A Sharper Angle on Optimization
Gregory C. Jones
October 5, 2009
The classical mean-variance optimization takes expected returns and variances and produces portfolio positions. In this paper we discuss the direction and the magnitude of the positions vector separately, and focus on the former. We quantify the distortions of the mean-variance optimization process by looking at the angle between the vector of expected returns and the vector of optimized portfolio positions. We relate this angle to the condition numbers of the covariance matrix and show how to control it by employing robust optimization techniques. The resulting portfolios are more intuitive and investment-relevant, in particular with lower leverage of the “noise” alphas at the expense of lower ex-ante Sharpe Ratio.
Number of Pages in PDF File: 16
Keywords: mean-variance optimization, covariance matrix, condition number, leverage, Sharpe Ratio
JEL Classification: G11, C61working papers series
Date posted: October 7, 2009 ; Last revised: November 19, 2009
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