Multi-asset portfolio optimization and out-of-sample performance: an evaluation of Black–Litterman, mean-variance, and naïve diversification approaches

AbstractDeMiguel, Garlappi, and Uppal (2009) report that naïve diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and extreme turnover. We find that mean-variance optimization often outperforms naïve diversification, but turnover can erode its advantage in the presence of transaction costs. To address this issue, we develop 2 new methods of mean-variance portfolio selection (volatility timing and reward-to-risk timing) that deliver portfolios characterized by low turnover. These timing strategies outperform naïve diversification even in the presence of high transaction costs.

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Out-of-sample performance of the Black-Litterman model

10.47260/jfia/1022 ◽

2021 ◽

pp. 29-51

Author(s):

Frieder Meyer-Bullerdiek

Keyword(s):

Portfolio Optimization ◽

Portfolio Management ◽

Asset Allocation ◽

Jel Classification ◽

Stock Index ◽

Out Of Sample ◽

Weighted Portfolio ◽

German Stock ◽

Mean Variance Portfolio ◽

Mean Variance

The aim of this paper is to test the out-of-sample performance of the Black Litterman (BL) model for a German stock portfolio compared to the traditional mean-variance optimized (MV) portfolio, the German stock index DAX, a reference portfolio, and an equally weighted portfolio. The BL model was developed as an alternative approach to portfolio optimization many years ago and has gained attention in practical portfolio management. However, in the literature, there are not many studies that analyze the out-of-sample performance of the model in comparison to other asset allocation strategies. The BL model combines implied returns and subjective return forecasts. In this study, for each stock, sample means of historical returns are employed as subjective return forecasts. The empirical analysis shows that the BL portfolio performs significantly better than the DAX, the reference portfolio and the equally weighted portfolio. However, overall, it is slightly outperformed by the MV portfolio. Nevertheless, the BL portfolio may be of greater interest to investors because -according to this study, where the subjective return forecasts are based on historical returns of a rather long past period of time-it could lead in most cases to lower absolute (normalized) values for the stock weights and for all stocks to smaller fluctuations in the (normalized) weights compared to the MV portfolio. JEL classification numbers: C61, G11. Keywords: Black-Litterman, Mean-variance, Portfolio optimization, Performance.

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A Network View of Portfolio Optimization Using Fundamental Information

Frontiers in Physics ◽

10.3389/fphy.2021.721007 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xiangzhen Yan ◽

Hanchao Yang ◽

Zhongyuan Yu ◽

Shuguang Zhang

Keyword(s):

Portfolio Optimization ◽

Asset Allocation ◽

Defensive Strategy ◽

Novel Approach ◽

Fundamental Information ◽

Out Of Sample ◽

Efficient Portfolios ◽

Robust Network ◽

Measure Of Similarity ◽

Mean Variance

This article proposes the use of a novel approach to portfolio optimization, referred to as “Fundamental Networks” (FN). FN is an effective and robust network-based fundamental-incorporated method, and can be served as an alternative to classical mean-variance framework models. As a proxy for a portfolio, a fundamental network is defined as a set of “interconnected” stocks, among which linkages are a measure of similarity of fundamental information and are referred to asset allocation directly. Two empirical models are provided in this paper as applications of Fundamental Networks. We find that Fundamental Networks efficient portfolios are in general more mean-variance efficient in out-of-sample performance than Markwotiz’s efficient portfolios. Specifically, portfolios set for profitability goals create excess return in a general/upward trending market; portfolios targeted for operating fitness perform better in a downward trending market, and can be considered as a defensive strategy in the event of a crisis.

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Fundamental Analysis and Mean-Variance Optimal Portfolios

The Accounting Review ◽

10.2308/tar-2019-0622 ◽

2021 ◽

Author(s):

Matthew R. Lyle ◽

Teri Lombardi Yohn

Keyword(s):

Portfolio Optimization ◽

Expected Returns ◽

Optimization Approach ◽

Fundamental Analysis ◽

Out Of Sample ◽

Sharpe Ratios ◽

Mean Variance Portfolio ◽

Portfolio Policy ◽

Performance Gains ◽

Mean Variance

We integrate fundamental analysis with mean-variance portfolio optimization to form fully optimized fundamental portfolios. We find that fully optimized fundamental portfolios produce large out-of-sample factor alphas with high Sharpe ratios. They substantially outperform equal-weighted and value-weighted portfolios of stocks in the extreme decile of expected returns, an approach commonly used in fundamental analysis research. They also outperform the factor-based and parametric portfolio policy approaches used in the prior portfolio optimization literature. The relative performance gains from mean-variance optimized fundamental portfolios are persistent through time, robust to eliminating small capitalization firms from the investment set, and robust to incorporating estimated transactions costs. Our results suggest that future fundamental analysis research could implement this portfolio optimization approach to provide greater investment insights.

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