Production
http://www.production.periodikos.com.br/article/doi/10.1590/0103-6513.20190144
Production
Systematic Review

Analysis of new approaches used in portfolio optimization: a systematic literature review

Danilo Alcantara Milhomem; Maria José Pereira Dantas

Downloads: 0
Views: 3

Abstract

Abstract: Paper aims: To do a comprehensive review of the exact and heuristic methods, software/programming languages, constraints, and types of analysis (technical and fundamental) used to solve the portfolio optimization problem.

Originality: The paper presents a useful discussion on aspects of portfolio optimization, both for researchers and investors and for finance professionals.

Research method: A systematic literature review was performed, and the articles were compiled according to pre-established criteria/filters.

Main findings: A point of attention should be given to the input data of optimization models. Depending on the degree of the estimation error of these input parameters, the optimization results may be lower than the results of the 1/N trading strategy.

Implications for theory and practice: Robust optimization, Fuzzy logic, and prediction are examples of techniques used to reduce estimation errors. At the end of the article are pointed out trends and some gaps for future work.

Keywords

Portfolio selection, Heuristics, Constraints, Stock market

References

Ackermann, F., Pohl, W., & Schmedders, K. (2016). Optimal and naive diversification in currency markets. Management Science, 63(10), 3347-3360. http://dx.doi.org/10.1287/mnsc.2016.2497.

Al Janabi, M. A. M. (2014). Optimal and investable portfolios: an empirical analysis with scenario optimization algorithms under crisis market prospects. Economic Modelling, 40, 369-381. http://dx.doi.org/10.1016/j.econmod.2013.11.021.

Algarvio, H., Lopes, F., Sousa, J., & Lagarto, J. (2017). Multi-agent electricity markets: retailer portfolio optimization using Markowitz theory. Electric Power Systems Research, 148, 282-294. http://dx.doi.org/10.1016/j.epsr.2017.02.031.

Ali, Ö. G., Akçay, Y., Sayman, S., Yılmaz, E., & Özçelik, M. H. (2016). Cross-selling investment products with a Win-win perspective in portfolio optimization. Operations Research, 65(1), 55-74. http://dx.doi.org/10.1287/opre.2016.1556.

Ayub, U., Shah, S. Z. A., & Abbas, Q. (2015). Robust analysis for downside risk in portfolio management for a volatile stock market. Economic Modelling, 44, 86-96. http://dx.doi.org/10.1016/j.econmod.2014.10.001.

Babaei, S., Sepehri, M. M., & Babaei, E. (2015). Multi-objective portfolio optimization considering the dependence structure of asset returns. European Journal of Operational Research, 244(2), 525-539. http://dx.doi.org/10.1016/j.ejor.2015.01.025.

Ban, G.-Y., El Karoui, N., & Lim, A. E. B. (2016). Machine learning and portfolio optimization. Management Science, 64(3), 1136-1154. http://dx.doi.org/10.1287/mnsc.2016.2644.

Bastos, L. D. S. L., Mendes, M. L., Nunes, D. R. D. L., Melo, A. C. S., & Carneiro, M. P. (2017). A systematic literature review on the joint replenishment problem solutions: 2006-2015. Production, 27(0), 27. http://dx.doi.org/10.1590/0103-6513.222916.

Behr, P., Guettler, A., & Miebs, F. (2013). On portfolio optimization: imposing the right constraints. Journal of Banking & Finance, 37(4), 1232-1242. http://dx.doi.org/10.1016/j.jbankfin.2012.11.020.

Benati, S. (2015). Using medians in portfolio optimization. The Journal of the Operational Research Society, 66(5), 720-731. http://dx.doi.org/10.1057/jors.2014.57.

Bensaïda, A., Boubaker, S., & Nguyen, D. K. (2018). The shifting dependence dynamics between the G7 stock markets. Quantitative Finance, 18(5), 801-812. http://dx.doi.org/10.1080/14697688.2017.1419628.

Berutich, J. M., López, F., Luna, F., & Quintana, D. (2016). Robust technical trading strategies using GP for algorithmic portfolio selection. Expert Systems with Applications, 46, 307-315. http://dx.doi.org/10.1016/j.eswa.2015.10.040.

