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May  2020, 16(3): 1171-1185. doi: 10.3934/jimo.2018198

A smoothing SAA algorithm for a portfolio choice model based on second-order stochastic dominance measures

1. 

School of Mathematics and Computational Sciences, Xiangtan University, Xiangtan 411105, Hunan, China

2. 

Hunan First Normal University, Changsha 410215, Hunan, China

* Corresponding author: Liu Yang

Received  March 2017 Revised  October 2017 Published  December 2018

In this paper, we provide a smoothing sample average approximation (SAA) method to solve a portfolio choice model based on second-order stochastic dominance (SSD) measure. Introducing a second-order stochastic dominance constraint in portfolio choice is theoretically attractive since all risk-averse investors would prefer a dominating portfolio. However, how to get the best choice among SSD efficient portfolios which is based on a stochastic optimization model is a challenge. We use the sample average to approximate the expected return rate function in the model and get a linear/nonlinear programming when the benchmark has discrete distribution. Then we propose a smoothing penalty algorithm to solve this problem. Meanwhile, we investigate the convergence of the optimal value of the transformed model and show that the optimal value converges to its counterpart with probability approaching to one at exponential rate as the sample size increases. By comparing the numerical results of the smoothing SAA algorithm with the common linear programming (LP) algorithm, we find that the smoothing algorithm has better performance than the LP algorithm in three aspects: (ⅰ)the smoothing SAA method can avoid the infinite constraints in the transformed models and the size of the smoothing algorithm model will not increase as the sample grows; (ⅱ)the smoothing SAA algorithm can deal with the nonlinear portfolio models with nonlinear transaction cost function; (ⅲ) the smoothing algorithm can get the global optimal solution because the smoothing function maintains the original convexity.

Citation: Liu Yang, Xiaojiao Tong, Yao Xiong, Feifei Shen. A smoothing SAA algorithm for a portfolio choice model based on second-order stochastic dominance measures. Journal of Industrial & Management Optimization, 2020, 16 (3) : 1171-1185. doi: 10.3934/jimo.2018198
References:
[1]

M. J. AkianL. Menaldi and A. Sulem, Multi-asset porfolio selection problem with transaction cosats, Mathematics and Computers in Simulation, 38 (1995), 163-172.  doi: 10.1016/0378-4754(93)E0079-K.  Google Scholar

[2]

J. AngF. Meng and J. Sun, Two-stage stochastic linear programs with incomplete information on uncertainty, European Journal of Operational Research, 233 (2014), 16-22.  doi: 10.1016/j.ejor.2013.07.039.  Google Scholar

[3]

R. BruniF. CesaroneA. Scozzari and F. Tardella, On exact and approximate stochastic dominance strategies for portfolio selection, European Journal of Operational Research, 259 (2017), 322-329.  doi: 10.1016/j.ejor.2016.10.006.  Google Scholar

[4]

D. Dentcheva and A. Ruszczyński, Optimization with stochastic dominance constraints, SIAM Journal on Optimization, 14 (2003), 548-566.  doi: 10.1137/S1052623402420528.  Google Scholar

[5]

D. Dentcheva and A. Ruszczyński, Optimality and duality theory for stochastic optimization problems with nonlinear dominance constraints, Mathematical Programming, 99 (2004), 329-350.  doi: 10.1007/s10107-003-0453-z.  Google Scholar

[6]

D. Dentcheva and A. Ruszczyński, Portfolio optimization with stochastic dominance constraints, Journal of Banking and Finance, 30 (2006), 433-451.   Google Scholar

[7]

J. Dupa$\check{c}$ov$\acute{a}$ and M. Kopa, Robustness of optimal portfolios under risk and stochastic dominance constraints, E.J.Oper. Res., 234 (2014), 434-441.  doi: 10.1016/j.ejor.2013.06.018.  Google Scholar

[8]

