Geometric Representation of the Mean-Variance-Skewness Portfolio Frontier Based upon the Shortage Function

The literature suggests that investors prefer portfolios based on mean, variance and skewness rather than portfolios based on mean-variance (MV) criteria solely. Furthermore, a small variety of methods have been proposed to determine mean-variance-skewness (MVS) optimal portfolios. Recently, the shortage function has been introduced as a measure of efficiency, allowing to characterize MVS optimalportfolios using non-parametric mathematical programming tools. While tracing the MV portfolio frontier has become trivial, the geometric representation of the MVS frontier is an open challenge. A hitherto unnoticed advantage of the shortage function is that it allows to geometrically represent the MVS portfolio frontier. The purpose of this contribution is to systematically develop geometric representations of the MVS portfolio frontier using the shortage function and related approaches.shortage function, efficient frontier, mean-variance-skewness efficiency

Advancing Strategy: How to Lead Change in Corporate Societal Engagement

Implementing a strategy may be even harder than developing it. This learning brief is intended for corporate foundation and CSR leaders who have completed an initial strategy refresh process and who seek effecitve practices and tools to advance this strategy. In our experience advising more than 100 multinational companie, effective leaders facilitate structured, data-informed decisions and enable important organizational improvements to achieve their strategic objectives. Specifically, advancing strategy in corporate societal engagement typically requires leading change in two major areas of the overall portfolio: designing a signative initiative and transforming local giving

Ensemble Committees for Stock Return Classification and Prediction

This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a non-linear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.Comment: 15 pages, 4 figures, Neukom Institute Computational Undergraduate Research prize - second plac

An EPIIC Vision to Evolve Project Integration, Innovation, and Collaboration with Broad Impact for How NASA Executes Complex Projects

Evolving Project Integration, Innovation, and Collaboration (EPIIC) is a vision defined to transform the way projects manage information to support real-time decisions, capture best practices and lessons learned, perform assessments, and manage risk across a portfolio of projects. The foundational project management needs for data and information will be revolutionized through innovations on how we manage and access that data, implement configuration control, and certify compliance. The embedded intelligence of new interactive data interfaces integrate technical and programmatic data such that near real time analytics can be accomplished to more efficiently and accurately complete systems engineering and project management tasks. The system-wide data analytics that are integrated into customized data interfaces allows the growing team of engineers and managers required to develop and implement major NASA missions the ability to access authoritative source(s) of system information while greatly reducing the labor required to complete system assessments. This would allow, for example, much of what is accomplished in a scheduled design review to take place as needed, between any team members, at any time. An intelligent data interface that rigorously integrates systems engineering and project management information in near real time can provide substantially greater insight for systems engineers, project managers, and the large diverse teams required to complete a complex project. System engineers, programmatic personnel (those who focus on cost, schedule, and risk), the technical engineering disciplines, and project management can realize immediate benefit from the shared vision described herein. Implementation of the vision also enables significant improvements in the performance of the engineered system being developed

