Mean-variance Efficient Portfolios Research Articles

VIX-managed portfolios

We propose a simple portfolio management strategy that gauges the leverage based on the observed implied volatility index (VIX). The strategy involves taking less risk when the cumulative previous-month VIX is high and more when it is low. We show that the strategy yields more stable weights and thus requires less rebalancing than comparable strategies based on realized volatility. As a result, it produces substantially higher spanning regression alphas when transaction costs are taken into account. We document this for ten equity factors, six classes of mean–variance efficient portfolios and 176 anomaly portfolios. We argue that the superior performance of the VIX-based strategy is driven by its ability to time volatility and tail risk simultaneously, resulting from the forward-looking nature of the information entailed in the index and the higher-order return moments embedded in the implied volatility smile.

Earnings Forecasting and Mean-Variance Efficient Portfolios in the United States

Earnings Forecasting and Mean-Variance Efficient Portfolios in the United States

ESTIMASI RISIKO PORTOFOLIO SAHAM PERUSAHAAN PERKEBUNAN DI BURSA EFEK INDONESIA MENGGUNAKAN VALUE AT RISK NON-NORMAL

Stock investment portfolio aims to minimize the investment risk. However, problems of the portfolio formation are determining funds allocation for each stock and measuring its risk. Fund allocation is determined using the Mean-Variance Efficient Portfolio method, while risk measurement is carried out using Value at Risk (VaR). Nevertheless, problem on VaR is determining a fit distribution which would be involved to obtain quantile values at certain probability. This study discusses way of funds allocation determination and VaR value calculation that is aimed to analyze their impact in estimating the VaR value. The study used stock price return rate data of plantation companies listed on Indonesia Stock Exchange such as Astra Agro Lestari Tbk. (AALI), BISI International Tbk. (BISI), and PP London Sumatra Indonesia Tbk. (LSIP). The result showed BISI stock has high volatility so that its funds allocation is relatively smaller. The distribution identified for portfolio return rate is Logistics Distribution with the estimated parameters 0.0001187447 and 0.008810698. Portfolio VaR value at the 95% confidence level is -0.02582382. We conclude the determination of funds allocation does not minimize risk and the calculation of VaR with distributions do not match the data result a relatively higher VaR value.

The Development and Evolution of Mean-Variance Efficient Portfolios in Japan: 30 Years After

The Development and Evolution of Mean-Variance Efficient Portfolios in Japan: 30 Years After

Portfolio optimization by price-to-earnings ratio network analysis

This paper introduces Price-to-Earnings Ratio Network (PEN) analysis as an alternative to mean–variance analysis for portfolio optimization. The equivalence among Price-to-Earnings (P/E) ratios, node degree distribution and capital allocation distribution is established with a Havel–Hakimi network structure. Such equivalences allow network entropy to be a measure of portfolio diversification and robustness. Our empirical analysis finds a linear correlation between in-sample network entropy and out-of-sample portfolio returns. Then, a return-entropy efficient frontier is introduced to interpret the return-diversification trade-offs. Further, we compare the out-of-sample performance of portfolios optimized with PEN analysis against those optimized with mean–variance analysis, showing that PEN-optimized portfolios outperform mean–variance efficient portfolios in returns, given a low-to-medium P/E ratio level. This outcome accords with the P/E effect that stocks with low P/E ratios are undervalued and will provide increased returns in the future. In addition, regarding global financial risk events such as the financial crisis in 2008, the Euro debt crisis in 2013 and Brexit in 2016, this study finds that the PEN-optimized portfolio size increased significantly (even to more than 300 stocks) to mitigate systemic risk, while mean–variance efficient portfolios were not sufficiently diversified.

The Development of Mean-Variance Efficient Portfolios: 30 Years Later

In 1992, in the initial year of this journal’s publication, Guerard and Takano reported mean-variance efficient portfolios for the Japanese and US equity markets and showed that the use of a regression-weighted composite model of earnings, book value, cash flow, sales, and their relative variables outperformed their respective equity benchmarks by approximately 400 basis points annually. Two years later, Markowitz and Xu tested the composite model strategy and found that its excess returns were statistically significant from a variety of models tested and that the composite model strategy was not the result of data mining. For the 30th anniversary issue, the authors of this article report robust regression modeling results for the 2001–2020 period using the latest features in R and the latest commercially available multi-factor models for portfolio selection. Quantitative investing requires constant implementation and discipline to maximize client wealth. The authors’ results suggest that stock selection models can be effectively employed to deliver excess returns.