L. F. EscuderoJ. F. Monge and D. R. Morales, An SDP approach for multiperiod mixed 0-1 linear programming models with stochastic dominance constraints for risk management, Comp. Oper. Res., 58 (2015), 32-40.  doi: 10.1016/j.cor.2014.12.007.  Google Scholar

[9]

C. I. F$\acute{a}$bi$\acute{a}$nG. MitraD. Roman and V. Zverovich, An enhanced model for portfolio choice with SSD criteria: A constructive approach, Quantitative Finance, 11 (2011), 1525-1534.  doi: 10.1080/14697680903493607.  Google Scholar

[10]

P. C. Fishburn, Decision and Value Theory, John Wiley and Sons, New York, 1964. Google Scholar

[11]

T. Homem-De-Mello and S. Mehrota, A cutting surface method for uncertain linear programs with polyhedral stochastic dominance constraints, SIAM Journal of Optimization, 20 (2009), 1250-1273.  doi: 10.1137/08074009X.  Google Scholar

[12]

J. E. HodderJ. C. Jackwerth and O. Kolokolova, Improved portfolio choice using second-order stochastic dominance, Review of Finance, 19 (2015), 1623-1647.   Google Scholar

[13]

J. HuT. Homem-De-Mello and S. Mehrota, Sample average approximation of stochastic dominance constrained programs, Mathematical Programming, Series A, 133 (2012), 171-201.  doi: 10.1007/s10107-010-0428-9.  Google Scholar

[14]

C. JiangQ. LinC. J. YuK. L. Teo and G. R. Duan, An exact penalty method for free terminal time optimal control problem with continuous inequality constraints, Journal of Optimization Theory and Applications, 154 (2012), 30-53.  doi: 10.1007/s10957-012-0006-9.  Google Scholar

[15]

B. LiC. Z. WuH. H. DamA. Cantoni and K. L. Teo, A parallel low complexity zero-forcing beamformer design for multiuser MIMO systems via a regularized dual decomposition method, IEEE Transactions on Signal Processing, 63 (2015), 4179-4190.  doi: 10.1109/TSP.2015.2437846.  Google Scholar

[16]

B. LiC. J. YuK. L. Teo and G. R. Duan, An exact penalty function method for continuous inequality constrained optimal control problem, Journal of Optimization Theory and Applications, 151 (2011), 260-291.  doi: 10.1007/s10957-011-9904-5.  Google Scholar

[17]

B. LiY. RongJ. Sun and K. L. Teo, A distributionally robust linear receiver design for multi-access space-time block coded MIMO systems, IEEE Transactions on Wireless Communications, 16 (2017), 464-474.   Google Scholar

[18]

Q. LinR. Loxton and K. L. Teo, The control parameterization method for nonlinear optimal control: A survey, Journal of Industrial and Management Optimization, 10 (2014), 275-309.  doi: 10.3934/jimo.2014.10.275.  Google Scholar

[19]

Y. Liu and H. Xu, Stability analysis of stochastic programs with second order dominance constraints, Mathematical Programming, 142 (2013), 435-460.  doi: 10.1007/s10107-012-0585-0.  Google Scholar

[20]

A. W. Marshall and I. Olkin, Inequalities: Theory of Majorization and Its Applications, Academic Press, San Diego, 1979.  Google Scholar

[21]

M. Menegatti, A note on portfolio selection and stochastic dominance, Decisions Econ. Finan., 39 (2016), 327-331.  doi: 10.1007/s10203-016-0179-z.  Google Scholar

[22]

R. MeskarianH. Xu and J. Fliege, Numerical methods for stochastic programs with second order dominance constraints with applications to portfolio optimization, European Journal of Operational Research, 216 (2012), 376-385.  doi: 10.1016/j.ejor.2011.07.044.  Google Scholar

[23]

R. MeskarianJ. Fliege and H. Xu, Stochstic programming with multivariate second order stochastic dominance constraints with applications in portfolio optimization, Appl. Math. Optim., 70 (2014), 111-140.  doi: 10.1007/s00245-014-9236-6.  Google Scholar

