Dynamic changes and multi-dimensional evolution of portfolio optimization

References

• Aksaraylı, M., & Pala, O. (2018). A polynomial goal programming model for portfolio optimization based on entropy and higher moments. Expert Systems with Applications, 94, 185–192. https://doi.org/10.1016/j.eswa.2017.10.056
   Web of Science ®Google Scholar
• Anagnostopoulos, K. P., & Mamanis, G. (2011). The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multiobjective evolutionary algorithms. Expert Systems with Applications, 38(11), 14208–14217.
   Web of Science ®Google Scholar
• Artzner, P., Delbaen, F., Ebe, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228. https://doi.org/10.1111/1467-9965.00068
   Web of Science ®Google Scholar
• Bai, L., Liu, Y., Wang, Q., & Chen, C. (2019). Improving portfolio performance of renewable energy stocks using robust portfolio approach: Evidence from China. Physica A: Statistical Mechanics and Its Applications, 533, 122059. https://doi.org/10.1016/j.physa.2019.122059
   Web of Science ®Google Scholar
• Basak, S., & Chabakauri, G. (2012). Dynamic hedging in incomplete markets: A simple solution. Review of Financial Studies, 25(6), 1845–1896. https://doi.org/10.1093/rfs/hhs050
   Web of Science ®Google Scholar
• Baykasoğlu, A., Yunusoglu, M. G., & Özsoydan, F. B. (2015). A GRASP based solution approach to solve cardinality constrained portfolio optimization problems. Computers & Industrial Engineering, 90, 339–351. https://doi.org/10.1016/j.cie.2015.10.009
   Web of Science ®Google Scholar
• Benítez-Andrades, J. A., García-Rodríguez, I., Benavides, C., Alaiz-Moretón, H., & Rodríguez-González, A. (2020). Social network analysis for personalized characterization and risk assessment of alcohol use disorders in adolescents using semantic technologies. Future Generation Computer Systems, 106, 154–170. https://doi.org/10.1016/j.future.2020.01.002
   Web of Science ®Google Scholar
• Bertsimas, D., Brown, D. B., & Caramanis, C. (2011). Theory and applications of robust optimization. SIAM Review, 53(3), 464–501. https://doi.org/10.1137/080734510
   Web of Science ®Google Scholar
• Bhattacharyya, R., Kar, S., & Majumder, D. D. (2011). Fuzzy mean–variance–skewness portfolio selection models by interval analysis. Computers & Mathematics with Applications, 61(1), 126–137. https://doi.org/10.1016/j.camwa.2010.10.039
   Web of Science ®Google Scholar
• Bielecki, T. R., & Pliska, S. R. (1999). Risk-sensitive dynamic asset management. Applied Mathematics and Optimization, 39(3), 337–360. https://doi.org/10.1007/s002459900110
   Web of Science ®Google Scholar
• Björk, T., Murgoci, A., & Zhou, X. Y. (2014). Mean–variance portfolio optimization with state‐dependent risk aversion. Mathematical finance: An International Journal of Mathematics. Statistics and Financial Economics, 24(1), 1–24.