Unleveraged Portfolios and Pure Allocation Return

In asset management, the portfolio leverage affects performance, and can be subject to constraints and operational limitations. Due to the possible leverage aversion of the investors, the comparison between portfolio performances can be incomplete or misleading. We propose a procedure to unleverage the mean-variance efficient portfolios to satisfy a leverage requirement. We obtain a class of unleveraged portfolios that are homogeneous in terms of leverage, so therefore properly comparable. The proposed unleverage procedure permits isolating the pure allocation return, i.e., the return component, due to the qualitative choice of portfolio allocation, from the return component due to the portfolio leverage. Theoretical analysis and empirical evidence on actual data show that efficient mean-variance portfolios, once unleveraged, uncover mean-variance dominance relations hidden by the leverage contribution to portfolio return. Our approach may be useful to practitioners proposing to take long positions on “short assets” (e.g. inverse ETF), thereby considering short positions as active investment choices, in contrast with the usual interpretation where are used to overweight long positions.

Robust Portfolio Choice

We develop a normative theory for constructing mean-variance portfolios robust to model misspecification. We identify two inefficient portfolios---an alpha'' portfolio, representing latent asset demand, that depends only on pricing errors and a beta'' portfolio that depends on factor risk premia, which when combined give mean-variance efficient portfolios. Our key insight is to recognize that the alpha and beta portfolios have different economic properties and therefore we treat misspecification in these portfolios using different methods. We demonstrate that our theoretical insights lead to an economically substantial and statistically significant improvement in out-of-sample portfolio performance, which increases with the number of assets.

Diversification Benefits from Leveraged Loans? Trends in Cross-Asset Correlations

The concept of diversification is well understood by both academics and investment professionals. In forming a mean-variance efficient portfolio, investment managers look for low-correlated assets in order to maximize diversification benefits. This article investigates the potential of European and US leveraged loans to serve as diversifiers in a multi-asset portfolio. By calculating correlations between leveraged loans and other asset classes over the sample period from 2002 to 2020, the authors are not only able to demonstrate increasing correlations in highly volatile markets (i.e., financial crises), but also long-term, crisis-independent positive trends in asset correlations. In particular, leveraged loans and their correlations with stocks and high-yield bonds present significantly positive long-term trends and hence diminishing diversification benefits. The authors state that instead of short-term market volatility, increasing market integration could be the driving force behind this effect. The results also indicate that diversification benefits from leveraged loans are lower in the US than in the European market. TOPICS:Fixed income and structured finance, developed markets, portfolio construction, performance measurement Key Findings ▪ The empirical analysis shows generally stable and low correlations of leveraged loans with most asset classes in European and US markets. Stocks and high-yield bonds are an exception, showing a pattern with rising long-term correlation coefficients. ▪ The correlation of leveraged loans with high-yield bonds is higher than those with other asset classes due to their comparable characteristics. Correlations between those asset classes have risen, especially since the global financial crisis in 2008. ▪ Due to higher market volumes and integration advantages in the US, the correlations of leveraged loans with other assets are higher in the US market, leading to lower diversification benefits compared to Europe.

Second order of stochastic dominance efficiency vs mean variance efficiency

In this paper, we compare two of the main paradigms of portfolio theory: mean variance analysis and expected utility. In particular, we show empirically that mean variance efficient portfolios are typically sub-optimal for non satiable and risk averse investors. We illustrate that the second order stochastic dominance (SSD) efficient set is the solution of a multi-objective optimization problem. We further show that the market portfolio is not necessarily a solution to this optimization problem. We also conduct an empirical analysis, examining the ex ante and ex post performance of SSD and mean variance efficient portfolios, using a bootstrap approach. In an ex ante analysis, we compare empirical moments, the level of diversification and set distances of mean variance and SSD efficient sets. We also show that the global minimum variance (GMV) portfolio and the part of the mean variance efficient frontier (MVEF) composed of highly diversified portfolios is second order stochastically dominated. This result also provides a possible alternative explanation for the diversification puzzle. Conducting an ex post analysis, we construct second order stochastic dominating strategies that outperform the GMV portfolio in terms of wealth and various other performance measures, producing a positive ex post opportunity cost.

