Authors: Hugh L. Christensen; Simon J. Godsill
Addresses: Signal Processing and Communications Laboratory, Engineering Department, Cambridge University, Trumpington Street, Cambridge, CB2 1PZ, UK ' Signal Processing and Communications Laboratory, Engineering Department, Cambridge University, Trumpington Street, Cambridge, CB2 1PZ, UK
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.
Keywords: ensemble HTT; Hilbert-Huang transform; direct quadrature; futures trading; quantitative finance; trend following; cyclic frequencies; trend frequencies; time series; simulation.
International Journal of Computational Economics and Econometrics, 2014 Vol.4 No.3/4, pp.372 - 412
Available online: 14 Sep 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article