PT - JOURNAL ARTICLE AU - Marcos López de Prado AU - Riccardo Rebonato TI - Kinetic Component Analysis AID - 10.3905/joi.2016.25.3.142 DP - 2016 Aug 31 TA - The Journal of Investing PG - 142--154 VI - 25 IP - 3 4099 - https://pm-research.com/content/25/3/142.short 4100 - https://pm-research.com/content/25/3/142.full AB - The authors introduce kinetic component analysis (KCA), a state-space application that extracts the signal from a series of noisy measurements by applying a Kalman filter on a Taylor expansion of a stochastic process. They show that KCA presents several advantages over such popular noise-reduction methods as fast Fourier transform (FFT) or locally weighted scatterplot smoothing (LOWESS). First, KCA provides band estimates in addition to point estimates. Second, KCA further decomposes the signal in terms of three hidden components, which can be intuitively associated with position, velocity, and acceleration. Third, KCA is more robust in forecasting applications. Fourth, KCA is a forward-looking, state-space approach, resilient to structural changes. The authors believe that this type of decomposition is particularly useful in the analysis of trend following, momentum, and mean reversion in financial prices. An instrument exhibits financial inertia when its price acceleration is not significantly greater than zero for long periods of time. This empirical analysis of 19 of the most liquid futures worldwide confirms the presence of strong inertia across all asset classes. The authors also argue that KCA can be useful to market makers, liquidity providers, and faders for the calculation of their trading ranges.TOPICS: Statistical methods, portfolio construction, portfolio theory