Portfolio E-V efficient: Bibliometrics of developments considering simulation or risk metrics with increased objective space

Authors

DOI:

https://doi.org/10.33448/rsd-v10i4.13802

Keywords:

Portfolio E-V efficient; Optimization; Simulation; Monte Carlo; Empirical probability distribution; Risk metric; Increased objective space; Bibliometry.

Abstract

Scientific production and the need for indicators that quantify it have strengthened and grown in the last decades, as well as tools that help in their measurement and the search for techniques to analyze in a temporal way, among others, the volume of publications, authors, citations and citations of references. Bibliometry is an important quantitative and statistical technique for measuring production indexes and scientific knowledge using software, keywords of interest and titles to carry out bibliometrics surveys on platforms for repositories of scientific articles, theses and books. The work aims to analyze the veracity of two assumptions about the evolution, to date, of Markowitz's theory of portfolio model E-V efficient. The first is whether there are evolutions of the model, with simulation by the Monte Carlo method, that use simulation as an end to obtain the empirical probability distribution of all the optimal results inherent to the simulation and not, by the usual method of data concentration, being just a means to obtain better results and models, to compare models or to analyze the results of the models or even to project the results of the models in holdout windows. The second is whether there are evolutions of the model using risk metrics with increased objective space, allowing the estimation of a greater number of parameters. From software for bibliometric analysis it was possible to reach the conclusion that in the analyzed theory, and in its evolution to date, the two assumptions are not true.

References

Abensur, E. O., Moreira, D. F., & Faria A. C. R. (2020). GeometricC Brownian Motion: An Alternative to High-Frequency Trading for Small Investors. Independent Journal of Management & Production (IJM&P), 10.14807/ijmp.v11i3.1114. http://www.ijmp.jor.br.

Adhikari, R., Putnam, K. J., & Panta, H. (2020). Robust Optimization-Based Commodity Portfolio Performance. Internacional Jounal of. Financial Studies, 8, p. 54, 2020.

Allen, D., Lizieri, C., & Satchell, S. (2019). In Defense of Portfolio Optimisation What If We Can Forecast? https://ssrn.com/abstract=3373594.

Andrade, F. (2012). Análise Bibliométrica da produção científica de pesquisadores e referências de um período da engenharia de produção. Universidade Federal do Rio Grande do Sul, 2012.

André, C. (2012) Análise Bibliométrica do Periódico Científico Trans informação. Universidade Federal de Santa Catarina, 2012.

Araújo, C. (2006). Bibliometria: evolução histórica e questões atuais. Em Questão, 12(1), 11-32. 2006.

Aziz, N. S. A., Spyridon V., S., & Hasim, H. M. (2019). Evaluation of multivariate GARCH models in an optimal asset allocation framework. North American Journal of Economics and Finance, 47 (2019) 568–596.

Bahia, E., Santos, R., & Blattmann, U. (2011). Estudo Bibliométrico sobre preservação digital: Library and information science abstracts – LISA. Enc. Bibli: R. Eletr. Bibliotecon. Ci. Inf..

Ban, G., Karoui, N. E., & Lim, A. E. B. (2016). Machine Learning and Portfolio Optimization. Published online in Management Science Articles in Advance, 21 Nov 2016. http://dx.doi.org/10.1287/mnsc.2016.2644.

Banihashemi, S., Azarpour, A. M., & Navvabpour, H. (2016). Portfolio Optimization by Mean-Value at Risk Framework. Applied Mathematics & Information Sciences, 10(5), 1935-1948 (2016).

Becker, F., Gurtler, M., & Hibbelin M. (2015). Markowitz versus Michaud Portfolio Optimization Strategies Reconsidered. (2015), European Journal of. Finance, 21, 269-291.

Begoña F. (2016). Bootstrap estimation of the efficient frontier. Computer Management Science, (2016) 13:541–570. 10.1007/s10287-016-0257-2.