Cross-Asset Skew

We find that realized skewness is a significant indicator of returns across a range of assets from different asset classes, namely commodities, government bonds, equity indices and currencies. Taking on skewness risk is broadly compensated within, but more substantially across asset classes. Portfolios in these four asset classes with long positions on most negatively (or least positively) skewed assets and short positions on least negatively (or most positively) skewed assets generate on average a Sharpe ratio of 0.35 between 1990 and 2017. We find little evidence of a common risk driver among these portfolios, to the extent that their combination benefits substantially from diversification, delivering a Sharpe ratio of 0.72. The patterns are not subsumed by other known factors that drive returns, such as value, momentum or carry factors and, consequently, mean-variance efficient multi-factor portfolios assign a positive weight to skewness. Our results remain robust to different measures of skewness and across sub-samples.

Analisis Portofolio Investasi dengan Metode Multi Objektif

In the formation of an efficient portfolio, many methods can be used. Of course with its own assumptions and advantages. In the process, reasonable investor assumptions tend to be risk averse. Investors who are risk averse are investors who, when faced with two investments with the same expected return, will choose an investment with a lower risk level. If an investor has several efficient portfolio choices, then the most optimal portfolio will be chosen. Optimal portfolio with mean-variance efficient portfolio criteria, investors only invest in risky assets. Investors do not include risk free assets in their portfolios. Mean-variance efficient portfolio is defined as a portfolio that has a minimum variance among all possible portfolio that can be formed, at the mean level of the same expected return. The mean variant method of the two constraints can be used as a basis in determining the optimal portfolio weight by minimizing the risk of portfolio return with two constraints. In this article the problem referred to is symbolized by lamda and beta. With this two-constraint method, the results obtained are more detailed so that they can describe the results of a sharper analysis for an investor.

Optimal Vs 1/N Diversification and Portfolio Evaluation: A study on Indian Stock Market

The Modern portfolio theory of Markowitz (1952) proposed maximisation of expected utility and minimisation of the risk of the optimal portfolio for the risk-averse investors. We used the linear programming technique to estimate the optimal portfolio weights for the mean-variance efficient optimal portfolio using rebalanced and non-rebalanced portfolios and compared the performances against the 1/N heuristic portfolio. We found that the minimum-variance optimal portfolio performed better than the 1/N heuristic portfolio.

Real Estate: The Case for Investment in Private and Listed Real Estate

Executive Summary. Our analysis of long-term trends points to the outperformance of listed REIT stocks and private equity real estate opportunistic funds compared to more traditional asset. To explain these results, we appeal to Merton (1973) and Fama's (1996) multifactor mean-variance efficient portfolio theory, where the asset weights for these portfolios are those that produce the market portfolio and an extra multifactor-efficient portfolio that performs well in a low equilibrium rate environment. Researchers have found that private and listed real estate performs well as a diversifier in a stock and bond portfolio. However, mean-variance portfolio theory does not provide a unified account of the outperformance of private and listed real estate over the past two and a half decades. A multifactor-minimum variance model is necessary, which introduces the issue of hedging and of tailoring portfolios to mitigate potential vulnerability to a low equilibrium rate environment. We find that investors should add a mimicking portfolio to mitigate their potential vulnerability to a low equilibrium rate environment, and that this portfolio should consist primarily of private and listed real estate.

Combining the minimum-variance and equally-weighted portfolios: Can portfolio performance be improved?

It is documented in the literature that due to estimation errors, mean-variance efficient portfolios deliver no higher out-of-sample Sharpe ratios than does the naïve equally-weighted portfolio (EWP). This paper demonstrates how the out-of-sample performance of the minimum-variance portfolio (MVP) can be improved in the presence of estimation errors by combining the MVP and EWP. Our results indicate that an appropriate combination of the MVP and EWP can enhance Sharpe ratios under any scenarios considered, and can also reduce the portfolio risk if short-selling is allowed. However, the combination strategy is not able to generate a lower risk level than the MVP when a short-selling restriction is imposed. We find that the optimal combination coefficient depends on the factors that greatly impact estimation errors in the MVP, including sample size, estimation method, no-short-selling restriction, and length of the out-of-sample period under consideration.

