We study how to best combine the sample mean-variance portfolio with the naive equally
weighted portfolio to optimize out-of-sample performance. We show that the seemingly …

Why Naive 1/N Diversification Is Not So Naive, and How to Beat It? Page 1 JOURNAL OF
FINANCIAL AND QUANTITATIVE ANALYSIS Vol. 59, No. 8, Dec. 2024, pp. 3601–3632 © …

Maximizing the out-of-sample Sharpe ratio is an important objective for investors. To achieve
this, we characterize optimal portfolio combinations maximizing expected out-of-sample …

Abstract Tail Mean-Variance (TMV) has emerged from the actuarial community as a criterion
for risk management and portfolio selection, with a focus on extreme losses. The existing …

We present a simple stochastic representation for the joint distribution of sample estimates of
three scalar parameters and two vectors of portfolio weights that characterize the minimum …

In this paper, we construct a shrinkage estimator of the global minimum variance (GMV)
portfolio by combining two techniques: Tikhonov regularization and direct shrinkage of …

A framework for portfolio allocation based on multiple hypotheses prediction using
structured ensemble models is presented. Portfolio optimization is formulated as an …

Estimation risk in portfolio selection can be mitigated with sparse approaches such as lasso
that penalizes for the norm of the portfolio weights and excludes assets from the investment …

We propose an ensemble framework for combining heterogeneous portfolio rules that
cannot be accommodated by previously proposed combination methods. Using our …

We introduce environmental, social, and governance (ESG) scores into the portfolio
selection framework using shrinkage estimators. We study nine linear shrinkage techniques …