[24]

J. M. Peng and Z. Lin, A non-interior continuation method for generalized linear complementarity problems, Math.Program, 86 (1999), 533-563.  doi: 10.1007/s101070050104.  Google Scholar

[25]

J. P. Quirk and R. Saposnik, Admissibility and measurable utility functions, Review of Economic Studies, 29 (1962), 140-146.   Google Scholar

[26]

A. Shapiro, Monte Carlo sampling Methods, in:Stochastic Programming, Handbook in Operations Research and Management Science, 10 (2003), 353-425.  doi: 10.1016/S0927-0507(03)10006-0.  Google Scholar

[27]

H. Sun, h. Xu and Y. Wang, A smoothing penalized sample average approximation method for stochastic programs with second-order stochastic dominance constraints, Asia-Pacific Journal of Operational Research, 30 (2013), 1340002, 25 pp. doi: 10.1142/S0217595913400022.  Google Scholar

[28]

H. Sun and H. Xu, Convergence analysis of stationary points in sample average approximation of stochastic programs with second order stochastic dominance constraints, Math. Program., Ser. A, 143 (2014), 31-59.  doi: 10.1007/s10107-013-0711-7.  Google Scholar

[29]

X. J. Tong, L. Qi, F. Wu, et al., A smoothing method for solving portfolio optimization with CVaR and applications in allocation of generation asset, Applied Mathematics and Computation, 216 (2010), 1723–1740. doi: 10.1016/j.amc.2009.12.031.  Google Scholar

[30]

L. YangY. Chen and X. Tong, Smoothing Newton-like method for the solution of nonlinear systems of equalities and inequalities, Numerical Mathematics: Theory, Methods and Applications, 2 (2009), 224-236.   Google Scholar

show all references

References:
[1]

M. J. AkianL. Menaldi and A. Sulem, Multi-asset porfolio selection problem with transaction cosats, Mathematics and Computers in Simulation, 38 (1995), 163-172.  doi: 10.1016/0378-4754(93)E0079-K.  Google Scholar

[2]

J. AngF. Meng and J. Sun, Two-stage stochastic linear programs with incomplete information on uncertainty, European Journal of Operational Research, 233 (2014), 16-22.  doi: 10.1016/j.ejor.2013.07.039.  Google Scholar

[3]

R. BruniF. CesaroneA. Scozzari and F. Tardella, On exact and approximate stochastic dominance strategies for portfolio selection, European Journal of Operational Research, 259 (2017), 322-329.  doi: 10.1016/j.ejor.2016.10.006.  Google Scholar

[4]

D. Dentcheva and A. Ruszczyński, Optimization with stochastic dominance constraints, SIAM Journal on Optimization, 14 (2003), 548-566.  doi: 10.1137/S1052623402420528.  Google Scholar

[5]

D. Dentcheva and A. Ruszczyński, Optimality and duality theory for stochastic optimization problems with nonlinear dominance constraints, Mathematical Programming, 99 (2004), 329-350.  doi: 10.1007/s10107-003-0453-z.  Google Scholar

[6]

D. Dentcheva and A. Ruszczyński, Portfolio optimization with stochastic dominance constraints, Journal of Banking and Finance, 30 (2006), 433-451.   Google Scholar

[7]

J. Dupa$\check{c}$ov$\acute{a}$ and M. Kopa, Robustness of optimal portfolios under risk and stochastic dominance constraints, E.J.Oper. Res., 234 (2014), 434-441.  doi: 10.1016/j.ejor.2013.06.018.  Google Scholar

[8]

L. F. EscuderoJ. F. Monge and D. R. Morales, An SDP approach for multiperiod mixed 0-1 linear programming models with stochastic dominance constraints for risk management, Comp. Oper. Res., 58 (2015), 32-40.  doi: 10.1016/j.cor.2014.12.007.  Google Scholar