   Google Scholar
• Cai, X., Teo, K. L., Yang, X., & Zhou, X. Y. (2000). Portfolio optimization under a minimax rule. Management Science, 46(7), 957–972. https://doi.org/10.1287/mnsc.46.7.957.12039
   Web of Science ®Google Scholar
• Cariño, D. R., Kent, T., Myers, D. H., Stacy, C., Sylvanus, M., Turner, A. L., Watanabe, K., & Ziemba, W. T. (1994). The Russell-Yasuda Kasai model: An asset/liability model for a Japanese insurance company using multistage stochastic programming. Interfaces, 24(1), 29–49. https://doi.org/10.1287/inte.24.1.29
   Web of Science ®Google Scholar
• Cariño, D. R., & Ziemba, W. T. (1998). Formulation of the Russell-Yasuda Kasai financial planning model. Operations Research, 46(4), 433–449. https://doi.org/10.1287/opre.46.4.433
   Web of Science ®Google Scholar
• Chang, T. J., Meade, N., Beasley, J. E., & Sharaiha, Y. M. (2000). Heuristics for cardinality constrained portfolio optimization. Computers & Operations Research, 27(13), 1271–1302. https://doi.org/10.1016/S0305-0548(99)00074-X
   Web of Science ®Google Scholar
• Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317
   Web of Science ®Google Scholar
• Chen, J., Meng, S., & Zhou, W. (2019). The exploration of fuzzy linguistic research: A scientometric review based on CiteSpace. Journal of Intelligent & Fuzzy Systems, 37(3), 3655–3669. https://doi.org/10.3233/JIFS-182737
   Web of Science ®Google Scholar
• Chen, W., Wang, Y., Gupta, P., & Mehlawat, M. K. (2018). A novel hybrid heuristic algorithm for a new uncertain mean-variance-skewness portfolio selection model with real constraints. Applied Intelligence, 48(9), 2996–3018. https://doi.org/10.1007/s10489-017-1124-8
   Web of Science ®Google Scholar
• Chen, W., Wang, Y., Zhang, J., & Lu, S. (2017). Uncertain portfolio selection with high-order moments. Journal of Intelligent & Fuzzy Systems, 33(3), 1397–1411. https://doi.org/10.3233/JIFS-17369
   Web of Science ®Google Scholar
• Chen, C., & Zhou, Y. S. (2018). Robust multi-objective portfolio with higher moments. Expert Systems with Applications, 100, 165–181. https://doi.org/10.1016/j.eswa.2018.02.004
   Web of Science ®Google Scholar
• Cuoco, D. (1997). Optimal consumption and equilibrium prices with portfolio constraints and stochastic income. Journal of Economic Theory, 72(1), 33–73. https://doi.org/10.1006/jeth.1996.2207
   Web of Science ®Google Scholar
• Cvitanic, J., & Karatzas, I. (1992). Convex duality in constrained portfolio optimization. The Annals of Applied Probability, 2(4), 767–818. https://doi.org/10.1214/aoap/1177005576
   Google Scholar
• Dantzig, G. B., & Infanger, G. (1993). Multi-stage stochastic linear programs for portfolio optimization. Annals of Operations Research, 45(1), 59–76. https://doi.org/10.1007/BF02282041
   Google Scholar
• DeMiguel, V., Garlappi, L., Nogales, F. J., & Uppal, R. (2009a). A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms. Management Science, 55(5), 798–812. https://doi.org/10.1287/mnsc.1080.0986
   Web of Science ®Google Scholar
• DeMiguel, V., Garlappi, L., & Uppal, R. (2009b). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy. Review of Financial Studies, 22(5), 1915–1953. https://doi.org/10.1093/rfs/hhm075
   Web of Science ®Google Scholar
• Dentcheva, D., & Ruszczynski, A. (2003). Optimization with stochastic dominance constraints. SIAM Journal on Optimization, 14(2), 548–566. https://doi.org/10.1137/S1052623402420528
   Web of Science ®Google Scholar
• Djomba, J. K., & Zaletel-Kragelj, L. (2016). A methodological approach to the analysis of egocentric social networks in public health research: A practical example. Zdravstveno Varstvo, 55(4), 256–263. https://doi.org/10.1515/sjph-2016-0035
   PubMed Web of Science ®Google Scholar
• Dohleman, B. S. (2006). Exploratory social network analysis with Pajek. Psychometrika, 71(3), 605–606. https://doi.org/10.1007/s11336-005-1410-y
   Web of Science ®Google Scholar