Brodie, J., Daubechies, I., De Mol, C., Giannone, D., & Loris, I. (2009). Sparse and stable Markowitz portfolios. Proceedings of the National Academy of Sciences of the United States of America, 106(30), 12267-12272. http://dx.doi.org/10.1073/pnas.0904287106. PMid:19617537.

Ceren, T. Ş., & Köksalan, M. (2014). Effects of Multiple Criteria on Portfolio Optimization. International Journal of Information Technology & Decision Making, 13(01), 77-99. http://dx.doi.org/10.1142/S0219622014500047.

Chen, C., & Zhou, Y. (2018). Robust multiobjective portfolio with higher moments. Expert Systems with Applications, 100, 165-181. http://dx.doi.org/10.1016/j.eswa.2018.02.004.

Dai, Z., & Wen, F. (2018). Some improved sparse and stable portfolio optimization problems. Finance Research Letters, 27, 46-52. http://dx.doi.org/10.1016/j.frl.2018.02.026.

DeMiguel, V., Garlappi, L., & Uppal, R. (2009a). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? Review of Financial Studies, 22(5), 1915-1953. http://dx.doi.org/10.1093/rfs/hhm075.

DeMiguel, V., Garlappi, L., Nogales, F. J., & Uppal, R. (2009b). A generalized approach to portfolio optimization: improving performance by constraining portfolio norms. Management Science, 55(5), 798-812. http://dx.doi.org/10.1287/mnsc.1080.0986.

Ertenlice, O., & Kalayci, C. B. (2018). A survey of swarm intelligence for portfolio optimization: algorithms and applications. Swarm and Evolutionary Computation, 39, 36-52. http://dx.doi.org/10.1016/j.swevo.2018.01.009.

Fabozzi, F. J., Kolm, P. N., Pachamanova, D. A., & Focardi, S. M. (2007). Robust portfolio optimization. Journal of Portfolio Management, 33(3), 40-48. http://dx.doi.org/10.3905/jpm.2007.684751.

Fan, J., Zhang, J., & Yu, K. (2012). Vast portfolio selection with gross-exposure constraints. Journal of the American Statistical Association, 107(498), 592-606. http://dx.doi.org/10.1080/01621459.2012.682825. PMid:23293404.

Furlan, P. K., & Laurindo, F. J. B. (2017). Agrupamentos epistemológicos de artigos publicados sobre big data analytics. Transinformação, 29(1), 91-100. http://dx.doi.org/10.1590/2318-08892017000100009.

García, F., Guijarro, F., & Oliver, J. (2018). Index tracking optimization with cardinality constraint: a performance comparison of genetic algorithms and tabu search heuristics. Neural Computing & Applications, 30(8), 2625-2641. http://dx.doi.org/10.1007/s00521-017-2882-2.

Goodman, D., & Brette, R. (2008). Brian: a simulator for spiking neural networks in Python. Frontiers in Neuroinformatics, 2, 5. http://dx.doi.org/10.3389/neuro.11.005.2008. PMid:19115011.

Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: a literature review. Applied Soft Computing, 36, 534-551. http://dx.doi.org/10.1016/j.asoc.2015.07.008.

Ji, R., Lejeune, M. A., & Prasad, S. Y. (2017). Properties, formulations, and algorithms for portfolio optimization using Mean-Gini criteria. Annals of Operations Research, 248(1-2), 305-343. http://dx.doi.org/10.1007/s10479-016-2230-4.

Karakalidis, A., & Sifaleras, A. (2017). Solving portfolio optimization problems using AMPL. In: E. Grigoroudis & M. Doumpos (Eds.), Operational research in business and economics (pp. 167-184). Cham: Springer. http://dx.doi.org/10.1007/978-3-319-33003-7_8.

Kumar, D., & Mishra, K. K. (2017). Portfolio optimization using novel co-variance guided Artificial Bee Colony algorithm. Swarm and Evolutionary Computation, 34(2), 353-369. http://dx.doi.org/10.1016/j.swevo.2016.11.003.

Le Thi, H. A., & Moeini, M. (2014). Long-short portfolio optimization under cardinality constraints by difference of convex functions algorithm. Journal of Optimization Theory and Applications, 161(1), 199-224. http://dx.doi.org/10.1007/s10957-012-0197-0.