Begusiae, S. & Kostanjèar, Z. (2020). Cluster-Specific Latent Factor Estimation in High-Dimensional Financial Time Series. in IEEE Access, 8, 164365-164379, 2020, 10.1109/ACCESS.2020.3021898.

Bianchi, D., & Guidoliny, M. (2013). Can Long-Run Dynamic Optimal Strategies Outperform Fixed-Mix Portfolios? Evidence from Multiple Data Sets. http://ssrn.com/abstract=2353761.

Bianchi, M. L., & Tassinari, G. L. (2018). Forward-looking portfolio selection with multivariate non-Gaussian models and the Esscher transform. arXiv:1805.05584v2.

Caporin, M. (2014). A Survey on the Four Families of Performance Measures. Journal of Economic Surveys, (2014) 28(5), 917–942.

Café, L., & Brascher, M. (2008). Organização da Informação e Bibliometria. Enc. Bibli: R. Eletr. Bibliotecon. Ci. Inf..

Chakkalakal, L., Hommel, U., & Li, W. (2018). Transport infrastructure equities in mixed-asset portfolios: estimating risk with a Garch-Copula CVaR model, Journal of Property Research, 35: 2, 117-138, 10.1080/09599916.2018.1461126.

Christopher, W., & Millery, I. Y. (2017). Optimal Control of Conditional Value-at-Risk in Continuous Time. arXiv:1512.05015v3.

Chueke, G., & Amatucci, M. (2015). O que é bibliometria? Uma introdução ao Fórum. 11(2), 1-5. 2015.

Cong, F., & Oosterlee, C. W. (2016). Multi-period mean–variance portfolio optimization based on Monte-Carlo simulation. Journal of Economic Dynamics & Control, 64(2016)23–38.

DeMiguel, V., Garlappi, L., & Uppal, R., (2009). Optimal Versus Naive Diversification: How Inefficient is the 1/N Portfolio Strategy? The Review of Financial Studies 22(5)2009. 10.1093/rfs/hhm075.

DeMiguel, V., Martin-Utrera, A., & Nogales, F. J. (2013). Size matters: Optimal calibration of shrinkage estimators for portfolio Selection. Journal of Banking & Finance, 37 (2013) 3018–3034.

Domingues, M. A. (2018). Mapeamento da Ciência com o Pacote R Bibliometrix: Uma aplicação no estudo de Empreendedorismo Acadêmico. Proceeding of ISTI/SIMTEC (2018), 10.7198/S2318-3403201800010033, 9(1), 287-294.

Du, Z., & Pei, P. (2020). Backtesting Portfolio Value-at-Risk with Estimated Portfólio Weights. Journal of. Time Series. Analysis, 41: 605–619 (2020). Published online 06April 2020 inWileyOnline Library (wileyonlinelibrary.com) 10.1111/jtsa.12524.

Eckert, C., Gatzert, N., & Heidinger, D. (2020). Empirically assessing and modeling spillover effects from operational risk events in the insurance industry. Insurance: Mathematics and Economics, 93, 72–83, 2020.

Edirisinghe, N. C. P., & Zhang, X. (2010). Input/output selection in DEA under expert information with application to financial markets. European Journal of Operational Research 207 (2010) 1669–1678.

Ekblom, J., & Blomvall J. (2020). Importance sampling in stochastic optimization: An application to intertemporal portfolio choice. European Journal of Operational Research, 285, 106-119, 2020.

Fan, J., Zhang, J., & Yu, K. (2012). Vast Portfolio Selection with Gross-Exposure Constraints. Journal of the American Statistical Association, 107: 498, 592-606.

Fortin, J. H. J. (2015). Downside loss aversion:Winner or loser? Math Meth Oper Res, (2015) 81:181–233. 10.1007/s00186-015-0493-1.

Hilario-Caballero, A. et al. (2020). Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach. International Journal of Environmental Research and. Public Health, 17, 6324, 10.3390/ijerph17176324.