Computing the Mean-Variance-Sustainability Nondominated Surface by ev-MOGA

[EN] Despite the widespread use of the classical bicriteria Markowitz mean-variance framework, a broad consensus is emerging on the need to include more criteria for complex portfolio selection problems. Sustainable investing, also called socially responsible investment, is becoming a mainstream investment practice. In recent years, some scholars have attempted to include sustainability as a third criterion to better reflect the individual preferences of those ethical or green investors who are willing to combine strong financial performance with social benefits. For this purpose, new computational methods for optimizing this complex multiobjective problem are needed. Multiobjective evolutionary algorithms (MOEAs) have been recently used for portfolio selection, thus extending the mean-variance methodology to obtain a mean-variance-sustainability nondominated surface. In this paper, we apply a recent multiobjective genetic algorithm based on the concept of epsilon-dominance called ev-MOGA. This algorithm tries to ensure convergence towards the Pareto set in a smart distributed manner with limited memory resources. It also adjusts the limits of the Pareto front dynamically and prevents solutions belonging to the ends of the front from being lost. Moreover, the individual preferences of socially responsible investors could be visualised using a novel tool, known as level diagrams, which helps investors better understand the range of values attainable and the tradeoff between return, risk, and sustainability.This work was funded by "Ministerio de Economia y Competitividad" (Spain), research project RTI2018-096904B-I00, and "Conselleria de Educacion, Cultura y DeporteGeneralitat Valenciana" (Spain), research project AICO/2019/055Garcia-Bernabeu, A.; Salcedo-Romero-De-Ávila, J.; Hilario Caballero, A.; Pla Santamaría, D.; Herrero Durá, JM. (2019). Computing the Mean-Variance-Sustainability Nondominated Surface by ev-MOGA. Complexity. 2019:1-12. https://doi.org/10.1155/2019/6095712S1122019Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77. doi:10.2307/2975974Hirschberger, M., Steuer, R. E., Utz, S., Wimmer, M., & Qi, Y. (2013). Computing the Nondominated Surface in Tri-Criterion Portfolio Selection. Operations Research, 61(1), 169-183. doi:10.1287/opre.1120.1140Utz, S., Wimmer, M., Hirschberger, M., & Steuer, R. E. (2014). Tri-criterion inverse portfolio optimization with application to socially responsible mutual funds. European Journal of Operational Research, 234(2), 491-498. doi:10.1016/j.ejor.2013.07.024Utz, S., Wimmer, M., & Steuer, R. E. (2015). Tri-criterion modeling for constructing more-sustainable mutual funds. European Journal of Operational Research, 246(1), 331-338. doi:10.1016/j.ejor.2015.04.035Qi, Y., Steuer, R. E., & Wimmer, M. (2015). An analytical derivation of the efficient surface in portfolio selection with three criteria. Annals of Operations Research, 251(1-2), 161-177. doi:10.1007/s10479-015-1900-yGasser, S. M., Rammerstorfer, M., & Weinmayer, K. (2017). Markowitz revisited: Social portfolio engineering. European Journal of Operational Research, 258(3), 1181-1190. doi:10.1016/j.ejor.2016.10.043Qi, Y. (2018). On outperforming social-screening-indexing by multiple-objective portfolio selection. Annals of Operations Research, 267(1-2), 493-513. doi:10.1007/s10479-018-2921-0Nathaphan, S., & Chunhachinda, P. (2010). Estimation Risk Modeling in Optimal Portfolio Selection: An Empirical Study from Emerging Markets. Economics Research International, 2010, 1-10. doi:10.1155/2010/340181DeMiguel, V., Garlappi, L., & Uppal, R. (2007). Optimal Versus Naive Diversification: How Inefficient is the 1/NPortfolio Strategy? Review of Financial Studies, 22(5), 1915-1953. doi:10.1093/rfs/hhm075Metaxiotis, K., & Liagkouras, K. (2012). Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review. Expert Systems with Applications, 39(14), 11685-11698. doi:10.1016/j.eswa.2012.04.053Bertsimas, D., & Shioda, R. (2007). Algorithm for cardinality-constrained quadratic optimization. Computational Optimization and Applications, 43(1), 1-22. doi:10.1007/s10589-007-9126-9Chang, T.-J., Yang, S.-C., & Chang, K.-J. (2009). Portfolio optimization problems in different risk measures using genetic algorithm. Expert Systems with Applications, 36(7), 10529-10537. doi:10.1016/j.eswa.2009.02.062Woodside-Oriakhi, M., Lucas, C., & Beasley, J. E. (2011). Heuristic algorithms for the cardinality constrained efficient frontier. European Journal of Operational Research, 213(3), 538-550. doi:10.1016/j.ejor.2011.03.030Chen, B., Lin, Y., Zeng, W., Xu, H., & Zhang, D. (2017). The mean-variance cardinality constrained portfolio optimization problem using a local search-based multi-objective evolutionary algorithm. Applied Intelligence, 47(2), 505-525. doi:10.1007/s10489-017-0898-zLiagkouras, K. (2019). A new three-dimensional encoding multiobjective evolutionary algorithm with application to the portfolio optimization problem. Knowledge-Based Systems, 163, 186-203. doi:10.1016/j.knosys.2018.08.025Kaucic, M., Moradi, M., & Mirzazadeh, M. (2019). Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures. Financial Innovation, 5(1). doi:10.1186/s40854-019-0140-6Silva, Y. L. T. V., Herthel, A. B., & Subramanian, A. (2019). A multi-objective evolutionary algorithm for a class of mean-variance portfolio selection problems. Expert Systems with Applications, 133, 225-241. doi:10.1016/j.eswa.2019.05.018Anagnostopoulos, K. P., & Mamanis, G. (2009). Multiobjective evolutionary algorithms for complex portfolio optimization problems. Computational Management Science, 8(3), 259-279. doi:10.1007/s10287-009-0113-8Ehrgott, M., Klamroth, K., & Schwehm, C. (2004). An MCDM approach to portfolio optimization. European Journal of Operational Research, 155(3), 752-770. doi:10.1016/s0377-2217(02)00881-0Steuer, R. E., Qi, Y., & Hirschberger, M. (2006). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297-317. doi:10.1007/s10479-006-0137-1Anagnostopoulos, K. P., & Mamanis, G. (2010). A portfolio optimization model with three objectives and discrete variables. Computers & Operations Research, 37(7), 1285-1297. doi:10.1016/j.cor.2009.09.009Hallerbach, W. (2004). A framework for managing a portfolio of socially responsible investments. European Journal of Operational Research, 153(2), 517-529. doi:10.1016/s0377-2217(03)00172-3Ballestero, E., Bravo, M., Pérez-Gladish, B., Arenas-Parra, M., & Plà-Santamaria, D. (2012). Socially Responsible Investment: A multicriteria approach to portfolio selection combining ethical and financial objectives. European Journal of Operational Research, 216(2), 487-494. doi:10.1016/j.ejor.2011.07.011Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313-325. doi:10.1016/j.ejor.2013.11.031Calvo, C., Ivorra, C., & Liern, V. (2014). Fuzzy portfolio selection with non-financial goals: exploring the efficient frontier. Annals of Operations Research, 245(1-2), 31-46. doi:10.1007/s10479-014-1561-2Laumanns, M., Thiele, L., Deb, K., & Zitzler, E. (2002). Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation, 10(3), 263-282. doi:10.1162/106365602760234108Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.01

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Where should livestock graze? Integrated modeling and optimization to guide grazing management in the Cañete basin, Peru

Integrated watershed management allows decision-makers to balance competing objectives, for example agricultural production and protection of water resources. Here, we developed a spatially-explicit approach to support such management in the Cañete watershed, Peru. We modeled the effect of grazing management on three services – livestock production, erosion control, and baseflow provision – and used an optimization routine to simulate landscapes providing the highest level of services. Over the entire watershed, there was a trade-off between livestock productivity and hydrologic services and we identified locations that minimized this trade-off for a given set of preferences. Given the knowledge gaps in ecohydrology and practical constraints not represented in the optimizer, we assessed the robustness of spatial recommendations, i.e. revealing areas most often selected by the optimizer. We conclude with a discussion of the practical decisions involved in using optimization frameworks to inform watershed management programs, and the research needs to better inform the design of such programs