A Sharper Angle on Optimization
16 Pages Posted: 7 Oct 2009 Last revised: 19 Nov 2009
Date Written: October 5, 2009
Abstract
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.
Keywords: mean-variance optimization, covariance matrix, condition number, leverage, Sharpe Ratio
JEL Classification: G11, C61
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Portfolio Selection and Asset Pricing Models
By Lubos Pastor
-
A Test for the Number of Factors in an Approximate Factor Model
-
Comparing Asset Pricing Models: an Investment Perspective
By Lubos Pastor and Robert F. Stambaugh
-
Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps
By Tongshu Ma and Ravi Jagannathan
-
On Portfolio Optimization: Forecasting Covariances and Choosing the Risk Model
By Louis K.c. Chan, Jason J. Karceski, ...
-
Honey, I Shrunk the Sample Covariance Matrix
By Olivier Ledoit and Michael Wolf
-
Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach
By Lorenzo Garlappi, Tan Wang, ...
-
Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach
By Lorenzo Garlappi, Tan Wang, ...
-
Portfolio Constraints and the Fundamental Law of Active Management