scholarly journals A new media optimizer based on the mean-variance model

2007 ◽  
Vol 27 (3) ◽  
pp. 427-456 ◽  
Author(s):  
Pedro Jesus Fernandez ◽  
Marcelo de Souza Lauretto ◽  
Carlos Alberto de Bragança Pereira ◽  
Julio Michael Stern
Keyword(s):  
New Media ◽  
Ad Hoc ◽  
Media Planning ◽  
Full Generality ◽  
Variance Model ◽  
Novel Approach ◽  
The Media ◽  
The Mean ◽  
Set Up ◽  
Mean Variance

In the financial markets, there is a well established portfolio optimization model called generalized mean-variance model (or generalized Markowitz model). This model considers that a typical investor, while expecting returns to be high, also expects returns to be as certain as possible. In this paper we introduce a new media optimization system based on the mean-variance model, a novel approach in media planning. After presenting the model in its full generality, we discuss possible advantages of the mean-variance paradigm, such as its flexibility in modeling the optimization problem, its ability of dealing with many media performance indices - satisfying most of the media plan needs - and, most important, the property of diversifying the media portfolios in a natural way, without the need to set up ad hoc constraints to enforce diversification.

Optimal selection of financial risk investment portfolio based on random matrix method

2020 ◽  
Vol 20 (3) ◽  
pp. 859-868
Author(s):  
Jie Tian ◽  
Kun Zhao
Keyword(s):  
Covariance Matrix ◽  
Random Matrix ◽  
Financial Risk ◽  
Good Effect ◽  
Investment Portfolio ◽  
Variance Model ◽  
Minimum Risk ◽  
The Mean ◽  
Risk Investment ◽  
Mean Variance

The optimization of investment portfolio is the key to financial risk investment. In this study, the investment portfolio was optimized by removing the noise of covariance matrix in the mean-variance model. Firstly, the mean-variance model and noise in covariance matrix were briefly introduced. Then, the correlation matrix was denoised by KR method (Sharifi S, Grane M, Shamaie A) from random matrix theory (RMT). Then, an example was given to analyze the application of the method in financial stock investment portfolio. It was found that the stability of the matrix was improved and the minimum risk was reduced after denoising. The study of minimum risk under different M values and stock number suggested that calculating the optimal value of M and stock number based on RMT could achieve optimal financial risk investment portfolio result. It shows that RMT has a good effect on portfolio optimization and is worth promoting widely.

Why Do We Reject the Mean-Variance Model?

1995 ◽  
Vol 97 (1) ◽  
pp. 137 ◽  
Author(s):  
W. Jos Jansen
Keyword(s):  
Variance Model ◽  
The Mean ◽  
Mean Variance

scholarly journals MEAN-VARIANCE VERSUS MEAN–EXPECTED SHORTFALL MODELS: AN APPLICATION TO WHEAT VARIETY SELECTION

2016 ◽  
Vol 48 (2) ◽  
pp. 148-172 ◽  
Author(s):  
KUNLAPATH SUKCHAROEN ◽  
DAVID LEATHAM
Keyword(s):  
Risk Measure ◽  
Management Strategies ◽  
Wheat Variety ◽  
Expected Shortfall ◽  
Wheat Varieties ◽  
Variance Model ◽  
Variety Selection ◽  
Risk Management Strategies ◽  
The Mean ◽  
Mean Variance

AbstractOne of the most popular risk management strategies for wheat producers is varietal diversification. Previous studies proposed a mean-variance model as a tool to optimally select wheat varieties. However, this study suggests that the mean–expected shortfall (ES) model (which is based on a downside risk measure) may be a better tool because variance is not a correct risk measure when the distribution of wheat variety yields is multivariate nonnormal. Results based on data from Texas Blacklands confirm our conjecture that the mean-ES framework performs better in term of selecting wheat varieties than the mean-variance method.

scholarly journals Estratégia Long-Short, Neutra ao Mercado, e Index Tracking Baseadas em Portfólios Cointegrados

2010 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
João Frois Caldeira ◽  
Marcelo Savino Portugal
Keyword(s):  
Ad Hoc ◽  
Tracking Error ◽  
Covariance Matrices ◽  
Return Level ◽  
Index Tracking ◽  
Optimal Portfolios ◽  
The Mean ◽  
Efficient Allocations ◽  
Mean Variance ◽  
Tracking Portfolios

The traditional models to optimize portfolios based on mean-variance analysis aim to determine the portfolio weights that minimize the variance for a certain return level. The covariance matrices used to optimize are difficult to estimate and ad hoc methods often need to be applied to limit or smooth the mean-variance efficient allocations recommended by the model. Although the method is efficient, the tracking error isn’t certainly stationary, so the portfolio can get distant from the benchmark, requiring frequent re-balancements. This work uses cointegration methodology to devise two quantitative strategies: index tracking and long-short market neutral. We aim to design optimal portfolios acquiring the asset prices’ co-movements. The results show that the devise of index tracking portfolios using cointegration generates goods results, replicating the benchmark’s return and volatility. The long-short strategy generated stable returns under several market circumstances, presenting low volatility.

scholarly journals Spatiotemporal characterization of Ensemble Prediction Systems – the Mean-Variance of Logarithms (MVL) diagram

2008 ◽  
Vol 15 (1) ◽  
pp. 109-114 ◽  
Author(s):  
J. M. Gutiérrez ◽  
C. Primo ◽  
M. A. Rodríguez ◽  
J. Fernández
Keyword(s):  
Weather Prediction ◽  
The Other ◽  
Ensemble Prediction ◽  
Spatiotemporal Evolution ◽  
Novel Approach ◽  
The Mean ◽  
The Ensemble Method ◽  
Prediction Systems ◽  
Mean Variance

Abstract. We present a novel approach to characterize and graphically represent the spatiotemporal evolution of ensembles using a simple diagram. To this aim we analyze the fluctuations obtained as differences between each member of the ensemble and the control. The lognormal character of these fluctuations suggests a characterization in terms of the first two moments of the logarithmic transformed values. On one hand, the mean is associated with the exponential growth in time. On the other hand, the variance accounts for the spatial correlation and localization of fluctuations. In this paper we introduce the MVL (Mean-Variance of Logarithms) diagram to intuitively represent the interplay and evolution of these two quantities. We show that this diagram uncovers useful information about the spatiotemporal dynamics of the ensemble. Some universal features of the diagram are also described, associated either with the nonlinear system or with the ensemble method and illustrated using both toy models and numerical weather prediction systems.

Nuances of Media Planning in New Media Age

2019 ◽  
pp. 151-170
Author(s):  
Anandan Pandiyan Pillai
Keyword(s):  
New Media ◽  
Marketing Strategy ◽  
Planning Process ◽  
Future Research ◽  
Media Planning ◽  
Communication Plan ◽  
Extant Literature ◽  
Planning Approach ◽  
The Media ◽  
Research Recommendations

This chapter aims to highlight the premises of traditional media planning process, create cognizance about the challenges that media planner today faces. A brief overview of extant literature on media planning, new media, is discussed. The author discusses media planning approach needs to consider marketing funnel and communication plan in consideration while building media plans. Further the chapter suggests a few additional steps that media planners need to consider during their future media planning exercises, given the increasing complexities in media consumption space. The author has mapped each of the media planning stages with the marketing funnel stage to emphasize that media planning is not an independent activity but needs to be closely stitched with the overall marketing strategy. Finally, future research recommendations are suggested.

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