Mean–variance combining rules that outperform naïve diversification

2021 ◽  
pp. 2141007
Author(s):  
Bacem Benjlijel
Keyword(s):  
Portfolio Management ◽  
Asset Allocation ◽  
Superior Performance ◽  
Investment Portfolio ◽  
Estimation Errors ◽  
Active Portfolio Management ◽  
Markowitz Portfolio ◽  
Weighted Portfolio ◽  
Naïve Diversification ◽  
Mean Variance

The mean–variance framework developed by Markowitz (1952). Portfolio selection, The Journal of Finance, 7(1), 77–91 is still the major model used nowadays in asset allocation and active portfolio management. However, the estimated mean–variance rules often fail to deliver superior performance compared with the simple naïve rule (the equally weighted portfolio) due to the problem of estimation errors. In this paper, I propose a portfolio construction method that is effective in dealing with estimation errors in the optimization process. Particularly, I specify the portfolio weights as an optimal combination of the equally weighted portfolio and a sample zero-investment portfolio. I show analytically that the proposed method alleviates the problem of estimation errors and dominates naïve diversification. I suggest two implementable versions of the combining method and show, empirically, their good performances relative to the naïve rule. The newly developed rules work well, particularly, for portfolios with a medium and high number of assets. Moreover, the outperformance persists generally even in the presence of transaction costs. Since the combinations are theory-based, my study may be interpreted as reaffirming the usefulness of the Markowitz portfolio theory in practice.

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

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.

scholarly journals Abnormal Portfolio Asset Allocation Model: Review

2020 ◽  
Vol 1 (1) ◽  
pp. 46-54
Author(s):  
Nurfadhlina Bt Abdul Halima ◽  
Dwi Susanti ◽  
Alit Kartiwa ◽  
Endang Soeryana Hasbullah
Keyword(s):  
Asset Allocation ◽  
Asset Returns ◽  
Higher Moments ◽  
Investment Portfolio ◽  
Allocation Model ◽  
Model Review ◽  
The Mean ◽  
Mean Variance ◽  
Skewness And Kurtosis ◽  
Normally Distributed

It has been widely studied how investors will allocate their assets to an investment when the return of assets is normally distributed. In this context usually, the problem of portfolio optimization is analyzed using mean-variance. When asset returns are not normally distributed, the mean-variance analysis may not be appropriate for selecting the optimum portfolio. This paper will examine the consequences of abnormalities in the process of allocating investment portfolio assets. Here will be shown how to adjust the mean-variance standard as a basic framework for asset allocation in cases where asset returns are not normally distributed. We will also discuss the application of the optimum strategies for this problem. Based on the results of literature studies, it can be concluded that the expected utility approximation involves averages, variances, skewness, and kurtosis, and can be extended to even higher moments.

scholarly journals Factor investing and asset allocation strategies: a comparison of factor versus sector optimization

2021 ◽  
Author(s):  
Wolfgang Bessler ◽  
Georgi Taushanov ◽  
Dominik Wolff
Keyword(s):  
Asset Allocation ◽  
Factor Versus ◽  
Minimum Variance ◽  
Superior Performance ◽  
Relative Advantage ◽  
Allocation Model ◽  
Risk Parity ◽  
And Performance ◽  
Mean Variance ◽  
Allocation Strategies

AbstractGiven the tremendous growth of factor allocation strategies in active and passive fund management, we investigate whether factor or sector asset allocation strategies provide investors with a superior performance. Our focus is on comparing factor versus sector allocations as some recent empirical evidence indicates the dominance of sector over country portfolios. We analyze the performance and performance differences of sector and factor portfolios for various weighting and portfolio optimization approaches, including “equal-weighting” (1/N), “risk parity,” minimum-variance, mean-variance, Bayes–Stein and Black–Litterman. We employ a sample-based approach in which the sample moments are the input parameters for the allocation model. For the period from May 2007 to November 2020, our results clearly reveal that, over longer investment horizons, factor portfolios provide relative superior performances. For shorter periods, however, we observe time-varying and alternating performance dominances as the relative advantage of one over the other strategy depends on the economic cycle. One important insight is that during “normal” times factor portfolios clearly dominate sector portfolios, whereas during crisis periods sector portfolios are superior offering better diversification opportunities.

scholarly journals Portfolio Selection Models

2017 ◽  
pp. 43-82
Keyword(s):  
Decision Theory ◽  
Portfolio Selection ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Computer Programs ◽  
Var Analysis ◽  
Widespread Acceptance ◽  
Portfolio Manager ◽  
The Mean ◽  
Mean Variance

The main goal behind the concept of portfolio management is to combine various assets into portfolios and then to manage those portfolios so as to achieve the desired investment objectives. To be more specific, the investors' needs are mostly defined in terms of profit and risk, and the portfolio manager makes a sound decision aimed ether to maximize the return or minimize the risk. The Mean-Variance and Mean-VaR analysis has gained widespread acceptance among practitioners of asset allocation. Although they are the simplest models of investment, sometimes they are sufficiently rich to be directly useful in applied problems and decision theory. Here you will learn how to apply these analyses in practice using computer programs and spreadsheets.

