scholarly journals A Network View of Portfolio Optimization Using Fundamental Information

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.

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 Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Andrea Rigamonti
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measures ◽  
Traditional Approach ◽  
Real Data ◽  
Downside Risk ◽  
Good Choice ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Allocation Decisions

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified.

scholarly journals Optimal Option Portfolio Strategies: Deepening the Puzzle of Index Option Mispricing

2017 ◽  
Vol 52 (1) ◽  
pp. 277-303 ◽  
Author(s):  
José Afonso Faias ◽  
Pedro Santa-Clara
Keyword(s):  
Asset Allocation ◽  
Risk Premia ◽  
Volatility Risk ◽  
Short Sample ◽  
Option Returns ◽  
Portfolio Strategies ◽  
Portfolio Strategy ◽  
Out Of Sample ◽  
Index Option ◽  
Mean Variance

Traditional methods of asset allocation (such as mean–variance optimization) are not adequate for option portfolios because the distribution of returns is non-normal and the short sample of option returns available makes it difficult to estimate their distribution. We propose a method to optimize a portfolio of European options, held to maturity, with a myopic objective function that overcomes these limitations. In an out-of-sample exercise incorporating realistic transaction costs, the portfolio strategy delivers a Sharpe ratio of 0.82 with positive skewness. This performance is mostly obtained by exploiting mispricing between options and not by loading on jump or volatility risk premia.

Humans, Econs and Portfolio Choice

2016 ◽  
Vol 07 (02) ◽  
pp. 1750001 ◽  
Author(s):  
Michael J. Best ◽  
Robert R. Grauer
Keyword(s):  
Prospect Theory ◽  
Portfolio Choice ◽  
Loss Aversion ◽  
Asset Allocation ◽  
Power Utility ◽  
Numerical Example ◽  
Portfolio Choices ◽  
Out Of Sample ◽  
Finance Theory ◽  
Mean Variance

We compare the portfolio choices of Humans — prospect theory investors — to the portfolio choices of Econs — power utility and mean-variance (MV) investors. In a numerical example, prospect theory portfolios are decidedly unreasonable. In an in-sample asset allocation setting, the prospect theory results are consistent with myopic loss aversion. However, the portfolios are extremely unstable. The power utility and MV results are consistent with traditional finance theory, where the portfolios are stable across decision horizons. In an out-of-sample asset allocation setting, the power utility and portfolios outperform the prospect theory portfolios. Nonetheless the prospect theory portfolios with loss aversion coefficients of 2.25 and 2 perform well.

scholarly journals Strategic Asset Allocation Of Credit Guarantors

2015 ◽  
Vol 31 (5) ◽  
pp. 1823
Author(s):  
Dong-Woo Rhee ◽  
Hyoung-Goo Kang ◽  
Soo-Hyun Kim
Keyword(s):  
Public Policy ◽  
Asset Allocation ◽  
Minimum Variance ◽  
Risk Return ◽  
Out Of Sample ◽  
Sample Test ◽  
Prior Literature ◽  
The Mean ◽  
Risk Contribution ◽  
Mean Variance

<p>How to manage the portfolio of credit guarantors is important in practice and public policy, but has not been investigated well in the prior literature. We empirically compare four different approaches in managing credit guarantor portfolios. The four approaches are equal weighted, minimum variance, mean variance optimization and equal risk contribution methods. In terms of risk return ratio, the mean variance optimization model performs best in out-of-sample test. This result contrasts with previous findings against mean variance optimization. Our results are robust. The results do not change as the characteristics of guarantee portfolio vary.</p>

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.

The Robustness of Portfolio Optimization Models

2018 ◽  
pp. 210-229
Author(s):  
Antonis Pavlou ◽  
Michalis Doumpos ◽  
Constantin Zopounidis
Keyword(s):  
Value At Risk ◽  
Conditional Value At Risk ◽  
Optimization Models ◽  
Absolute Deviation ◽  
Efficient Frontiers ◽  
Out Of Sample ◽  
Efficient Portfolios ◽  
Using Data ◽  
Investment Portfolios ◽  
Mean Variance

The optimization of investment portfolios is a topic of major importance in financial decision making, and many relevant models can be found in the literature. These models extend the traditional mean-variance framework using a variety of other risk-return measures. Existing comparative studies have adopted a rather restrictive approach, focusing solely on the minimum risk portfolio without considering the whole set of efficient portfolios, which are also relevant for investors. This chapter focuses on the performance of the whole efficient set. To this end, the authors examine the out-of-sample robustness of efficient portfolios derived by popular optimization models, namely the traditional mean-variance model, mean-absolute deviation, conditional value at risk, and a multi-objective model. Tests are conducted using data for S&P 500 stocks over the period 2005-2016. The results are analyzed through novel performance indicators representing the deviations between historical (estimated) efficient frontiers, actual out-of-sample efficient frontiers, and realized out-of-sample portfolio results.

scholarly journals Exploring the diversification benefits of US international equity closed-end funds

2021 ◽  
Author(s):  
Jonathan Fletcher
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Asset Management ◽  
Sample Period ◽  
Simulation Approach ◽  
Oxford University ◽  
Out Of Sample ◽  
International Equity ◽  
Closed End Funds ◽  
Stock Portfolio

AbstractI use the simulation approach of Jobson and Korkie (J Portfolio Manag 7:70–74, 1981), combined with Michaud optimization (Michaud and Michaud, Efficient asset management: a practical guide to stock portfolio optimization and asset allocation, Oxford University Press, Oxford, 2008), to evaluate whether US international equity closed-end funds (CEF) provide out-of-sample diversification benefits. My study finds that international CEF do not provide diversification benefits across the whole sample period. However, the out-of-sample diversification benefits of international CEF do vary across economic states. I find that there are significant diversification benefits when the lagged one-month US Treasury Bill return is lower than normal, and when higher than normal, regardless of the benchmark investment universe used.

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