[9]

C. I. F$\acute{a}$bi$\acute{a}$nG. MitraD. Roman and V. Zverovich, An enhanced model for portfolio choice with SSD criteria: A constructive approach, Quantitative Finance, 11 (2011), 1525-1534.  doi: 10.1080/14697680903493607.  Google Scholar

[10]

P. C. Fishburn, Decision and Value Theory, John Wiley and Sons, New York, 1964. Google Scholar

[11]

T. Homem-De-Mello and S. Mehrota, A cutting surface method for uncertain linear programs with polyhedral stochastic dominance constraints, SIAM Journal of Optimization, 20 (2009), 1250-1273.  doi: 10.1137/08074009X.  Google Scholar

[12]

J. E. HodderJ. C. Jackwerth and O. Kolokolova, Improved portfolio choice using second-order stochastic dominance, Review of Finance, 19 (2015), 1623-1647.   Google Scholar

[13]

J. HuT. Homem-De-Mello and S. Mehrota, Sample average approximation of stochastic dominance constrained programs, Mathematical Programming, Series A, 133 (2012), 171-201.  doi: 10.1007/s10107-010-0428-9.  Google Scholar

[14]

C. JiangQ. LinC. J. YuK. L. Teo and G. R. Duan, An exact penalty method for free terminal time optimal control problem with continuous inequality constraints, Journal of Optimization Theory and Applications, 154 (2012), 30-53.  doi: 10.1007/s10957-012-0006-9.  Google Scholar

[15]

B. LiC. Z. WuH. H. DamA. Cantoni and K. L. Teo, A parallel low complexity zero-forcing beamformer design for multiuser MIMO systems via a regularized dual decomposition method, IEEE Transactions on Signal Processing, 63 (2015), 4179-4190.  doi: 10.1109/TSP.2015.2437846.  Google Scholar

[16]

B. LiC. J. YuK. L. Teo and G. R. Duan, An exact penalty function method for continuous inequality constrained optimal control problem, Journal of Optimization Theory and Applications, 151 (2011), 260-291.  doi: 10.1007/s10957-011-9904-5.  Google Scholar

[17]

B. LiY. RongJ. Sun and K. L. Teo, A distributionally robust linear receiver design for multi-access space-time block coded MIMO systems, IEEE Transactions on Wireless Communications, 16 (2017), 464-474.   Google Scholar

[18]

Q. LinR. Loxton and K. L. Teo, The control parameterization method for nonlinear optimal control: A survey, Journal of Industrial and Management Optimization, 10 (2014), 275-309.  doi: 10.3934/jimo.2014.10.275.  Google Scholar

[19]

Y. Liu and H. Xu, Stability analysis of stochastic programs with second order dominance constraints, Mathematical Programming, 142 (2013), 435-460.  doi: 10.1007/s10107-012-0585-0.  Google Scholar

[20]

A. W. Marshall and I. Olkin, Inequalities: Theory of Majorization and Its Applications, Academic Press, San Diego, 1979.  Google Scholar

[21]

M. Menegatti, A note on portfolio selection and stochastic dominance, Decisions Econ. Finan., 39 (2016), 327-331.  doi: 10.1007/s10203-016-0179-z.  Google Scholar

[22]

R. MeskarianH. Xu and J. Fliege, Numerical methods for stochastic programs with second order dominance constraints with applications to portfolio optimization, European Journal of Operational Research, 216 (2012), 376-385.  doi: 10.1016/j.ejor.2011.07.044.  Google Scholar

[23]

R. MeskarianJ. Fliege and H. Xu, Stochstic programming with multivariate second order stochastic dominance constraints with applications in portfolio optimization, Appl. Math. Optim., 70 (2014), 111-140.  doi: 10.1007/s00245-014-9236-6.  Google Scholar

[24]

J. M. Peng and Z. Lin, A non-interior continuation method for generalized linear complementarity problems, Math.Program, 86 (1999), 533-563.  doi: 10.1007/s101070050104.  Google Scholar