Huang, S., & Lin, T. (2018). A Linearization of the Portfolio Optimization Problem with General Risk Measures Under Multivariate Conditional Heteroskedastic Models. Asia-Pacific Journal of Financial Studies, 47, 449-469. 10.1111/ajfs.12218.

Jiang, G., Hong, L. J., & Nelson, B. L. (2020). Online Risk Monitoring Using Offline Simulation. INFORMS Journal on Computing, 32(2), 356-375. https://doi.org/10.1287/ijoc.2019.0892.

Kaczmarek, K., Dymova, L., & Sevastjanov, P. (2020). A Simple View on the Interval and Fuzzy Portfolio Selection Problems. Entropy, 22, 932, 10.3390/e22090932.

Klimenka, F., & Wolter, J. L. (2019). Multiple Regression Model Averaging and the Focused Information Criterion with an Application to Portfolio Choice. Journal of Business & Economic Statistics, 37: 3, 506-516, 10.1080/07350015.2017.1383262.

Kolm, P. N., Tütüncü, R., & Fabozzi, F. J. (2014). 60 Years of portfolio optimization: Practical challenges and current trends. European Journal of Operational Research 234 (2014) 356–371.

Kwong, R., & Low, Y. (2015). Vine copulas: Modeling systemic risk and enhancing higher-moment portfolio optimization. http://ssrn.com/abstract=2259076.

Lai, S. et al, (2019). Gas Generation Portfolio Management Strategy Based on Financial Derivatives: Options. 2019 9th International Conference on Power and Energy Systems (ICPES), 1-6, 10.1109/ICPES47639.2019.9105461.

Leal, R. P. C., & Mendes, B. V. M. (2013). Assessing the effect of tail dependence in portfolio allocations. Applied Financial Economics, 2013 23(15), 1249–1256, http://dx.doi.org/10.1080/09603107.2013.804160.

Li, X., & Qin, Z. (2014). Interval portfolio selection models within the framework of uncertainty theory. Economic Modelling 41 (2014) 338–344.

Lin, P. (2012). Portfolio Optimization and Risk Measurement Based on Non-Dominated Sorting Genetic Algorithm. Journal of Industrial and Management Optimization, 8(3), 10.3934/jimo.2012.8.549.

Liu, Y., Zhang, W., & Zhang, P. (2013). A multi-period portfolio selection optimization model by using interval analysis. Economic Modelling, 33 (2013) 113–119.

Mahdi, M., Masoud, M., & Alireza A., K., (2020). Development of an efficient cluster‑based portfolio optimization model under realistic market conditions. Empirical Economics, (2020), 59:2423–2442. https://doi.org/10.1007/s00181-019-01802-5.

Mello, I., Barbosa, K., Dantas, J., & Botelho, D. (2015). 25 Anos de Publicação em Auditoria: Análise Bibliométrica com Ênfase na Lei de Lotka, Lei de Zipf e Ponto de Transição (T) de Goffman. Congresso de Contabilidade, Universidade Federal de Santa Catarina. 2015.

Mugnaini, R. (2004). Indicadores bibliométricos da produção científica brasileira: uma análise a partir da base Pascal, Ci. Inf., Brasília, 33(2), 123-131, 2004.

Münnix, M. C., Schäfer, R., & Grothe, O. (2014). Estimating correlation and covariance matrices by weighting of market similarity. Quantitative Finance, 14: 5, pp. 931-939, 10.1080/14697688.2011.605075.

Naccarato, A., & Pierini, A. (2014). Element-by-element estimation of a volatility matrix. An Italian portfolio simulation. Investment Management and Financial Innovations, 11(3), 2014.

Otlet, P. (1934). Traité de documentatión: Le Livre Sur Le Livre – Théorie et Pratique. Éditeurs-Imprimeurs D. Van Keerberghen & Fils, Editones Mundaneum, Palais Mondial. https://lib.ugent.be/fulltxt/handle/1854/5612/Traite_de_documentation_ocr.pdf.

