Momentum Strategies: From Novel Estimation Techniques to Financial Applications
212 Pages Posted: 26 Nov 2013 Last revised: 23 Dec 2013
Date Written: September 30, 2011
Abstract
The objectives of this report are two-fold. We first studied some novel techniques in statistics and signal processing fields such as trend filtering, daily and high frequency volatility estimator or support vector machine. We employed these techniques to extract interesting financial signals. These signals are used to implement the momentum strategies which will be described in detail in every chapter of this report. The second objective concerns the study of the performance of momentum strategies based on the risk-return analysis framework (see B. Bruder and N. Gaussel 7th White Paper, Lyxor).
Keywords: Momentum strategy, L1 filtering, L2 filtering, trend-following, meanreverting, volatility, voltarget, range-based estimator, high-low estimator, microstructure noise, machine learning, support vector machine, regression, classification, stock selection, CTA , Kalman filter, Chi-square distribution.
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