Momentum Strategies: From Novel Estimation Techniques to Financial Applications

212 Pages Posted: 26 Nov 2013 Last revised: 23 Dec 2013

See all articles by Tung-Lam Dao

Tung-Lam Dao

affiliation not provided to SSRN

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.

Suggested Citation

Dao, Tung-Lam, Momentum Strategies: From Novel Estimation Techniques to Financial Applications (September 30, 2011). Available at SSRN: https://ssrn.com/abstract=2358988 or http://dx.doi.org/10.2139/ssrn.2358988

Tung-Lam Dao (Contact Author)

affiliation not provided to SSRN

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
2,125
Abstract Views
9,390
Rank
13,588
PlumX Metrics