• Ehrgott, M., Klamroth, K., & Schwehm, C. (2004). An MCDM approach to portfolio optimization. European Journal of Operational Research, 155(3), 752–770. https://doi.org/10.1016/S0377-2217(02)00881-0
   Web of Science ®Google Scholar
• El Karoui, N., & Jeanblanc-Picqué, M. (1998). Optimization of consumption with labor income. Finance and Stochastics, 2(4), 409–440. https://doi.org/10.1007/s007800050048
   Google Scholar
• El Karoui, N., & Quenez, M. C. (1995). Dynamic programming and pricing of contingent claims. SIAM Journal on Control and Optimization, 33(1), 29–66. https://doi.org/10.1137/S0363012992232579
   Web of Science ®Google Scholar
• Elton, E. J., Gruber, M. J., & Blake, C. R. (1996). The persistence of risk-adjusted mutual fund performance. The Journal of Business, 69(2), 133–157. https://doi.org/10.1086/209685
   Web of Science ®Google Scholar
• Fernández, A., & Gómez, S. (2007). Portfolio selection using neural networks. Computers & Operations Research, 34(4), 1177–1191. https://doi.org/10.1016/j.cor.2005.06.017
   Web of Science ®Google Scholar
• Gaigi, M. H., Vath, V. L., Mnif, M., & Toumi, S. (2016). Numerical approximation for a portfolio optimization problem under liquidity risk and costs. Applied Mathematics & Optimization, 74(1), 163–195. https://doi.org/10.1007/s00245-015-9311-7
   Web of Science ®Google Scholar
• Garlappi, L., Uppal, R., & Wang, T. (2007). Portfolio selection with parameter and model uncertainty: A multi-prior approach. Review of Financial Studies, 20(1), 41–81. https://doi.org/10.1093/rfs/hhl003
   Web of Science ®Google Scholar
• Goldfarb, D., & Iyengar, G. (2003). Robust portfolio selection problems. Mathematics of Operations Research, 28(1), 1–38. https://doi.org/10.1287/moor.28.1.1.14260
   Web of Science ®Google Scholar
• Golub, B., Holmer, M., McKendall, R., Pohlman, L., & Zenios, S. A. (1995). A stochastic programming model for money management. European Journal of Operational Research, 85(2), 282–296. https://doi.org/10.1016/0377-2217(94)00038-E
   Web of Science ®Google Scholar
• Gupta, P., Inuiguchi, M., Mehlawat, M. K., & Mittal, G. (2013). Multiobjective credibilistic portfolio selection model with fuzzy chance-constraints. Information Sciences, 229, 1–17. https://doi.org/10.1016/j.ins.2012.12.011
   Web of Science ®Google Scholar
• Hirschberger, M., Qi, Y., & Steuer, R. E. (2007). Randomly generating portfolio-selection covariance matrices with specified distributional characteristics. European Journal of Operational Research, 177(3), 1610–1625. https://doi.org/10.1016/j.ejor.2005.10.014
   Web of Science ®Google Scholar
• Huang, X. (2008). Mean-semivariance models for fuzzy portfolio selection. Journal of Computational and Applied Mathematics, 217(1), 1–8. https://doi.org/10.1016/j.cam.2007.06.009
   Web of Science ®Google Scholar
• Huang, X. (2011). Minimax mean-variance models for fuzzy portfolio selection. Soft Computing, 15(2), 251–260. https://doi.org/10.1007/s00500-010-0654-3
   Web of Science ®Google Scholar
• Hummon, N. P., & Dereian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63. https://doi.org/10.1016/0378-8733(89)90017-8
   Web of Science ®Google Scholar
• Inuiguchi, M., & Ramı́k, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), 3–28. https://doi.org/10.1016/S0165-0114(98)00449-7
   Web of Science ®Google Scholar
• Jorion, P. (2003). Portfolio optimization with tracking-error constraints. Financial Analysts Journal, 59(5), 70–82. https://doi.org/10.2469/faj.v59.n5.2565
   Web of Science ®Google Scholar
• Kalayci, C. B., Ertenlice, O., & Akbay, M. A. (2019). A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Systems with Applications, 125, 345–368. https://doi.org/10.1016/j.eswa.2019.02.011
   Web of Science ®Google Scholar
• Karatzas, I., & Shreve, S. E. (2000). Methods of mathematical finance. Journal of the American Statistical Association, 95(450), 685.