[25]

J. P. Quirk and R. Saposnik, Admissibility and measurable utility functions, Review of Economic Studies, 29 (1962), 140-146.   Google Scholar

[26]

A. Shapiro, Monte Carlo sampling Methods, in:Stochastic Programming, Handbook in Operations Research and Management Science, 10 (2003), 353-425.  doi: 10.1016/S0927-0507(03)10006-0.  Google Scholar

[27]

H. Sun, h. Xu and Y. Wang, A smoothing penalized sample average approximation method for stochastic programs with second-order stochastic dominance constraints, Asia-Pacific Journal of Operational Research, 30 (2013), 1340002, 25 pp. doi: 10.1142/S0217595913400022.  Google Scholar

[28]

H. Sun and H. Xu, Convergence analysis of stationary points in sample average approximation of stochastic programs with second order stochastic dominance constraints, Math. Program., Ser. A, 143 (2014), 31-59.  doi: 10.1007/s10107-013-0711-7.  Google Scholar

[29]

X. J. Tong, L. Qi, F. Wu, et al., A smoothing method for solving portfolio optimization with CVaR and applications in allocation of generation asset, Applied Mathematics and Computation, 216 (2010), 1723–1740. doi: 10.1016/j.amc.2009.12.031.  Google Scholar

[30]

L. YangY. Chen and X. Tong, Smoothing Newton-like method for the solution of nonlinear systems of equalities and inequalities, Numerical Mathematics: Theory, Methods and Applications, 2 (2009), 224-236.   Google Scholar

Figure 1.  The expected returns for different smoothing parameters
Figure 2.  Comparison of the CPU time for LP and SMOOTH
Figure 3.  The expected returns for different transaction cost ratio
Figure 4.  The expected returns for different penalty parameter and sample size
Table 1.  Expectation and variance of return rates
code of stock 600690 000713 600115 600111 600000
expectation 0.0051 0.0040 0.0030 0.0100 0.0049
variance 0.0031 0.0042 0.0050 0.0060 0.0036
code of stock 600252 600011 600362 000401 600267
expectation 0.0072 0.0028 0.0073 0.0052 0.0050
variance 0.0061 0.0027 0.0065 0.0046 0.0030
code of stock 600690 000713 600115 600111 600000
expectation 0.0051 0.0040 0.0030 0.0100 0.0049
variance 0.0031 0.0042 0.0050 0.0060 0.0036
code of stock 600252 600011 600362 000401 600267
expectation 0.0072 0.0028 0.0073 0.0052 0.0050
variance 0.0061 0.0027 0.0065 0.0046 0.0030
Table 2.  Comparison of the numerical results for LP and SMOOTH
Method Problem $x$ E$[\cdot]$
LP No-cost (0, 0, 0, 0.7744, 0, 0.2165, 0, 0.0091, 0, 0) 0.0095
SMOOTH No-cost (0, 0, 0, 0.9592, 0, 0.0348, 0, 0.006, 0, 0) 0.0089
LP cost (0, 0, 0, 0.6620, 0, 0.2554, 0, 0.0642, 0, 0) 0.0075
SMOOTH cost (0, 0.0586, 0.0809, 0.1435, 0, 0, 0.4344, 0, 0, 0.2608) 0.0067
Method Problem $x$ E$[\cdot]$
LP No-cost (0, 0, 0, 0.7744, 0, 0.2165, 0, 0.0091, 0, 0) 0.0095
SMOOTH No-cost (0, 0, 0, 0.9592, 0, 0.0348, 0, 0.006, 0, 0) 0.0089
LP cost (0, 0, 0, 0.6620, 0, 0.2554, 0, 0.0642, 0, 0) 0.0075
SMOOTH cost (0, 0.0586, 0.0809, 0.1435, 0, 0, 0.4344, 0, 0, 0.2608) 0.0067
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