Levy, M., & Kaplanski, G. (2015). Portfolio selection in a two-regime world. European Journal of Operational Research, 242(2), 514-524. http://dx.doi.org/10.1016/j.ejor.2014.10.012.

Li, Q., & Bao, L. (2014). Enhanced index tracking with multiple time-scale analysis. Economic Modelling, 39, 282-292. http://dx.doi.org/10.1016/j.econmod.2014.03.009.

Liu, Y.-J., & Zhang, W.-G. (2015). A multi-period fuzzy portfolio optimization model with minimum transaction lots. European Journal of Operational Research, 10(2), 143-164. http://dx.doi.org/10.1016/j.ejor.2014.10.061.

Macedo, L. L., Godinho, P., & Alves, M. J. (2017). Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules. Expert Systems with Applications, 79, 33-43. http://dx.doi.org/10.1016/j.eswa.2017.02.033.

Mansini, R., Ogryczak, W., & Speranza, M. G. (2014). Twenty years of linear programming based portfolio optimization. European Journal of Operational Research, 234(2), 518-535. http://dx.doi.org/10.1016/j.ejor.2013.08.035.

Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. http://dx.doi.org/10.1111/j.1540-6261.1952.tb01525.x.

Marzban, S., Mahootchi, M., & Arshadi Khamseh, A. (2015). Developing a multi-period robust optimization model considering American style options. Annals of Operations Research, 233(1), 305-320. http://dx.doi.org/10.1007/s10479-013-1461-x.

McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 1697900(Scipy), 50-59. Retrieved in 2019, November 27, from http://conference.scipy.org/proceedings/scipy2010/pdfs/proceedings.pdf

Meghwani, S. S., & Thakur, M. (2018). Multi-objective heuristic algorithms for practical portfolio optimization and rebalancing with transaction cost. Applied Soft Computing, 67, 865-894. http://dx.doi.org/10.1016/j.asoc.2017.09.025.

Merton, R. C. (1971). Optimum consumption and portfolio rules in a continuous-time model. In Stochastic optimization models in finance (pp. 621-661). New York: Academic Press.

Mishra, S. K., Panda, G., & Majhi, B. (2016). Prediction based mean-variance model for constrained portfolio assets selection using multiobjective evolutionary algorithms. Swarm and Evolutionary Computation, 28, 117-130. http://dx.doi.org/10.1016/j.swevo.2016.01.007.

Mitchell, S., O’Sullivan, M., & Dunning, I. (2011). PuLP: a linear programming toolkit for Python. Auckland: The University of Auckland. Retrieved in 2019, November 27, from http://www.optimization-online.org/DB_FILE/2011/09/3178.pdf

Morandi, M. I. W. M., & Camargo, L. F. R. (2015). Revisão sistemática da literatura. In A. Dresch, D. P. Lacerda & J. A. V. Antunes Jr. (Eds.), (pp. 141-175). Design science research: método de pesquisa para avanço da ciência e tecnologia. Porto Alegre: The Bookman.

Pai, G. A. V. (2017). Fuzzy decision theory based metaheuristic portfolio optimization and active rebalancing using interval type-2 fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(2), 377-391. http://dx.doi.org/10.1109/TFUZZ.2016.2633972.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12(1), 2825-2830.

Pekár, J., Čičková, Z., & Brezina, I. (2016). Portfolio performance measurement using differential evolution. Central European Journal of Operations Research, 24(2), 421-433. http://dx.doi.org/10.1007/s10100-015-0393-8.

Pflug, G. C., Pichler, A., & Wozabal, D. (2012). The 1/N investment strategy is optimal under high model ambiguity. Journal of Banking & Finance, 36(2), 410-417. http://dx.doi.org/10.1016/j.jbankfin.2011.07.018.

Pflug, G., & Wozabal, D. (2007). Ambiguity in portfolio selection. Quantitative Finance, 7(4), 435-442. http://dx.doi.org/10.1080/14697680701455410.

Qu, B. Y., Zhou, Q., Xiao, J. M., Liang, J. J., & Suganthan, P. N. (2017). Large-scale portfolio optimization using multiobjective evolutionary algorithms and preselection methods. Mathematical Problems in Engineering, 2017, 1-14. http://dx.doi.org/10.1155/2017/4197914.

