Since the pioneering work of Harry Markowitz, mean–variance portfolio selection model has
been widely used in both theoretical and empirical studies, which maximizes the investment …

We 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. Of …

There is considerable literature on the strengths and limitations of mean-variance analysis.
The basic theory and extensions of MV analysis are discussed in Markowitz [1987] and …

We provide a general framework for finding portfolios that perform well out-of-sample in the
presence of estimation error. This framework relies on solving the traditional minimum …

In this paper we show how to formulate and solve robust portfolio selection problems. The
objective of these robust formulations is to systematically combat the sensitivity of the …

How do people make decisions when time is limited, information unreliable, and the future
uncertain? Based on the work of Nobel laureate Herbert Simon and with the help of …

In spite of theoretical benefits, Markowitz mean-variance (MV) optimized portfolios often fail
to meet practical investment goals of marketability, usability, and performance, prompting …

We develop a model for an investor with multiple priors and aversion to ambiguity. We
characterize the multiple priors by a “confidence interval” around the estimated expected …

The portfolio optimization model has limited impact in practice because of estimation issues
when applied to real data. To address this, we adapt two machine learning methods …

The mean-variance model for portfolio selection requires estimates of many parameters.
This paper investigates the effect of errors in parameter estimates on the results of mean …