Exploring the mean-variance portfolio optimization approach for planning wind repowering actions in Spain

2017 ◽  
Vol 106 ◽  
pp. 335-342 ◽  
Author(s):  
F.J. Santos-Alamillos ◽  
N.S. Thomaidis ◽  
J. Usaola-García ◽  
J.A. Ruiz-Arias ◽  
D. Pozo-Vázquez
Keyword(s):  
Portfolio Optimization ◽  
Optimization Approach ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance

scholarly journals An Improvement of Stochastic Gradient Descent Approach for Mean-Variance Portfolio Optimization Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Stephanie S. W. Su ◽  
Sie Long Kek
Keyword(s):  
Portfolio Optimization ◽  
Standard Error ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Stochastic Gradient Descent ◽  
Moment Estimation ◽  
The Mean ◽  
Portfolio Optimization Problem ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this paper, the current variant technique of the stochastic gradient descent (SGD) approach, namely, the adaptive moment estimation (Adam) approach, is improved by adding the standard error in the updating rule. The aim is to fasten the convergence rate of the Adam algorithm. This improvement is termed as Adam with standard error (AdamSE) algorithm. On the other hand, the mean-variance portfolio optimization model is formulated from the historical data of the rate of return of the S&P 500 stock, 10-year Treasury bond, and money market. The application of SGD, Adam, adaptive moment estimation with maximum (AdaMax), Nesterov-accelerated adaptive moment estimation (Nadam), AMSGrad, and AdamSE algorithms to solve the mean-variance portfolio optimization problem is further investigated. During the calculation procedure, the iterative solution converges to the optimal portfolio solution. It is noticed that the AdamSE algorithm has the smallest iteration number. The results show that the rate of convergence of the Adam algorithm is significantly enhanced by using the AdamSE algorithm. In conclusion, the efficiency of the improved Adam algorithm using the standard error has been expressed. Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.

scholarly journals An Entropy-Based Approach to Portfolio Optimization

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 332 ◽  
Author(s):  
Peter Joseph Mercurio ◽  
Yuehua Wu ◽  
Hong Xie
Keyword(s):  
Objective Function ◽  
Portfolio Optimization ◽  
Historical Data ◽  
Optimization Problems ◽  
Improved Method ◽  
Financial Industry ◽  
New Family ◽  
The Mean ◽  
Mean Variance Portfolio ◽  
Mean Variance

This paper presents an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced that simplifies the computation of portfolio entropy using a combinatorial approach. REPO addresses five main practical concerns with the mean-variance portfolio optimization (MVPO). Pioneered by Harry Markowitz, MVPO revolutionized the financial industry as the first formal mathematical approach to risk-averse investing. REPO uses a mean-entropy objective function instead of the mean-variance objective function used in MVPO. REPO also simplifies the portfolio entropy calculation by utilizing combinatorial generating functions in the optimization objective function. REPO and MVPO were compared by emulating competing portfolios over historical data and REPO significantly outperformed MVPO in a strong majority of cases.

Markowitz Theory in Portfolio Optimization for Heavy Tailed Assets

2019 ◽  
Vol 1 (3) ◽  
pp. 1-14
Author(s):  
Wong Ghee Ching ◽  
Che Mohd Imran Che Taib
Keyword(s):  
Portfolio Optimization ◽  
Composite Index ◽  
Minimum Variance ◽  
Optimization Techniques ◽  
Expected Return ◽  
Markowitz Model ◽  
The Mean ◽  
Heavy Tailed ◽  
Mean Variance Portfolio ◽  
Mean Variance

This paper aims at solving an optimization problem in the presence of heavy tail behavior of financial assets. The question of minimizing risk subjected to a certain expected return or maximizing return for a given expected risk are two objective functions to be solved using Markowitz model. The Markowitz based strategies namely the mean variance portfolio, minimum variance portfolio and equally weighted portfolio are proposed in conjunction with mean and variance analysis of the portfolio. The historical prices of stocks traded at Bursa Malaysia are used for empirical analysis. We employed CAPM in order to investigate the performance of the Markowitz model which was benchmarked with risk adjusted KLSE Composite Index. We performed a backtesting study of portfolio optimization techniques defined under modern portfolio theory in order to find the optimal portfolio. Our findings showed that the mean variance portfolio outperformed the other two strategies in terms of performance of investment for heavy tailed assets.

Portfolio Optimization in Post Financial Crisis of 2008-2009 in the Mongolian Stock Exchange

Jurnal METRIS ◽  
2020 ◽  
Vol 21 (01) ◽  
pp. 47-58
Author(s):  
Cheng-Wen Lee ◽  
Dolgion Gankhuyag
Keyword(s):  
Financial Crisis ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Mathematical Statistic ◽  
The Mean ◽  
Financial Crisis Of 2008 ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Safety First ◽  
Portfolio Return

In this study, we present the Mongolian stock market’s performance post phenomenal financial crisis of 2008-2009, opportunities to invest and the risks problems. For analysis of the study, we used financial portfolio optimization models with restricted structure, mathematical statistic methods and financial methods. First, we considered about portfolio optimization in the Mongolian Stock Exchange using Markowitz’s modern portfolio theory and Telser’s safety first model. We used MSE weekly trading data chosen 50 most traded stocks out of 237 stocks listed at the MSE between 2009 and 2013. We generated 50 weeks mean-variance portfolio and safety first portfolio for 2014 and discussed. We considered weekly investment in the MSE using mean-variance portfolio andsafety first portfolio. The mean-variance portfolio has the best performance of weekly portfolio return with average weekly return and cumulative return. We found stable portfolio against investing risk and did back-test the result. For prospect investors in the MSE, we suggest invest and earn high return in the MSE.

Fundamental Analysis and Mean-Variance Optimal Portfolios

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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