   Google Scholar
• Kenyon, C., Savage, S., & Ball, B. (1999). Equivalence of linear deviation about the mean and mean absolute deviation about the mean objective functions. Operations Research Letters, 24(4), 181–185. https://doi.org/10.1016/S0167-6377(99)00011-5
   Web of Science ®Google Scholar
• 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(2), 356–371. https://doi.org/10.1016/j.ejor.2013.10.060
   Google Scholar
• Konno, H. (1990). Piecewise linear risk function and portfolio optimization. Journal of the Operations Research Society of Japan, 33(2), 139–156. https://doi.org/10.15807/jorsj.33.139
   Google Scholar
• Konno, H., & Watanabe, H. (1996). Bond portfolio optimization problems and their applications to index tracking: A partial optimization approach. Journal of the Operations Research Society of Japan, 39(3), 295–306. https://doi.org/10.15807/jorsj.39.295
   Google Scholar
• Konno, H., & Yamazaki, H. (1991). Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. Management Science, 37(5), 519–623. https://doi.org/10.1287/mnsc.37.5.519
   Web of Science ®Google Scholar
• Kramkov, D., & Schachermayer, W. (1999). The asymptotic elasticity of utility functions and optimal investment in incomplete markets. Annals of Applied Probability, 9(3), 904–950.
   Web of Science ®Google Scholar
• Liagkouras, K., & Metaxiotis, K. (2015). Efficient portfolio construction with the use of multiobjective evolutionary algorithms: Best practices and performance metrics. International Journal of Information Technology & Decision Making, 14(03), 535–564. https://doi.org/10.1142/S0219622015300013
   Web of Science ®Google Scholar
• Lin, C. C., & Liu, Y. T. (2008). Genetic algorithms for portfolio selection problems with minimum transaction lots. European Journal of Operational Research, 185(1), 393–404. https://doi.org/10.1016/j.ejor.2006.12.024
   Web of Science ®Google Scholar
• Li, X., Qin, Z., & Kar, S. (2010). Mean-variance-skewness model for portfolio selection with fuzzy returns. European Journal of Operational Research, 202(1), 239–247. https://doi.org/10.1016/j.ejor.2009.05.003
   Web of Science ®Google Scholar
• Li, X., Shou, B., & Qin, Z. (2012). An expected regret minimization portfolio selection model. European Journal of Operational Research, 218(2), 484–492. https://doi.org/10.1016/j.ejor.2011.11.015
   Web of Science ®Google Scholar
• Liu, Y. J., & Zhang, W. G. (2015). A multi-period fuzzy portfolio optimization model with minimum transaction lots. European Journal of Operational Research, 242(3), 933–941. https://doi.org/10.1016/j.ejor.2014.10.061
   Google Scholar
• Liu, Y. J., Zhang, W. G., & Xu, W. J. (2012). Fuzzy multi-period portfolio selection optimization models using multiple criteria. Automatica, 48(12), 3042–3053. https://doi.org/10.1016/j.automatica.2012.08.036
   Web of Science ®Google Scholar
• Liu, Y. J., Zhang, W. G., & Zhang, P. (2013). A multi-period portfolio selection optimization model by using interval analysis. Economic Modelling, 33, 113–119. https://doi.org/10.1016/j.econmod.2013.03.006
   Web of Science ®Google Scholar
• Lu, L. Y., & Liu, J. S. (2013). An innovative approach to identify the knowledge diffusion path: The case of resource-based theory. Scientometrics, 94(1), 225–246. https://doi.org/10.1007/s11192-012-0744-3
   Web of Science ®Google Scholar
• Mansini, R., Ogryczak, W., & Speranza, M. G. (2003). On LP solvable models for portfolio selection. Informatica, 14(1), 37–62. https://doi.org/10.15388/Informatica.2003.003
   Web of Science ®Google Scholar
• Mansini, R., & Speranza, M. G. (2005). An exact approach for portfolio selection with transaction costs and rounds. IIE Transactions, 37(10), 919–929. https://doi.org/10.1080/07408170591007821
   Web of Science ®Google Scholar
• Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91.
