Journal ArticleDOI
The Markowitz Optimization Enigma: Is ‘Optimized’ Optimal?
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TLDR
The Improving Portfolio Performance With Quantitative Models (IPPMQM) conference as mentioned in this paper was the first conference devoted to quantitative models for portfolio performance improvement, which was held in 1989.Abstract:
This presentation comes from the Improving Portfolio Performance With Quantitative Models conference held in New York on April 13, 1989.read more
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Journal ArticleDOI
Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?
TL;DR: In this article, the authors evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio.
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A well-conditioned estimator for large-dimensional covariance matrices
Olivier Ledoit,Michael Wolf +1 more
TL;DR: This paper introduces an estimator that is both well-conditioned and more accurate than the sample covariance matrix asymptotically, that is distribution-free and has a simple explicit formula that is easy to compute and interpret.
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Improved Estimation of the Covariance Matrix of Stock Returns With an Application to Portfolio Selection
TL;DR: In this paper, the covariance matrix of stock returns is estimated by an optimally weighted average of two existing estimators: the sample covariance and single-index covariance matrices.
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Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps
Ravi Jagannathan,Tongshu Ma +1 more
TL;DR: In this paper, the authors explain why constraining portfolio weights to be nonnegative can reduce the risk in estimated optimal portfolios even when the constraints are wrong, and they reconcile this apparent contradiction.
Journal ArticleDOI
A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms
TL;DR: In this article, a general framework for finding portfolios that perform well out-of-sample in the presence of estimation error is proposed, which relies on solving the traditional minimum-variance problem but subject to the additional constraint that the norm of the portfolio-weight vector be smaller than a given threshold.
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Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy?
Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps
Ravi Jagannathan,Tongshu Ma +1 more