scholarly journals Asymmetric information, trading volume, and portfolio performance

2013 ◽  
Vol 11 (1) ◽  
pp. 8-23
Author(s):  
Antony Jackson
Keyword(s):  
Asymmetric Information ◽  
Asset Allocation ◽  
Trading Volume ◽  
Portfolio Performance ◽  
Estimation Errors ◽  
Multiple Markets ◽  
Positive Externality ◽  
Portfolio Managers ◽  
Volatility Timing ◽  
Naïve Diversification

In dealership markets, asymmetric information feeds through to higher transaction costs as dealers adjust their bid-ask spreads to compensate for anticipated losses. In this paper, we show that the presence of asymmetric information can also provide a positive externality to those market participants who operate in multiple markets-portfolio managers. Specifically, insiders lower the estimation errors of portfolio selection methods, thus improving asset allocation. We develop multiple artificial markets, in which portfolio managers trade alongside informed and uniformed speculators, and we contrast the performance of ‘volatility timing’—a method that relies on efficient price discovery - with that of ‘naive diversification’. Volatility timing is shown to consistently outperform naive diversification on a risk-adjusted basis.

It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification

2012 ◽  
Vol 47 (2) ◽  
pp. 437-467 ◽  
Author(s):  
Chris Kirby ◽  
Barbara Ostdiek
Keyword(s):  
Transaction Costs ◽  
Asset Allocation ◽  
Estimation Risk ◽  
Portfolio Strategies ◽  
Out Of Sample ◽  
Volatility Timing ◽  
Naïve Diversification ◽  
Mean Variance Portfolio ◽  
Timing Strategies ◽  
Mean Variance

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.

scholarly journals Decision Making in Real Estate: Portfolio Approach

2021 ◽  
Vol 21 (4) ◽  
pp. 28-44
Author(s):  
Todor Stoilov ◽  
Krasimira Stoilova ◽  
Miroslav Vladimirov
Keyword(s):  
Decision Making ◽  
Real Estate ◽  
Portfolio Management ◽  
Asset Allocation ◽  
Portfolio Theory ◽  
Investment Policy ◽  
Added Value ◽  
Real Estate Markets ◽  
Active Portfolio Management ◽  
Mean Variance Portfolio

Abstract An investment policy is suggested about assets on real estate markets. Such analysis recommends investments in non-financial assets and optimization of the results from such decisions. The formalization of the investment policy is based on the portfolio theory for asset allocation. Two main criteria are applied for the decision making: return and risk. The decision support is based on Mean-Variance portfolio model. A dynamical and adaptive investment policy is derived for active portfolio management. Sliding procedure in time with definition and solution of a set of portfolio problems is applied. The decision defines the relative value of the investment to which real estates are to be allocated. The regional real estate markets of six Bulgarian towns, which identify the regions with potential for investments, are compared. The added value of the paper results in development of algorithm for a quantitative analysis of real estate markets, based on portfolio theory.

Traditional Asset Allocation Securities

2017 ◽  
Author(s):  
Christopher Milliken ◽  
Ehsan Nikbakht ◽  
Andrew Spieler
Keyword(s):  
Portfolio Management ◽  
Asset Allocation ◽  
Management Strategies ◽  
Building Blocks ◽  
Warning Signs ◽  
Decision Makers ◽  
Behavioral Biases ◽  
Industry Professionals ◽  
Mean Variance ◽  
Allocation Strategies

Asset allocation models have evolved in complexity with the development of modern portfolio theory, but they continue to operate under the assumption of investor rationality and other assumptions that do not hold in the real world. For this reason, academics and industry professionals make efforts to understand the behavioral biases of decision makers and the implications these biases have on asset allocation strategies. This chapter reviews the building blocks of asset allocation, involving stocks, bonds, real estate, and cash. It also examines the history and theory behind two of the most popular portfolio management strategies: mean-variance optimization and the Black-Litterman Model. Finally, the chapter examines five common behavioral biases that have direct implications for asset allocation: familiarity, status quo, framing, mental accounting, and overconfidence. Each behavioral bias discussion contains examples, warning signs, and steps to correct the emotional or cognitive errors in decision making.

Positional Portfolio Management*

2019 ◽  
Author(s):  
P Gagliardini ◽  
C Gourieroux ◽  
M Rubin
Keyword(s):  
Portfolio Management ◽  
Factor Model ◽  
Management Strategies ◽  
Cross Sectional ◽  
Weighted Portfolio ◽  
The Cross ◽  
The Individual ◽  
Mean Variance ◽  
Sectional Rank ◽  
Allocation Strategies

AbstractWe study positional portfolio management strategies in which the manager maximizes an expected utility function written on the cross-sectional rank (position) of the portfolio return. The objective function reflects the manager’s goal to be well-ranked among competitors. To implement positional allocation strategies, we specify a nonlinear unobservable factor model for the asset returns which disentangles the dynamics of the cross-sectional distribution and the dynamics of the ranks of the individual assets. Using a large dataset of stocks returns we find that positional strategies outperform standard momentum, reversal and mean-variance allocation strategies, as well as equally weighted portfolio for criteria based on position.

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