Owen W. S. (2015). Foreign Currency Exposure within Country Exchange Traded Funds. Frontiers in Finance, 1, 2015.

Penteado, R. (2002). Aplicação da Bibliometria na Construção de Indicadores sobre a Produção Científica da Embrapa. Workshop Brasileiro de Inteligência Competitiva e Gestão do Conhecimento. 2002.

Pimenta, A., Portela, A., Oliveira, C., & Ribeiro, R. (2017). A Bibliometria nas pesquisas acadêmicas. Revista de ensino, pesquisa e extensão, 4(7), 2017.

Pizzani, L., Silva, R., & Hossne, W. (2010). Análise bibliométrica dos 40 anos da produção científica em Bioética no Brasil e no mundo. Revista - Centro Universitário São Camilo. 2010.

Platanakis, E., Sutcliffe, C., & Ye, X. (2020). Horses for Courses: Mean-Variance for Asset Allocation and 1/N for Stock Selection. https://ssrn.com/abstract=3372334.

Post, T., Karabati, S., & Arvanitis, S. (2018). Portfolio optimization based on stochastic dominance and empirical likelihood. Journal of Econometrics, 206 (2018) 167–186.

Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 24(4), 348-349. SCOPUS. https://www-scopus.ez20.periodicos.capes.gov.br/search/form.uri?display=basic.

Resta, M. (2012). Portfolio optimization with dimension reduction techniques: A comprehensive simulation study. Neurocomputing: Learning, Architectures and Modeling, 9, 93-118, 2012.

Ruidi S., & Yue C. (2020). A New Adaptive Entropy Portfolio Selection Model. Entropy. 22, 951, 10.3390/e22090951.

Salah, H. B. et al. (2018). Mean and median-based nonparametric estimation of returns in mean-downside risk portfolio frontier. Ann Oper Res., (2018) 262:653–681. https://doi.org/10.1007/s10479-016-2235-z.

Santamaría, R., Aguarón, J., & Moreno-Jiménez, J. M. (2020). A multicriteria approach based on Analytic Hierarchy Process and compromise programming in portfolio selection. J Multi-Crit Decis Anal. 2020, 27, 141–146. 10.1002/mcda.1699.

Scarpel, L. (2016). Pesquisa Científica. Instituto Tecnológico de Aeronáutica Engenharia Mecânica-Aeronáutica (IEM), Departamento de gestão e suporte à decisão centro de gestão em engenharia. 2016.

Shadabfar, M., & Cheng, L. (2020). Probabilistic approach for optimal portfolio selection using a hybrid Monte Carlo simulation and Markowitz model. Alexandria Engineering Journal, 59, 3381-3393, 2020.

Shen, W. et al. (2019). The Kelly Growth Optimal Portfolio with Ensemble Learning. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).

Shinzato, T. (2016). Replica Analysis for the Duality of the Portfolio Optimization Problem. arXiv:1609.05475v1.

Silva, F., Santos, B. M. E., & Vils, L. (2016). Estudo Bibliométrico: Orientações sobre sua Aplicação. Revista Brasileira de Marketing, 2016.

Soares, P., Carneiro, T., Calmon, Castro, J., &Otávio L. (2016). Análise bibliométrica da produção científica brasileira sobre Tecnologia de Construção e Edificações na base de dados Web of Science. Ambiente Construído, Porto Alegre, 16(1), 175-185. 2016.

Sui, Y., Hu, J., & Ma, F. (2020). A Mean-Variance Portfolio Selection Model with Interval-Valued Possibility Measures. Mathematical Problems in Engineering, 12. https://doi.org/10.1155/2020/4135740.

Sun, R., Ma, T., & Liu, S. (2018). A Stein-type shrinkage estimator of the covariance matrix for portfolio selections. Metrika (2018), 81, 931-952. https://doi.org/10.1007/s00184-018-0663-2.

