'Portfolio Rebalancing with Reinvestment Based on Noise Filtered Correlation/Covariance Matrices and the Case of NASDAQ 100' : Paper 2 in a Series 'Uncovering Hidden Dependencies'

Alexander Izmailov, Ph.D (theoretical physics) and Brian Shay, Ph.D (mathematics) of Market Memory Trading, L.L.C. present, in a series of nine (9) white papers, aspects of a revolutionary advance in uncovering hidden dependencies via filtering noise from correlation matrices developed by the New York based company, Market Memory Trading, L.L.C. (MMT). Correlations are quantitative measures of these dependencies and noise filtering increases their accuracy as a decision-making tool, from asset allocation to LIBOR Surveillance and cyber security.“PORTFOLIO REBALANCING BASED ON NOISE FILTERED CORRELATION/COVARIANCE MATRICES the Case of NASDAQ 100.” White Paper 2, dated December 2, 2013, provides a demonstration of outstanding investment performance due to noise filtering of covariance matrices in optimum portfolio selection exercises. This demonstration is based on the out-of-sample simulation of rebalancing trading done daily, weekly, biweekly and monthly; and exhibits of investment performance for a number of long-short and mostly long portfolios consisting of NASDAQ 100 names. The reported results clearly favor filtered covariance matrices. In extreme cases, 100 % additional performance (annualized returns) can be derived from using noise filtered matrices in optimization rather than using unfiltered matrices. This particular portfolio selection method succeeded in “beating the index” even without filtering, in all exhibits during a 7 year period including the most recent financial crisis: 2006-2013. Refer to Appendix A for Complete Series