Ren, F., Lu, Y. N., Li, S. P., Jiang, X. F., Zhong, L. X., & Qiu, T. (2017). Dynamic portfolio strategy using clustering approach. PLoS One, 12(1), e0169299. http://dx.doi.org/10.1371/journal.pone.0169299. PMid:28129333.

Reveiz-Herault, A. (2016). An active asset management investment process for drawdown-averse investors. Intelligent Systems in Accounting, Finance & Management, 23(1-2), 85-96. http://dx.doi.org/10.1002/isaf.1375.

Rezaei Pouya, A., Solimanpur, M., & Jahangoshai Rezaee, M. (2016). Solving multi-objective portfolio optimization problem using invasive weed optimization. Swarm and Evolutionary Computation, 28, 42-57. http://dx.doi.org/10.1016/j.swevo.2016.01.001.

Rubio, A., Bermúdez, J. D., & Vercher, E. (2016). Forecasting portfolio returns using weighted fuzzy time series methods. International Journal of Approximate Reasoning, 75, 1-12. http://dx.doi.org/10.1016/j.ijar.2016.03.007.

Rubio, A., Bermúdez, J. D., & Vercher, E. (2017). Improving stock index forecasts by using a new weighted fuzzy-trend time series method. Expert Systems with Applications, 76, 12-20. http://dx.doi.org/10.1016/j.eswa.2017.01.049.

Saborido, R., Ruiz, A. B., Bermúdez, J. D., Vercher, E., & Luque, M. (2016). Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection. Applied Soft Computing, 39, 48-63. http://dx.doi.org/10.1016/j.asoc.2015.11.005.

Sharma, C., & Banerjee, K. (2015). A study of correlations in the stock market. Physica A, 432, 321-330. http://dx.doi.org/10.1016/j.physa.2015.03.061.

Silva, A., Neves, R., & Horta, N. (2015). A hybrid approach to portfolio composition based on fundamental and technical indicators. Expert Systems with Applications, 42(4), 2036-2048. http://dx.doi.org/10.1016/j.eswa.2014.09.050.

Sun, X., & Liu, Z. (2016). Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: evidence from the Chinese stock market. Physica A, 444, 667-679. http://dx.doi.org/10.1016/j.physa.2015.10.065.

Uryasev, S. (2000). Conditional value-at-risk: Optimization algorithms and applications. In Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520) (pp. 49-57). New York: IEEE. http://dx.doi.org/10.1109/CIFER.2000.844598.

Vercher, E., & Bermúdez, J. D. (2015). Portfolio optimization using a credibility mean-absolute semi-deviation model. Expert Systems with Applications, 42(20), 7121-7131. http://dx.doi.org/10.1016/j.eswa.2015.05.020.

Yu, D., Wang, W., Zhang, W., & Zhang, S. (2018). A bibliometric analysis of research on multiple criteria decision making. Current Science, 114(4), 747. http://dx.doi.org/10.18520/cs/v114/i04/747-758.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.

Zhang, W. G., & Liu, Y. J. (2014). Credibilitic mean-variance model for multi-period portfolio selection problem with risk control. OR-Spektrum, 36(1), 113-132. http://dx.doi.org/10.1007/s00291-013-0335-6.

Zhang, Y., Li, X., & Guo, S. (2018). Portfolio selection problems with Markowitz’s mean-variance framework: a review of literature. Fuzzy Optimization and Decision Making, 17(2), 1-34. http://dx.doi.org/10.1007/s10700-017-9266-z.

Zhao, L., Li, W., Fenu, A., Podobnik, B., Wang, Y., & Stanley, H. E. (2018a). The q-dependent detrended cross-correlation analysis of stock market. Journal of Statistical Mechanics, 2018(2), 1-28. http://dx.doi.org/10.1088/1742-5468/aa9db0.

Zhao, L., Wang, G. J., Wang, M., Bao, W., Li, W., & Stanley, H. E. (2018b). Stock market as temporal network. Physica A, 506, 1104-1112. http://dx.doi.org/10.1016/j.physa.2018.05.039.
 


Submitted date:
11/27/2019

Accepted date:
06/15/2020

5f23234b0e8825b122e56d7d production Articles
Links & Downloads

Production

Share this page
Page Sections