Enhancing Portfolio Performance in Global Equity Allocation with a Forward-Looking Indicator

2018 ◽  
Keyword(s):  
Equilibrium State ◽  
Portfolio Optimization ◽  
Asset Allocation ◽  
Economic Freedom ◽  
Equity Markets ◽  
Portfolio Performance ◽  
Risk Minimization ◽  
Expected Return ◽  
Ex Post ◽  
Forward Looking

The Black–Litterman model provides a more reasonable platform for portfolio optimization and asset allocation, as compared to the traditional CAPM approach, by presenting an equilibrium state of the markets and only deviating from that equilibrium state with forward-looking strategic views. The Index of Economic Freedom (IEF) can be used as a handy tool for forming such strategic views on global markets. Ex-post performance analysis of portfolios covering both developed and developing equity markets constructed with CAPM, Black–Litterman equilibrium implied return, and Black–Litterman absolute view approaches shows that by smoothing expected return with changes in the IEF, significantly superior portfolio performance can be achieved at a lower risk. The Index of Economic Freedom contains superior information in terms of idiosyncratic country-specific risks, which the market seems to ignore or under price. This study has particular relevance to asset allocation strategy, portfolio optimization, and risk minimization in the context of global equity markets.

scholarly journals CVaR Robust Mean-CVaR Portfolio Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Maziar Salahi ◽  
Farshid Mehrdoust ◽  
Farzaneh Piri
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measure ◽  
Optimization Technique ◽  
Large Error ◽  
Data Uncertainty ◽  
Final Decision ◽  
Estimation Risk ◽  
Expected Return ◽  
The Impact

One of the most important problems faced by every investor is asset allocation. An investor during making investment decisions has to search for equilibrium between risk and returns. Risk and return are uncertain parameters in the suggested portfolio optimization models and should be estimated to solve the problem. However, the estimation might lead to large error in the final decision. One of the widely used and effective approaches for optimization with data uncertainty is robust optimization. In this paper, we present a new robust portfolio optimization technique for mean-CVaR portfolio selection problem under the estimation risk in mean return. We additionally use CVaR as risk measure, to measure the estimation risk in mean return. To solve the model efficiently, we use the smoothing technique of Alexander et al. (2006). We compare the performance of the CVaR robust mean-CVaR model with robust mean-CVaR models using interval and ellipsoidal uncertainty sets. It is observed that the CVaR robust mean-CVaR portfolios are more diversified. Moreover, we study the impact of the value of confidence level on the conservatism level of a portfolio and also on the value of the maximum expected return of the portfolio.

scholarly journals Emerging Equity Markets: To Invest Or Not To Invest?

2011 ◽  
Vol 22 (1) ◽  
Author(s):  
Xavier Garza-Gómez ◽  
Massoud Metghalchi
Keyword(s):  
Stock Market ◽  
Emerging Markets ◽  
Equity Markets ◽  
Portfolio Performance ◽  
Specific Time ◽  
Ex Post ◽  
Time Periods ◽  
The U.S ◽  
Over Time

Numerous studies suggest that investors diversifying their portfolios with equity of emerging markets benefit from increased returns and/or reduced volatility. Using a 16-year sample from 1988 to 2003, we test this assertion and find that ex-post benefits to U.S. investors in this period are small. Our tests show that the improvement in portfolio performance is not consistent through time, and it is statistically significant only when we restrict our analysis to some regions and/or specific time periods. We find that the lack of significant gains of diversifying into emerging markets is caused by problems with the two main sources of diversification benefits: contrary to expectations, emerging markets have low relative realized returns and their correlation with the U.S. stock market has increased over time.

scholarly journals Portfolio Optimization Constrained by Performance Attribution

2021 ◽  
Vol 14 (5) ◽  
pp. 201
Author(s):  
Yuan Hu ◽  
W. Brent Lindquist ◽  
Svetlozar T. Rachev
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Selection Effect ◽  
Conditional Value At Risk ◽  
Positive Role ◽  
Performance Attribution ◽  
Dow Jones Industrial Average

This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE.

Risk-Return Measures of Ex Post Portfolio Performance

1969 ◽  
Vol 4 (4) ◽  
pp. 449 ◽  
Author(s):  
Keith V. Smith ◽  
Dennis A. Tito
Keyword(s):  
Portfolio Performance ◽  
Risk Return ◽  
Ex Post

scholarly journals Cojumps and asset allocation in international equity markets

2019 ◽  
Vol 98 ◽  
pp. 1-22 ◽  
Author(s):  
Mohamed Arouri ◽  
Oussama M’saddek ◽  
Duc Khuong Nguyen ◽  
Kuntara Pukthuanthong
Keyword(s):  
Asset Allocation ◽  
Equity Markets ◽  
International Equity Markets ◽  
International Equity

scholarly journals CHARACTERISTICS OF OMEGA-OPTIMIZED PORTFOLIOS AT DIFFERENT LEVELS OF THRESHOLD RETURNS

2014 ◽  
Vol 12 (2) ◽  
pp. 245-265 ◽  
Author(s):  
Renaldas Vilkancas
Keyword(s):  
Portfolio Optimization ◽  
Risk Measures ◽  
High Threshold ◽  
Portfolio Performance ◽  
Threshold Values ◽  
Portfolio Returns ◽  
Upside Risk ◽  
And Performance ◽  
Risk Free Rate ◽  
Different Levels

There is little literature considering effects that the loss-gain threshold used for dividing good and bad outcomes by all downside (upside) risk measures has on portfolio optimization and performance. The purpose of this study is to assess the performance of portfolios optimized with respect to the Omega function developed by Keating and Shadwick at different levels of the threshold returns. The most common choices of the threshold values used in various Omega studies cover the risk-free rate and the average market return or simply a zero return, even though the inventors of this measure for risk warn that “using the values of the Omega function at particular points can be critically misleading” and that “only the entire Omega function contains information on distribution”. The obtained results demonstrate the importance of the selected values of the threshold return on portfolio performance – higher levels of the threshold lead to an increase in portfolio returns, albeit at the expense of a higher risk. In fact, within a certain threshold interval, Omega-optimized portfolios achieved the highest net return, compared with all other strategies for portfolio optimization using three different test datasets. However, beyond a certain limit, high threshold values will actually start hurting portfolio performance while meta-heuristic optimizers typically are able to produce a solution at any level of the threshold, and the obtained results would most likely be financially meaningless.

scholarly journals Market Timing For active Asset Allocation: A Discrete Regression Model Approach

2011 ◽  
Vol 11 (1) ◽  
pp. 125
Author(s):  
Glen A. Larsen, Jr. ◽  
Gregory D. Wozniak
Keyword(s):  
Regression Model ◽  
Asset Allocation ◽  
Market Timing ◽  
The Other ◽  
Portfolio Performance ◽  
Empirical Results ◽  
Portfolio Strategies ◽  
Fixed Weight ◽  
Asset Class ◽  
Model Approach

A discrete regression model (DRM) approach to timing the asset class markets for stocks, bonds, and cash in active asset allocation is presented. The technique involves investing in the asset class whose return is predicted to exceed the other asset class return after observing a sequential signal of estimated probabilities. The empirical results show that the DRM approach provides enhanced portfolio performance when compared to more passive fixed-weight portfolio strategies.

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