|Table of Contents|

Mean-Semivariance Portfolio Selection with Distortion Based on a Data Driven Approach(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年02期
Page:
7-14
Research Field:
数学
Publishing date:

Info

Title:
Mean-Semivariance Portfolio Selection with Distortion Based on a Data Driven Approach
Author(s):
Yang Dongfang; Li Mengyu; Wang Tianfang; Mi Hui; Liu Guoxiang
(School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China)
Keywords:
mean-semivariance with probability distortion;  portfolio optimization;  ICA-GA hybrid algorithm;  data driven approach
PACS:
O211; F830
DOI:
10.3969/j.issn.1001-4616.2023.02.002
Abstract:
This paper studies a mean-semivariance portfolio optimization problem with probability distortion by using the data driven sample average approximation(SAA)approach. The paper builds ICA-GA hybrid algorithm based on the traditional imperial competitive algorithm(ICA)and genetic algorithm(GA). Employing the true market data, the model is empirically analyzed and the effective frontier is solved. Finally, by comparing the running time of the computer programs, it shows that the ICA-GA hybrid algorithm in this paper combines the advantages of imperial competitive algorithm and genetic algorithm, and it performs better than them.

References:

[1]MARKOWITZ H. Portfolio selection[J]. Journal of finance,1952,7(1):77-91.
[2]ZHOU X Y,LI D. Continuous-time mean-variance portfolio selection:a stochastic LQ framework[J]. Applied mathematics & optimization,2000,42:19-33.
[3]LIOUI A,PONCET P. Understanding dynamic mean variance asset allocation[J]. European journal of operational research,2016,254(1):320-337.
[4]RAY P,JENAMANI M. Mean-variance analysis of sourcing decision under disruption risk[J]. European journal of operational research,2016,250(2):679-689.
[5]MARKOWITZ H,TODD P,XU G,et al. Computation of mean-semivariance efficient sets by the critical line algorithm[J]. Annals of operations research,1993,45(1):307-317.
[6]BALLESTERO E. Mean-semivariance efficient frontier:a downside risk model for portfolio selection[J]. Applied mathematical finance,2005,12(1):1-15.
[7]ESTRADA J. Mean-semivariance behavior:downside risk and capital asset pricing. [J]. International review of economics & finance,2007,16(2):169-185.
[8]QUIGGIN J. A theory of anticipated utility[J]. Journal of economic behavior and organization,1982,3(4):323-343.
[9]JIN H,ZHOU X Y. Behavioral portfolio selection in continuous time[J]. Mathematical finance,2008,18(3):385-426.
[10]HE X D,ZHOU X Y. Portfolio choice via quantiles[J]. Mathematical finance,2011,21(2):203-231.
[11]BI J,JIN H,MENG Q. Behavioral mean-variance portfolio selection[J]. European journal of operational research,2018,271(2):644-663.
[12]BI J,ZHONG Y,ZHOU X Y. Mean-semivariance portfolio selection under probability distortion[J]. Stochastics,2013,85(4):604-619.
[13]KANG Z,LI X,LI Z,et al. Data-driven robust mean-CVaR portfolio selection under distribution ambiguity[J]. Quantitative finance,2019,19(1):105-121.
[14]BAN G Y,EL KAROUI N,LIM A E. Machine learning and portfolio optimization[J]. Management science,2016,64(3):1136-1154.
[15]BERTSIMAS D,GUPTA V,KALLUS N. Robust sample average approximation[J]. Mathematical programming,2018,171(1/2):217-282.
[16]DELAGE E,YE Y. Distributionally robust optimization under moment uncertainty with application to data-driven problems[J]. Operations research,2010,58(3):595-612.
[17]CETINKAYA E,THIELE A. Data-driven portfolio management with quantile constraints[J]. OR spectrum,2015,37(3):761-786.
[18]PRELEC D. The probability weighting function[J]. Econometrica,1998,66(3):497-527.
[19]SHAPIRO A,DENTCHEVA D,RUSZCZYNSKI A. Lectures on Stochastic Programming:modeling and theory[M]. Cambridge:Cambridge University Press,2009.
[20]KLEYWEGT A J,SHAPIRO A,HOMEM-DE-MELLO T. The sample average approximation method for stochastic discrete optimization[J]. SIAM journal on optimization,2001,12(2):479-502.
[21]MEHDINEJAD M,MOHAMMADI-IVATLOO B,DADASHZADEH-BONAB R,et al. Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms[J]. International journal of electrical power and energy systems,2016,83:104-116.
[22]HOSSEINI S,AL KHALED A. A survey on the imperialist competitive algorithm metaheuristic:implementation in engineering domain and directions for future research[J]. Applied soft computing,2014,24:1078-1094.
[23]SADEGHI J,MOUSAVI S M,NIAKI S T A. Optimizing an inventory model with fuzzy demand,backordering,and discount using a hybrid imperialist competitive algorithm[J]. Applied mathematical modelling,2016,40(15/16):7318-7335.

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Last Update: 2023-06-15