14-Sep-2014
by Hugh L. Christensen; Simon J. Godsill
International Journal of Computational Economics and Econometrics (IJCEE), Vol. 4, No. 3/4, 2014
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.
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