The profitability and investment premium: Pre-1963 evidence

I investigate the profitability and investment premium in stock returns using hand-collected data from Moody's Manuals for 1940–1963. Controlling for value, the profitability premium emerges as important in this period. In contrast, there is no reliable relation between investment and returns, regardless of whether investment is measured using growth in total assets or book equity and even after extending the data back to 1926. In spanning regressions, factors constructed from profitability and book-to-market ratios (RMW and HML, respectively) improve the mean-variance efficient tangency portfolio but the investment factor (CMA) does not.

Creating Optimal Portfolio and the Efficient Frontier Using Microsoft Excel ®

Portfolio managers and investors strive to achieve the best possible trade-off between risk and return, and one of the tools they use is constructing mean-variance efficient portfolios. Finance students learn about optimal portfolios and efficient frontiers, though it is difficult to replicate them unless they have access to sophisticated software. This paper develops a teaching module that uses Microsoft Excel® to create mean-variance portfolios and traces out the efficient frontier using real-world data. In the process, the students learn to determine optimal investment allocations in a portfolio, select the optimum investment portfolio given investor’s objectives and preferences and learn about factors that influence different asset allocations. For multiple assets (N>3), the paper uses Matrix algebra in Excel®. The paper enables students and investors to learn how to construct real-world mean-variance efficient portfolios using Excel®.

Disentangling the role of variance and covariance information in portfolio selection problems

The covariation among financial asset returns is often a key ingredient used in the construction of optimal portfolios. Estimating covariances from data, however, is challenging due to the potential influence of estimation error, specially in high-dimensional problems, which can impact negatively the performance of the resulting portfolios. We address this question by putting forward a simple approach to disentangle the role of variance and covariance information in the case of mean-variance efficient portfolios. Specifically, mean-variance portfolios can be represented as a two-fund rule: one fund is a fully invested portfolio that depends on diagonal covariance elements, whereas the other is a long-short, self financed portfolio associated with the presence of non-zero off-diagonal covariance elements. We characterize the contribution of each of these two components to the overall performance in terms of out-of-sample returns, risk, risk-adjusted returns and turnover. Finally, we provide an empirical illustration of the proposed portfolio decomposition using both simulated and real market data.

PENGUKURAN RISIKO KREDIT DAN PENGUKURAN KINERJA DARI PORTOFOLIO OBLIGASI

Except getting coupon as a profit, there is loss probability in bond investment that is credit risks investment. One way to measure the credit risk of a bond is to use the credit metrics method. It uses the ratings of the bond issuer company and the transition rating issued by the rating company for its calculations. Mean Variance Efficient Portfolio (MVEP) can be used to make an optimal portfolio so that risk can be obtained to a minimum. An assessment of portfolio performance is needed to increase confidence to invest. Sharpe index can measure portfolio performance based on return value of bond. In this case, study has been conduct in two bonds which are Obligasi Berkelanjutan I Bank BTN Tahap II Tahun 2013 and Obligasi Berkelanjutan I PLN Tahap I Tahun 2013 Seri B. The optimum portfolio formed results 67,96% proportion for the first bond and 32,04% for the second bond. For the result, and there is Rp239,4235(billion) of portfolio risk formed. And there is 0,212496for Sharpe index performance assessment portfolio. Keywords: Bond, portfolio, credit risk, credit metrics, Mean Variance Efficient Portfolio, Sharpe index

Empirical Asset Pricing With Many Assets and Short Time Series

We construct tracking portfolios consisting of a large number of assets for macroeconomic factors using the L_2-boosting algorithm. We use these tracking portfolios as instruments to estimate factor risk prices. The same learning algorithm also provides the weights of a mean-variance efficient portfolio. With this additional input we compute the Hansen-Jagannathan distance to compare how alternative models fit the cross-section. We apply the method to 900 portfolio return series in the Kenneth French data library. While macro factors fail to explain most cross-sectional variation, we find that both consumption and inflation risk are priced.