Tang, L., & Ling, A. (2014). Closed-Form Solution for Robust Portfolio Selection with Worst-Case CVaR Risk Measure. Mathematical Problems in Engineering, Volume 2014, Article ID 494575, 9 pages. http://dx.doi.org/10.1155/2014/494575.

Torres, O. T., & Enciso, I. M. T (2017). Is socially responsible investment useful in Mexico? A multi-factor and ex-ante review. Contaduría y Administración, 62 (2017) pp. 222–238.

Vasyl, G. V., et al. (2020). Statistical inferences for realized portfolio weights. Econometrics and Statistics, 14, 49-62, 2020.

Vanti, N. A. P. (2002) Da bibliometria à webometria: uma exploração conceitual dos mecanismos utilizados para medir o registro da informação e a difusão do conhecimento. Ciência da Informação, Brasília, 31(2), 152-162.

Wallin, M. W. The bibliometric structure of spin-off literature. Innovation: Management, Policy, & Practice, 14(2), 162(16), 2012.

Wang, C. D., et al. (2020). Asset selection based on high frequency Sharpe ratio. Journal of Econometrics (2020), https://doi.org/10.1016/j.jeconom.2020.05.007.

Xiao, H., Ren, T., & Ren, T. (2020). Estimation of fuzzy portfolio efficiency via an improved DEA approach. INFOR: Information Systems and Operational Research, 58: 3, pp. 478-510, 10.1080/03155986.2020.1734904.

Xinxin, J., & Jianjun, G. (2016). Extensions of Black-Litterman portfolio optimization model with downside risk measure. IEEE, 978-1-4673-9714-8/16/$31.00© 2016.

Yamada, Y., & Primbs, J.A. (2018). Model Predictive Control for Optimal Pairs Trading Portfolio with Gross Exposure and Transaction Cost Constraints. Asia-Pacific Finan Markets, 25. 1–21, 2018. https://doi.org/10.1007/s10690-017-9236-z.

Yan, D., Hu, Y. & Lai, K. A. (2018). A Nonlinear Interval Portfolio Selection Model and Its Application in Banks. J Syst Sci Complex 31, 696-733 (2018). https://doi.org/10.1007/s11424-017-6070-3.

Yu, J., Chiou, W. P., & Mu, D. (2015). A linearized value-at-risk model with transaction costs and short selling. European Journal of Operational Research 247 (2015) 872–878.

Yua, X. et al. (2020). Hedging the exchange rate risk for international portfolios. Mathematics and Computers in Simulation, 173, 85-104, 2020.

Zhao, L., Chakrabarti, D., & Muthuraman, K. (2019). Portfolio Construction by Mitigating Error Amplification: The Bounded-Noise Portfolio. Operations Research, 67(4): 965-983. https://doi.org/10.1287/opre.2019.1858.

Zhou, Z. et al. (2018). Time-Consistent Strategies for Multi-Period Portfolio Optimization with/without the Risk-Free Asset. Mathematical Problems in Engineering, 20 https://doi.org/10.1155/2018/7563093.

Zhu, Y., Yu, P. L. H., & Mathew, T. (2020). Improved estimation of optimal portfolio with an application to the US stock market. Journal of Statistical Theory and Practice, 14(1). https://doi.org/10.1007/s42519-019-0067-2.

Published

24/04/2021

How to Cite

MENDES, M. H. .; SOUZA, R. C. .; SANFINS, M. A. .; SILVA, T. E. B. de C. .; MARTINS, L. M. Portfolio E-V efficient: Bibliometrics of developments considering simulation or risk metrics with increased objective space. Research, Society and Development, [S. l.], v. 10, n. 4, p. e57310413802, 2021. DOI: 10.33448/rsd-v10i4.13802. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/13802. Acesso em: 20 apr. 2024.

Issue

Section

Exact and Earth Sciences