   Web of Science ®Google Scholar
• Mehlawat, M. K. (2016). Credibilistic mean-entropy models for multi-period portfolio selection with multi-choice aspiration levels. Information Sciences, 345, 9–26. https://doi.org/10.1016/j.ins.2016.01.042
   Web of Science ®Google Scholar
• Mehlawat, M. K., & Gupta, P. (2014). Credibility-based fuzzy mathematical programming model for portfolio selection under uncertainty. International Journal of Information Technology & Decision Making, 13(01), 101–135. https://doi.org/10.1142/S0219622014500059
   Web of Science ®Google Scholar
• Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based model for optimizing bank lending decisions. Expert Systems with Applications, 80, 75–82. https://doi.org/10.1016/j.eswa.2017.03.021
   Web of Science ®Google Scholar
• Mulvey, J. M., & Vladimirou, H. (1992). Stochastic network programming for financial planning problems. Management Science, 38(11), 1642–1685. https://doi.org/10.1287/mnsc.38.11.1642
   Web of Science ®Google Scholar
• Ogryczak, W., & Ruszczyński, A. (1999). From stochastic dominance to mean-risk models: Semideviations as risk measures. European Journal of Operational Research, 116(1), 33–50. https://doi.org/10.1016/S0377-2217(98)00167-2
   Web of Science ®Google Scholar
• Paiva, F. D., Cardoso, R. T. N., Hanaoka, G. P., & Duarte, W. M. (2019). Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Applications, 115, 635–655. https://doi.org/10.1016/j.eswa.2018.08.003
   Web of Science ®Google Scholar
• Peng, Y., Albuquerque, P. H. M., do Nascimento, I. F., & Machado, J. V. F. (2019). Between nonlinearities, complexity, and noises: An application on portfolio selection using kernel principal component analysis. Entropy, 21(4), 376. https://doi.org/10.3390/e21040376
   PubMed Web of Science ®Google Scholar
• Perold, A. F. (1984). Large-scale portfolio optimization. Management Science, 30(10), 1143–1160. https://doi.org/10.1287/mnsc.30.10.1143
   Web of Science ®Google Scholar
• Pikovsky, I., & Karatzas, I. (1996). Anticipative portfolio optimization. Advances in Applied Probability, 28(4), 1095–1122. https://doi.org/10.2307/1428166
   Web of Science ®Google Scholar
• Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of Banking & Finance, 26(7), 1443–1471. https://doi.org/10.1016/S0378-4266(02)00271-6
   Web of Science ®Google Scholar
• Rockafellar, R. T., Uryasev, S., & Zabarankin, M. (2007). Equilibrium with investors using a diversity of deviation measures. Journal of Banking & Finance, 31(11), 3251–3268. https://doi.org/10.1016/j.jbankfin.2007.04.002
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1016/j.asoc.2015.11.005
   Web of Science ®Google Scholar
• Sharpe, W. F. (1963). A simplified model for portfolio analysis. Management Science, 9(2), 277–349. https://doi.org/10.1287/mnsc.9.2.277
   Web of Science ®Google Scholar
• Silva, Y. L. T., 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. https://doi.org/10.1016/j.eswa.2019.05.018
   Web of Science ®Google Scholar
• Speranza, M. G. (1993). Linear programming models for portfolio optimization. Finance, 14, 107–123.
