6 research outputs found

    Resampled efficient frontier integration for MOEAs

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    This article belongs to the Section Multidisciplinary Applications.Mean-variance portfolio optimization is subject to estimation errors for asset returns and covariances. The search for robust solutions has been traditionally tackled using resampling strategies that offer alternatives to reference sets of returns or risk aversion parameters, which are subsequently combined. The issue with the standard method of averaging the composition of the portfolios for the same risk aversion is that, under real-world conditions, the approach might result in unfeasible solutions. In case the efficient frontiers for the different scenarios are identified using multiobjective evolutionary algorithms, it is often the case that the approach to averaging the portfolio composition cannot be used, due to differences in the number of portfolios or their spacing along the Pareto front. In this study, we introduce three alternatives to solving this problem, making resampling with standard multiobjective evolutionary algorithms under real-world constraints possible. The robustness of these approaches is experimentally tested on 15 years of market data.This research was funded by Spanish Ministry of Education under grant number CAS15/0025

    Combining RMT-based filtering with time-stamped resampling for robust portfolio optimization

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    Finding the optimal weights for a set of financial assets is a difficult task. The mix of real world constrains and the uncertainty derived from the fact that process is based on estimates for parameters that likely to be inaccurate, often result in poor results. This paper suggests that a combination of a filtering mechanism based on random matrix theory with time-stamped resampled evolutionary multiobjective optimization algorithms enhances the robustness of forecasted efficient frontiers.The authors acknowledge financial support granted by the Spanish Ministry of Economy and competitivity under grant ENE2014-56126-C2-2-R

    Combining RMT-based filtering with time-stamped resampling for robust portfolio optimization

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    Finding the optimal weights for a set of ï¬nancial assets is a difï¬cult task. The mix of real world constrains and the uncertainty derived from the fact that process is based on estimates for parameters that likely to be inaccurate, often result in poor results. This paper suggests that a combination of a ï¬ltering mechanism based on random matrix theory with time-stamped resampled evolutionary multiobjective optimization algorithms enhances the robustness of forecasted efï¬cient frontiers

    SIS 2017. Statistics and Data Science: new challenges, new generations

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    The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data
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