   Google Scholar
• Tu, J., & Zhou, G. (2011). Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies. Journal of Financial Economics, 99(1), 204–215. https://doi.org/10.1016/j.jfineco.2010.08.013
   Web of Science ®Google Scholar
• Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
   PubMed Web of Science ®Google Scholar
• Wang, X. X., Chang, Y. R., Xu, Z. S., Wang, Z. D., & Kadirkamanathan, V. (2021a). 50 Years of international journal of systems science: A review of the past and trends for the future. International Journal of Systems Science, 52(8), 1515–1538. https://doi.org/10.1080/00207721.2020.1862937
   Web of Science ®Google Scholar
• Wang, X. X., Xu, Z. S., & Skare, M. (2020). A bibliometric analysis of Economic Research-Ekonomska istraživanja (2007-2019). Economic Research-Ekonomska Istraživanja, 33(1), 865–886. https://doi.org/10.1080/1331677X.2020.1737558
   Web of Science ®Google Scholar
• Wang, X. X., Xu, Z. S., Su, S.-F., & Zhou, W. (2021b). A comprehensive bibliometric analysis of uncertain group decision making from 1980 to 2019. Information Sciences, 547, 328–353. https://doi.org/10.1016/j.ins.2020.08.036
   Web of Science ®Google Scholar
• Woodside-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. https://doi.org/10.1016/j.ejor.2011.03.030
   Web of Science ®Google Scholar
• Worzel, K. J., Vassiadou-Zeniou, C., & Zenios, S. A. (1994). Integrated simulation and optimization models for tracking indices of fixed-income securities. Operations Research, 42(2), 223–233. https://doi.org/10.1287/opre.42.2.223
   Web of Science ®Google Scholar
• Xu, Z., Zhou, W., & Baltrėnaitė, E. (2019). Comprehensive bibliometric study of journal of environmental engineering and landscape management from 2007 to 2019. Journal of Environmental Engineering and Landscape Management, 27(4), 215–227. https://doi.org/10.3846/jeelm.2019.11366
   Web of Science ®Google Scholar
• Young, M. R. (1998). A minimax portfolio selection rule with linear programming solution. Management Science, 44(5), 673–741. https://doi.org/10.1287/mnsc.44.5.673
   Web of Science ®Google Scholar
• Yue, W., & Wang, Y. (2017). A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios. Physica A: Statistical Mechanics and Its Applications, 465, 124–140. https://doi.org/10.1016/j.physa.2016.08.009
   Web of Science ®Google Scholar
• Yu, J. R., & Lee, W. Y. (2011). Portfolio rebalancing model using multiple criteria. European Journal of Operational Research, 209(2), 166–175. https://doi.org/10.1016/j.ejor.2010.09.018
   Web of Science ®Google Scholar
• Yu, D. J., & Liao, H. C. (2016). Visualization and quantitative research on intuitionistic fuzzy studies. Journal of Intelligent & Fuzzy Systems, 30(6), 3653–3663. https://doi.org/10.3233/IFS-162111
   Web of Science ®Google Scholar
• Yu, D. J., Li, D. F., Merigó, J. M., & Fang, L. C. (2016). Mapping development of linguistic decision making studies. Journal of Intelligent & Fuzzy Systems, 30(5), 2727–2736. https://doi.org/10.3233/IFS-152026
   Web of Science ®Google Scholar
• Zenios, S. A. (1995). Asset/liability management under uncertainty for fixed-income securities. Annals of Operations Research, 59(1), 77–97. https://doi.org/10.1007/BF02031744
   Web of Science ®Google Scholar
• Zenios, S. A., & Kang, P. (1993). Mean-absolute deviation portfolio optimization for mortgage-backed securities. Annals of Operations Research, 45(1), 433–450. https://doi.org/10.1007/BF02282062
   Google Scholar
• Zhang, W. G., Liu, Y. J., & Xu, W. J. (2012). A possibilistic mean-semivariance-entropy model for multi-period portfolio selection with transaction costs. European Journal of Operational Research, 222(2), 341–349. https://doi.org/10.1016/j.ejor.2012.04.023
   Web of Science ®Google Scholar
• Zhang, P., & Zhang, W. G. (2014). Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints. Fuzzy Sets and Systems, 255, 74–91. https://doi.org/10.1016/j.fss.2014.07.018
   Web of Science ®Google Scholar
• Zhou, W., Luo, D., Fang, H., Gou, X., & Chen, J. (2020). Bibliometric overview and retrospective analysis of fund performance research between 1966 and 2019. Economic research-Ekonomska Istraživanja, 33(1), 1510–1537. https://doi.org/10.1080/1331677X.2020.1755879
   Web of Science ®Google Scholar
• Zhou, W., Xu, Z., & Zavadskas, E. K. (2019). A bibliometric overview of the International Journal of Strategic Property Management between 2008 and 2019. International Journal of Strategic Property Management, 23(6), 366–377. https://doi.org/10.3846/ijspm.2019.10535
   Web of Science ®Google Scholar
• Zhu, S., & Fukushima, M. (2009). Worst-case conditional value-at-risk with application to robust portfolio management. Operations Research, 57(5), 1155–1168. https://doi.org/10.1287/opre.1080.0684
   Web of Science ®Google Scholar